U.S. patent application number 14/138616 was filed with the patent office on 2014-11-27 for dynamic characterization of particles with flow cytometry.
This patent application is currently assigned to University of Washington. The applicant listed for this patent is University of Washington. Invention is credited to Thomas J. Matula, Jarred Swalwell.
Application Number | 20140347669 14/138616 |
Document ID | / |
Family ID | 41430898 |
Filed Date | 2014-11-27 |
United States Patent
Application |
20140347669 |
Kind Code |
A1 |
Matula; Thomas J. ; et
al. |
November 27, 2014 |
Dynamic Characterization of Particles With Flow Cytometry
Abstract
Flow cytometry concepts are modified to enable dynamic
characterizations of particles to be obtained using optical
scattering data. Particles in flow will be introduced into a sample
volume. Light scattered by a particle in the sample volume is
collected and analyzed. What differentiates the concepts disclosed
herein from conventional flow cytometry is the use of an acoustic
source that is disposed to direct acoustic energy into the sample
volume. As the particle passes through the sample volume, it
responds to the acoustic energy, causing changes in the light
scattered by the particle. Those changes, which are not measured
during conventional flow cytometry, can be analyzed to determine
additional physical properties of the particle.
Inventors: |
Matula; Thomas J.;
(Kirkland, WA) ; Swalwell; Jarred; (Shoreline,
WA) |
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Applicant: |
Name |
City |
State |
Country |
Type |
University of Washington |
Seattle |
WA |
US |
|
|
Assignee: |
University of Washington
Seattle
WA
|
Family ID: |
41430898 |
Appl. No.: |
14/138616 |
Filed: |
December 23, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13858245 |
Apr 8, 2013 |
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14138616 |
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13528016 |
Jun 20, 2012 |
8441624 |
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13858245 |
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12497281 |
Jul 2, 2009 |
8264683 |
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13528016 |
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11531998 |
Sep 14, 2006 |
7804595 |
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12497281 |
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61077808 |
Jul 2, 2008 |
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60716861 |
Sep 14, 2005 |
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Current U.S.
Class: |
356/446 |
Current CPC
Class: |
G01N 33/15 20130101;
A61B 5/0059 20130101; G01N 21/01 20130101; G01N 21/70 20130101;
G01N 21/49 20130101; G01N 15/1459 20130101; G01N 2021/0193
20130101; G01N 2015/1493 20130101; A61B 8/481 20130101 |
Class at
Publication: |
356/446 |
International
Class: |
G01N 21/49 20060101
G01N021/49; G01N 21/01 20060101 G01N021/01; G01N 33/15 20060101
G01N033/15; G01N 21/70 20060101 G01N021/70 |
Goverment Interests
STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED
RESEARCH AND DEVELOPMENT
[0002] This invention was made with U.S. government support under
5R01EB000350 awarded by the National Institutes of Health (NIH).
The U.S. government has certain rights in the invention.
Claims
1. (canceled)
2. A method of separating a first sample population from a sample
solution including the first sample population and a second sample
population different than the first sample population, the method
comprising: inputting the sample solution in a fluid flow channel;
using an energy source to administer an energy pulse to the sample
solution as the sample solution flows through the fluid flow
channel; and separating particles of the first sample population
from the sample solution as the sample solution flows through the
fluid channel based on a reaction of the particles to the energy
pulse.
3. The method of claim 2, further comprising collecting a dynamic
scattering intensity profile of the particles while the particles
react to the energy pulse and wherein separating particles based on
the reaction comprises separating based on the collected dynamic
scattering intensity profile of the particles.
4. The method of claim 3, wherein collecting the dynamic scattering
intensity profile of the particles comprises illuminating the
particles with a light source and capturing light scattered from
the particles as the particles react to the energy pulse.
5. The method of claim 3, wherein separating particles based on the
reaction comprises comparing the collected dynamic scattering
intensity profile to a database of dynamic scattering intensity
profiles and identifying the particles based on the comparison.
6. The method of claim 2, wherein the energy pulse is administered
to the sample solution constantly as the sample solution moves
through the fluid flow channel.
7. The method of claim 2, wherein the energy pulse is initially
administered to a particle in the fluid flow channel as the
particle enters a region of interest of the fluid flow channel and
then terminated as the particle travels through the region of
interest.
8. A method of modifying a flow cytometer having a sampling volume,
a light source for illuminating a sample within the sample volume
at a region of interest, and a light sensor for detecting light
scattered from the region of interest, the method comprising:
incorporating a pressure generator with the flow cytometer so that
the pressure generator induces pressure changes in the sampling
volume when energized.
9. The method of claim 2, wherein the pressure generator generates
pressure changes in the sampling volume prior to the region of
interest of the sampling volume.
10. The method of claim 2, wherein the pressure generator generates
pressure changes in the sampling volume at the region of interest
of the sampling volume.
11. The method of claim 2, wherein the pressure generator comprises
an ultrasound device.
12. The method of claim 2, wherein the pressure generator is
coupled to an external face of the sampling volume and is
acoustically coupled the sampling volume.
13. The method of claim 2, wherein the pressure generator is
coupled to an internal face to the sampling volume.
14. The method of claim 2, further comprising incorporating a
pressure sensor to measure pressure in the sampling volume and to
generate a pressure signal in response to measured pressure.
15. The method of claim 14, wherein the pressure sensor comprises a
hydrophone.
16. The method of claim 14, further comprising coupling the
pressure sensor with the light sensor of the flow cytometer such
that the light sensor captures scattered light and generates a
scattered light signal in response to the pressure signals
generated by the pressure sensor.
17. The method of claim 2, wherein the flow cytometer further
comprises a particle sorter, and wherein the method further
comprises modifying the particle sorter to sort particles based on
a scattering intensity profile collected while particles react to a
pressure wave generated by the pressure generator.
18. A method of separating first particles from a particle sample
having first particles and second particles different from the
first particles, the method comprising: collecting a scattering
intensity profile associated with a first particle of the particle
sample while the first particle reacts to a pressure wave; sorting
the first particle from an unsorted portion of the particle sample
based on the collected scattering intensity profile.
19. The method of claim 18, further comprising: delivering the
particle sample to a sampling volume with a region of interest;
inducing a pressure change in the sample volume by administering
the pressure wave so that the first particle from the particle
sample exhibits a reaction to the pressure change as the first
particle enters the region of interest; illuminating the first
particle in the region of interest with a light source; and
collecting the scattering intensity profile of the first
particle.
20. The method of claim 19, wherein the pressure wave is
administered during the collection of the scattering intensity
profile.
21. The method of claim 19, wherein the pressure wave is
administered initially to excite the first particle as the first
particle enters the region of interest and then terminated as the
first particle travels through the region of interest of the
sampling volume.
22. The method of claim 18, wherein sorting the first particle from
the unsorted portion of the particle sample comprises, comparing
the collected scattering intensity profile to previously determined
dynamic scattering intensity profiles and identifying the first
particle based on the comparison.
23. A system for analyzing particles within a sample, the system
comprising: a fluid channel having a region and a sample inlet
upstream from the region; an acoustic transducer acoustically
coupled with the fluid channel and configured to administer an
acoustic wave to the sample as the sample flows within the fluid
channel prior to or at the region; a light source for illuminating
the sample at the region; a light sensor for detecting light
scattered from the sample in response to being illuminated by the
light source, the light sensor configured to generate a signal in
response to the detected scattered light; a processor configured to
analyze the signal from the light sensor to determine one or more
characteristics of particles within the sample.
24. The system of claim 23, wherein the fluid channel is defined by
an interior and an exterior, and wherein the acoustic transducer is
acoustically coupled to the exterior of the fluid channel.
25. The system of claim 23, wherein the fluid channel is defined by
an interior and an exterior, and wherein the acoustic transducer is
acoustically coupled to the interior of the fluid channel.
26. The system of claim 23, further comprising a pressure sensor
coupled with the fluid channel and configured to measure a pressure
of the sample at the region and generate a signal in response to
measured pressures.
27. The system of claim 26, wherein the pressure sensor is coupled
with the light sensor such that the light sensor is triggered to
capture scattered light in response to the signals from the
pressure sensor.
28. The system of claim 23, further comprising one or more
reservoirs branching from the fluid channel downstream from the
region.
29. The system of claim 28, further comprising a particle sorter
coupled with the fluid channel and positioned downstream from the
region and upstream of the one or more reservoirs, the particle
sorter configured to sort particles into the one or more
reservoirs.
30. The system of claim 29, wherein the processor is configured to
determine dynamic scattering intensity profiles of particles in the
sample based on the signal from the light sensor, and wherein the
particle sorter is configured to sort the sample based on the
dynamic scattering intensity profiles of particles in the
sample.
31. A method of measuring characteristics of a particle in a
particle sample, the method comprising: agitating the particle
sample so that the particle of the particle sample undergoes a
physical property change; interrogating the particle while the
particle undergoes the physical property changes with a light
source; capturing light scattered by the particle while the
particle is interrogated by the light source and as the particle
undergoes physical property changes; generating a light scatter
signal in response to the captured scattered light; identifying a
physical characteristic of the particle by processing the light
scatter signal.
32. The method of claim 31, wherein the physical property change
comprises an increase in size of the particle.
33. The method of claim 32, wherein the physical property changes
comprises an oscillation in size of the particle.
34. The method of claim 31, wherein the particle sample is agitated
using an acoustic transducer.
35. The method of claim 31, further comprising measuring pressure
changes of the particle sample and identifying a physical
characteristic of the particle based in part on the pressure
changes.
36. The method of claim 35, wherein the capturing of light
scattered by the particle is triggered by a measured pressure
change.
37. The method of claim 31, wherein processing the light scatter
signal comprises computing a power spectral density of the light
scatter signal.
38. The method of claim 31, wherein identifying a physical
characteristic of the particle comprises identifying ratio of a
maximum radius and an ambient radius of the particle in the
particle sample and identifying whether the particle is breaking up
based on the ratio.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. Ser. No.
13/858,245 filed Apr. 8, 2013; which application is a continuation
of U.S. Ser. No. 13/528,016 filed Jun. 20, 2012 (now U.S. Pat. No.
8,441,624); which application is a continuation of U.S. Ser. No.
12/497,281 filed on Jul. 2, 2009; which claims the benefit of U.S.
Provisional Appln. No. 61/077,808 filed on Jul. 2, 2008; and which
is a continuation-in-part of U.S. Ser. No. 11/531,998 filed on Sep.
14, 2006; which claims the benefit of U.S. Provisional Appln. No.
60/716,861 filed on Sep. 14, 2005. All of the disclosures of which
are hereby incorporated by reference in their entirety for all
purposes.
BACKGROUND OF THE INVENTION
[0003] Efficiently determining the size and physical
characteristics of relatively small particles (such as cells or
microbubbles) can be a challenging task. In particular, gas-filled
microbubbles with an encapsulating shell, generally referred to as
ultrasound contrast agents (UCAs), are used regularly in diagnostic
ultrasound and are becoming important in therapeutic ultrasound
applications. In general, UCAs are very small bubbles, on the order
of a micron in diameter, stabilized against dissolution with a
coating material (such as a lipid-based material, an albumin-based
material, or a polymer-based material). Clearly, the physical
properties of any material used for medical applications must be
well understood. As such, it would be desirable to provide
efficient techniques for investigating the physical properties of
UCAs, to enable UCAs to be more effectively used in diagnostic and
therapeutic medical applications.
[0004] Further, it would thus be desirable if such techniques could
be used to efficiently study other types of similar sized
particles.
BRIEF SUMMARY OF THE INVENTION
[0005] This application specifically incorporates by reference the
disclosures and drawings of each patent application identified
above as a related application.
[0006] Disclosed herein are techniques to perform the following
functions: determining the size of particles (e.g., drops and
bubbles); determining their thresholds for changes, such as
destruction; and, obtaining information about their dynamic
properties using a flow-based instrument that can rapidly analyze
large populations of particles.
[0007] Particles in flow are introduced into a sample volume. Light
scattered by a particle in the sample volume is collected and
analyzed, as is also done in conventional flow cytometry. However,
the technique disclosed herein is distinguished from conventional
flow cytometry by the use of an acoustic source or pressure source
that is disposed to direct acoustic energy (or a pressure pulse)
into the sample volume. As the particle passes through the sample
volume, it responds to the acoustic energy (or pressure pulse),
causing changes in the light scattered by the particle. Those
changes, which are not measured by conventional flow cytometry, can
be analyzed to determine additional physical properties of the
particle.
[0008] In one exemplary embodiment, the acoustic energy is directed
at the particle at a constant rate. In another exemplary
embodiment, the acoustic energy is directed at the particle at a
variable rate. In still another exemplary embodiment, the acoustic
energy is directed at the particle initially and then terminated,
so that the scattered light provides information about a decay rate
of particle vibrations induced by the acoustic energy (or pressure
pulse).
[0009] Thus, the concepts disclosed herein employ scattered light
to measure the pulsations of an UCA or other particle as it is
exposed to acoustic energy or a pressure pulse. In one exemplary
embodiment, the particle is introduced into a fluid, and the fluid
is directed through a sample volume. The particle is exposed to
acoustic energy, while the optical scattering data are processed.
The scattering intensity is related to the radius of the particle.
Thus, changes in the radius due to vibrations induced in the
particle by the acoustic energy results in variations in the
scattering intensity. The collected data are processed to provide a
radius versus time (RT) relationship. The RT relationship is fit to
one or more conventional dynamic models using known techniques
(such as linear squares). Depending on the model employed, the
fitted empirical data can be used to determine one or more UCA
parameters, such as shear modulus, and shell viscosity.
[0010] More broadly stated, the scattering intensity (or amplitude)
is related to the properties of the particle. Thus, changes in the
properties of the particle due to vibrations induced in the
particle by the acoustic energy results in variations in the
scattering intensity (or amplitude). The collected data are
processed to provide an amplitude versus time (AT) relationship.
The AT relationship is fit to one or more dynamic models using
known techniques. The RT relationship noted above is one type of AT
relationship. As noted above, the use of a particle model enables
fitted empirical data to be used to determine one or more particle
parameters. In an exemplary embodiment, one property being analyzed
is the radius of the particle, but it should be recognized that the
amplitude changes can be analyzed to determine other particle
properties as well. Exemplary properties include, but are not
limited to a radius of the particle, a shell viscosity of the
particle, and a shear modulus of the particle. The specific
parameters that can be determined are a function of the specific
particle model being employed. Several specific particle models are
discussed herein, but it should be recognized that the empirical AT
curve that can be collected by the techniques disclosed herein can
be used with many different particle models, and not only those
particle models specifically discussed herein.
[0011] A system for implementing the light scattering technique
includes a sample volume into which the fluid containing the
particle can be introduced, a light source for illuminating the
particle, a light sensitive detector for collecting light scattered
by the particle, an acoustic transducer for directing acoustic
energy at the particle, and a processor for manipulating the
collected data. Preferably, the light source is a laser, the light
sensitive detector is a photomultiplier tube (PMT), and the
processor is a computing device (although other types of logical
processing devices, such as an applications specific integrated
circuit, can also be employed). In an exemplary embodiment, the
processor is configured to generate an RT curve (or AT curve) based
on the collected data, to fit the curve to one or more pre-defined
models, and to calculate one or more parameters based on the fitted
RT curve. It should also be recognized that the processor can
manipulate the data to determine other parameters.
[0012] In general, a conventional flow cytometer can be modified to
achieve such a system, by adding the acoustic transducer, and
modifying the processor.
[0013] The data collected by such a modified flow cytometer can be
considered to include dynamic scattering intensity spectrums (or
dynamic scattering intensity curves). In yet another embodiment,
such dynamic scattering intensity spectrums can be determined for
specific particles, and then used to separate those particles from
a larger population of particles. In other words, the dynamic
scattering intensity spectrums can be used to sort particles based
on their spectrums (different particles exhibiting different
spectrums).
[0014] An exemplary method includes the steps of collecting
scattering data using a system generally consistent with the system
described above, while a particle is exposed to acoustic
energy.
[0015] This Summary has been provided to introduce a few concepts
in a simplified form that are further described in detail below in
the Description. However, this Summary is not intended to identify
key or essential features of the claimed subject matter, nor is it
intended to be used as an aid in determining the scope of the
claimed subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Various aspects and attendant advantages of one or more
exemplary embodiments and modifications thereto will become more
readily appreciated as the same becomes better understood by
reference to the following detailed description, when taken in
conjunction with the accompanying drawings, wherein:
[0017] FIG. 1 schematically illustrates exemplary method steps for
using scattered light to calculate one or more UCA parameters;
[0018] FIG. 2 schematically illustrates an exemplary system for
implementing the method steps of FIG. 1;
[0019] FIG. 3A graphically illustrates the relationship between
scattering intensity and scattering angle;
[0020] FIG. 3B graphically illustrates the relationship between
bubble radius and scattered light intensity;
[0021] FIG. 4A is a simulation graphically illustrating the
dependence of shell parameters on microbubble size at a particular
pressure and frequency;
[0022] FIG. 4B graphically illustrates a waterfall plot of the
simulated response curve R(t) for various initial bubble sizes and
a fixed shell parameter using the same drive amplitude and
frequency;
[0023] FIG. 4C graphically illustrates an exemplary simulation
contour map of (R.sub.max-R.sub.0) vs. R.sub.0 and
.epsilon..mu..sub.sh showing the dependence of shell parameters and
bubble sizes on the bubble's response to an incident sound
pulse;
[0024] FIG. 4D graphically illustrates the peak in the power
spectral density (the main frequency component) of the simulation
shown in FIG. 4A;
[0025] FIGS. 5A-5B, 6A-6B, and 7A-7B graphically illustrate
empirical data and model fits for two different types of UCAs, in
both water and a more viscous liquid (xanthan gum gel);
[0026] FIG. 8 (Table I) summarizes parameters for the data set
corresponding to FIGS. 5A-5B, 6A-6B, and 7A-7B;
[0027] FIGS. 9A-9C graphically illustrate how a Sonazoid.TM.
microbubble in water dynamically evolves over consecutive acoustic
pulses, collected using three successive groups of ten pulses;
[0028] FIG. 10 (Table II) summarizes parameters for the data set
corresponding to FIGS. 9A-9C;
[0029] FIGS. 11A-11D graphically illustrate the dynamical evolution
of an Optison.TM. bubble to individual (i.e., non-averaged)
pressure pulses from diagnostic ultrasound in an aqueous/xanthan
gum solution;
[0030] FIG. 12 (Table III) summarizes response curve parameters for
the data set corresponding to FIGS. 11A-11D;
[0031] FIGS. 13A-13F graphically illustrate the dynamical evolution
of a Sonazoid.TM. bubble in water, showing individual responses
(i.e., non-averaged responses) due to consecutive ultrasound
pulses;
[0032] FIG. 14 (Table IV) summarizes response curve parameters for
the data set corresponding to FIGS. 13A-13F;
[0033] FIG. 15 graphically illustrates normalized PSDs from FIGS.
9A-9C;
[0034] FIG. 16 schematically illustrates yet another exemplary
system to collect light scattered by microbubbles during changing
pressure conditions;
[0035] FIG. 17A illustrates a modified Gaussian pulse used to
simulate a driving signal for different dynamic models;
[0036] FIG. 17B illustrates typical bubble responses using four
different dynamic bubble models;
[0037] FIGS. 18A-18C graphically illustrate results provided by
three different dynamic models, with varying parameters;
[0038] FIGS. 19A (showing measured driving pressure pulse) and 19B
(showing measured bubble response and fits) graphically illustrate
a comparison between the experimental data and simulated results,
with a Sonovue.TM. bubble oscillating with a driving pressure
amplitude of about 0.15 MPa;
[0039] FIG. 20A is a graph showing a change of a shell shear
modulus as a function of radius;
[0040] FIG. 20B is a graph showing a change of a shell viscosity as
a function of radius;
[0041] FIG. 21A graphically illustrates a typical effective RT
curve of a group of UCA bubbles stimulated using B-Mode
ultrasound;
[0042] FIG. 21B graphically illustrates the power spectrum
corresponding to the data of FIG. 21A;
[0043] FIG. 21C graphically illustrates power spectrums collected
from UCA bubbles collected at different times, using the same
acoustic power settings;
[0044] FIG. 22 is a composite image graphically illustrating the
power spectrum of UCA bubbles driven by different acoustic power
settings using B-Mode ultrasound;
[0045] FIGS. 23A and 23B are composites images, with FIG. 23A
including RT curves, and FIG. 23B including power spectrums,
showing how bubbles dynamically evolve over several acoustic
pulses;
[0046] FIG. 24 graphically illustrates data averaged over 100
consecutive pulses, showing dynamic changes to bubble size;
[0047] FIG. 25A includes RT curves of the same group of UCA bubbles
during the consecutive insonification, showing their dynamic
response to the acoustic pulse;
[0048] FIG. 25B shows power spectrums corresponding to the data of
FIG. 25A;
[0049] FIG. 26A graphically illustrates an RT curve;
[0050] FIG. 26B graphically illustrates a power spectrum
corresponding to the data of FIG. 26A;
[0051] FIG. 27 graphically illustrates a typical RT curve for a
mass of UCA bubbles stimulated with Pulse-Doppler Mode
ultrasound;
[0052] FIG. 28 graphically illustrates a power spectrum
corresponding to the data of FIG. 27;
[0053] FIG. 29 is a composite image graphically illustrating the
power spectrum of multiple UCA bubbles being driven by different
acoustic powers (MI) in a Pulse-Doppler Mode;
[0054] FIG. 30A graphically illustrates a typical response from a
mass of UCA bubbles stimulated by M-Mode ultrasound;
[0055] FIG. 30B graphically illustrates a power spectrum
corresponding to the data of FIG. 30A;
[0056] FIG. 30C is a composite image that graphically illustrates
consecutive effective RT curves of a mass of UCA bubbles responding
to M-Mode stimulation;
[0057] FIG. 31 schematically illustrates an exemplary flow
cytometer, modified by the addition of an acoustic transducer to
direct an acoustic pulse toward a particle from which scattered
light will be collected, to implement the concepts disclosed
herein;
[0058] FIGS. 32A-C are plan views of exemplary flow cytometer
sample volumes, showing the relative locations of the sample
volume, an acoustic transducer to direct an acoustic pulse toward a
particle from which scattered light will be collected, and a light
source for illuminating the particle;
[0059] FIGS. 33A-33C are side views of an exemplary flow cytometer
sample volume, schematically illustrating a particle moving through
the sample volume while interacting with an acoustic pressure pulse
in a variety of ways;
[0060] FIG. 34A is a functional block diagram of another exemplary
flow cytometer system including an acoustic transducer to direct
acoustic energy toward a particle, before or while light scattered
by the particle is detected;
[0061] FIG. 34B schematically illustrates an exemplary relationship
between a light source used to illuminate a particle in a sample
volume, and a light collection system used to collect light
scattered by the particle and direct that light to a sensor;
[0062] FIG. 35A graphically illustrates a static scattering
intensity profile collected from a particle in a flow cytometer
under constant pressure conditions;
[0063] FIGS. 35B and 35C graphically illustrate dynamic scattering
intensity profiles collected from a particle in a flow cytometer
under varying pressure conditions;
[0064] FIG. 35D graphically illustrates a plurality of dynamic
scattering intensity spectrums, where each particle from which
light is being collected is reacting to an applied pressure pulse
or acoustic pulse; and
[0065] FIG. 36 is a functional block diagram of a suitable
computing environment for practicing the concepts disclosed
herein.
DETAILED DESCRIPTION OF THE INVENTION
Figures and Disclosed Embodiments are not Limiting
[0066] Exemplary embodiments are illustrated in referenced Figures
of the drawings. It is intended that the embodiments and Figures
disclosed herein are to be considered illustrative rather than
restrictive. No limitation on the scope of the technology and of
the claims that follow is to be imputed to the examples shown in
the drawings and discussed herein.
[0067] As used herein and the claims that follow, it should be
understood that the terms "UCA," "microbubble," and "encapsulated
microbubble" have been used interchangeably. These terms refer to
relatively small (on the order of microns in size) bubbles
including a shell and a core. Shells are generally implemented
using lipids, polymers, and/or albumin (although such materials are
intended to be exemplary, rather than limiting), while cores are
generally implemented using gases such as air, perfluoropropane
(PFP), perfluorobutane (PFB), and octafluoropropane (OFP) (although
such materials are intended to be exemplary, rather than
limiting).
[0068] FIG. 1 schematically illustrates exemplary method steps for
using scattered light to calculate one or more UCA parameters. The
exemplary method steps include collecting light scattered from a
UCA while the UCA is exposed to varying pressure conditions, as
indicated by a block 10. An RT curve is generated based on the
collected data, as indicated by a block 12, and the RT curve is
fitted to one or more predefined models, as indicated by a block
14. The fitted curve is used to calculate one or more UCA
parameters, as indicated in a block 16.
[0069] FIG. 2 schematically illustrates an exemplary system 20 for
implementing the method steps of FIG. 1. System 20 includes a fluid
volume 22 (for example, implemented using an optically transparent
container, such as glass or plastic) into which the UCA can be
introduced, a light source 24 for illuminating the UCA, a light
sensitive detector 26 for collecting light scattered by the UCA, a
pressure source for varying the pressure in the fluid volume
(preferably implemented using an ultrasound probe/system 28), a
sensor 30 for measuring actual pressure conditions, and a processor
32 for manipulating the collected data. In this exemplary
embodiment, light source 24 is a laser, light sensitive detector 26
is a photomultiplier tube (PMT), sensor 30 is a hydrophone, and
processor 32 is a computing device (an oscilloscope 34 can be used
to manipulate the signal from the PMT before the data are processed
by the computing device). Processor 32 is configured to generate an
RT curve based on the collected data, to fit the curve to one or
more pre-defined models, and to calculate one or more parameters
based on the fitted RT curve. A lens 36 may (or may not) be used to
direct light scattered by a UCA in the fluid volume toward the PMT.
A UCA injector 38 (such as a syringe pump or pipette) is used to
inject a UCA agent into the fluid volume. While not specifically
shown in the Figure, a scattering angle from about 70 degrees to
about 90 degrees is desirable, and the relative orientations of one
or more of the injector, light source, and lens can be manipulated
to achieve such a scattering angle. The laser light source employed
in an empirical system was a red helium/neon (HeNe) laser, having a
wavelength of 633 nm.
[0070] Various Figures provided herein graphically depict RT curves
generated using light scattered by microbubbles. Such Figures often
include both solid lines and dashed lines. Except where otherwise
indicated, the solid line refers to empirically collected data,
while the dashed line refers to fitted data. Those of ordinary
skill in the art will readily recognize that many fitting
algorithms and commercial fitting software programs are available.
It should also be recognized that many different dynamic models
describing microbubble are available, or may become available. Many
variables in the model can be measured or estimated, to minimize
the number of variables that are fitted. The unknown variables can
be limited to shell parameters. Examples of variables that can be
measured include pressure (e.g., as measured by the hydrophone) and
bubble radius (which can be measured optically using a microscope
or microscope and camera, or with light scattering while the bubble
is static). Radius measurements for many UCAs are readily available
in the published literature.
[0071] Having briefly discussed the exemplary method and apparatus,
it will be useful to provide general information about light
scattering and dynamic models describing the motion of
microbubbles, so that the above noted concepts are understood in
context.
[0072] The Mie theory describes light scattering from homogeneous
spheres (or bubbles, in the context of the current disclosure) in a
homogeneous environment. In general, this theory indicates that the
intensity of scattered light depends strongly on the observation
angle. For an air bubble in water, and for a single light detector,
the observation angle should be near the critical angle (about 83
degrees) from forward scattering. This preference is based on a
physical-optics approximation, which suggests that the scattered
light intensity is a monotonic function of bubble size.
Calculations and empirical data have indicated the presence of
relatively thin shells (i.e., on the order of 10-15 nm), which does
not substantially change the relationship between scattering
intensity and scattering angle (graphically illustrated in FIG. 3A,
where the dashed lines represented thin shell bubbles and the solid
lines representing bubbles without shells are nearly
indistinguishable), indicating the Mie theory can be usefully
applied to UCA (which are not homogenous spheres), as well as to
homogeneous spheres. Significantly, the Mie theory also establishes
a monotonic relationship between sphere/bubble size and scattered
light intensity (larger bubbles result in an increase in the
intensity of the scattered light), as indicated in FIG. 3B. Because
of the relationship indicated in FIG. 3B, it is straightforward to
convert scattered light intensity into a radius. Significantly,
even if the calculated radius varies from the actual radius, the
relative differences between radii calculated based on different
measured scattered light intensities can still be quite useful in
generating the RT curve discussed above, which once fitted to a
selected dynamic bubble model, enables calculation of UCA
parameters to be carried out, generally as discussed above.
[0073] With respect to the system of FIG. 2, it is important to
recognize that the detector collects light from a finite angular
distribution, not just at a single angle (the lens being employed
to increase the light intensity onto the detector). Preferably, the
angular span ranges from approximately 70.degree. to 90.degree..
The curve in FIG. 3B shows the relative integrated intensity over
this span as a function of bubble size.
[0074] As noted above, the use of dynamic models of UCA bubbles is
an important aspect to the concepts disclosed herein. Fortunately,
there are many models from which to choose, and empirical evidence
suggests that the concepts disclosed herein can be used with many
of these models. There are several approaches for modeling a coated
bubble, many of which are based on the RPNNP equation, which
describes the response of a spherical bubble to a time-varying
pressure field (including acoustic pressure) in an incompressible
liquid:
P L R R + 3 2 .rho. L R . 2 = P g ( R 0 R ) 3 .gamma. + P v - P 0 -
2 .sigma. R - .delta..omega..rho. L R R . - P a cos ( .omega. t ) (
1 ) ##EQU00001##
where R.sub.0 is the initial bubble radius, .rho..sub.L is the
density of a Newtonian liquid, P.sub.0 is the ambient pressure,
P.sub.v, is the vapor pressure, .sigma. is the surface tension,
.gamma. is the polytropic exponent of the gas, .delta. is the
damping coefficient, P.sub..alpha. is the amplitude of the incident
acoustic pressure, .omega. is the angular frequency of driving
signal, and P.sub.g is the gas pressure inside the bubble
(P.sub.g=P.sub.0-P.sub.v+2.sigma./R.sub.0).
[0075] The assumptions for Eq. (1) include following: (1) the
motion of the bubble is symmetric; (2) the wavelength of ultrasound
is much larger than the bubble radius; (3) no rectified diffusion
occurs; and, (4) the bubble contains gas or vapor, which is
compressed and behaves according to the gas law, with the
polytropic parameter held constant.
[0076] De Jong's model, Church's model, Hoff s model, and Sarkar's
model, discussed in greater detail below, are each modified from
the general PRNNP equation. The choice of which bubble dynamics
model is employed is not based on the relative accuracy of any
particular model. It should be recognized that these models should
not be considered restrictive; as new models that may be developed
can also be employed.
[0077] Initial work in developing the concepts disclosed herein
employed a simplified model that has previously been used in
comparisons with high-speed camera images of encapsulated
microbubble dynamics, the Morgan et al. model. A major advantage in
the Morgan model is that it has a reduced set of fitting
parameters. The Morgan model is:
.rho. R R + 3 2 .rho. R . 2 = ( P 0 + 2 .sigma. R 0 + 2 .chi. R 0 )
( R 0 R ) 3 .gamma. ( 1 - 3 R c ) - 4 .mu. R R - 2 .sigma. R ( 1 -
R c ) - 2 .chi. R ( R 0 R ) 2 ( 1 - 3 R c ) - 12 .mu. sh R R ( R -
) - ( P 0 + P drive ( t ) ) ( 2 ) ##EQU00002##
where R is radius of the bubble, R.sub.0 is initial radius of the
bubble, P.sub.0=1.01.times.10.sup.5 Pa is the ambient pressure,
P.sub.drive(t) is the acoustic driving pressure, p=1000 kg/m.sup.3
is the liquid density, .gamma..apprxeq.1 is the ratio of specific
heats, c=1500 m/s is the sound speed in the liquid, .sigma.=0.051
N/m.sup.2 is the surface tension coefficient, .chi.=0 is the shell
elasticity, .mu.=0.001 Pa s is fluid shear viscosity, .mu..sub.sh
is the UCA shell shear viscosity, and .epsilon. is the UCA shell
thickness.
[0078] Using Eq. (2), the relevant parameter space was examined to
determine the relationship between the various parameters, which
was done in order to determine if a fit to the data would be
unique. For UCAs, this parameter space covers
0.1.ltoreq.R.sub.0.ltoreq.6 mm,
0.ltoreq..epsilon..mu..sub.sh.ltoreq.8 nm Pa s, and
0.0235.ltoreq.P.sub.drive(t).ltoreq.1.2 MPa (peak negative),
relevant for thin shelled agents. Because isothermal behavior is
assumed, the elasticity terms cancel. Assuming R>>.epsilon.
(also assumed by Morgan in developing the model), the only term
with shell parameters is given by 12.epsilon..mu..sub.shR/R.sup.2.
Hence, the shell parameter can be referred to as the product
.epsilon..mu..sub.sh. Note that there are initially three unknowns:
R.sub.0,P.sub.drive(t), and the product .epsilon..mu..sub.sh.
[0079] Referring to the driving pressure P.sub.drive(t), a
calibrated needle hydrophone (the sensor in FIG. 2) can be used to
measure acoustic driving pressure, as an input to the Morgan bubble
dynamics model, thereby decreasing the unknowns by one. Most of the
initially collected empirical data was obtained from the M-Mode of
a diagnostic ultrasound system (the ATL Ultramark 4Plus.TM.).
Empirical data indicate the acoustic driving pressure of the
selected ultrasound system falls well within the above-noted
parameter space for the driving pressure.
[0080] The remaining parameters are R.sub.0 and the product
.epsilon..mu..sub.sh. Significantly, examining the parameter space
is necessary in order to ensure that the empirically fitted data
will be unique. FIG. 4A graphically illustrates a simulation for
microbubbles with a varying shell parameter, for an initial bubble
radius size of R.sub.0=1 .mu.m, and P.sub.drive(t)=235 kPa peak
negative pressure. FIG. 4B graphically illustrates a waterfall plot
of the simulated response curve R(t) for various initial bubble
sizes and a fixed shell parameter (same drive amplitude). The
resonant bubble size is darkened. FIG. 4C graphically illustrates a
simulation contour map of (R.sub.max-R.sub.0) vs. R.sub.0 and
.epsilon..mu..sub.sh, (with the same drive amplitude). Finally,
FIG. 4D graphically illustrates the peak in the power spectral
density (the main frequency component) of the simulation in FIG.
4A. Significantly, the resonant bubble size can be seen in FIG. 4B,
where the curves appear to bunch together. As expected, the
response curve R(t) has significant fluctuations near resonance. In
FIG. 4C, the resonant bubble size increases with increasing shell
parameter, from about 1.3 .mu.m to about 2.1 .mu.m, which is an
expected behavior, consistent with the thin shell behaving as a
damping mechanism. That is, an increase in damping results in a
decrease in resonant frequency, or, equivalently, an increase in
resonant size FIG. 4C also shows that near resonance, there is a
strong dependence on the shell parameter (when moving from contour
to contour). However, for bubbles larger than about 3 .mu.m, the
dependence is weak at best (note the vertical contour lines). Thus,
for larger UCAs, this model would not be useful for fitting shell
parameters to the data.
[0081] Furthermore, the maximum amplitudes of the two main peaks in
FIG. 4A change relative to each other as the shell parameter
increases. The first peak, initially smaller than the second peak,
becomes the larger peak for .epsilon..mu..sub.sh>.0.4 nm Pa s,
which is most probably a consequence of the specific pressure pulse
used. That is, the pressure pulse has two resonant peaks, near 2.3
MHz and 3.2 MHz. Because the resonance size depends on the shell
parameter, as the shell parameter increases, it is possible that
first one, and then the other of these resonances are manifest,
resulting in a change in the bubble response.
[0082] The power spectral density (PSD) for the example of FIG. 4A
is shown in FIG. 4D. The peak in the PSD decreases with increasing
shell parameter, but levels off quickly. It would be difficult to
distinguish between two bubbles with different shell parameters,
for .epsilon..mu..sub.sh>3 nm Pa s, using only the PSD; however,
the PSD might be used to determine when the shell breaks. The PSD
is the frequency response of the system driven by the measured
pressure pulse, and thus, includes the spectral characteristics of
the driving pulse. It is nevertheless instructive to compare it to
the bubble's resonance frequency, obtained from linearizing the
equation of motion, setting R.fwdarw.R.sub.0(1+.epsilon.),
expanding relevant terms in a binomial expansion, and neglecting
second-order and higher terms. This leads to the frequency of
oscillation, f.sub.r, as follows:
f r = 1 2 .pi. 3 .gamma. .rho. R 0 2 ( P 0 + 2 .sigma. R 0 + 2
.chi. R 0 ) - 2 .sigma. + 6 .chi. .rho. R 0 3 - ( 4 .mu. + 12 .mu.
sh / R 0 ) 2 .rho. 2 R 0 4 ( 3 ) ##EQU00003##
[0083] Using the parameters above (R.sub.0=1 .mu.m, .gamma.=1), and
considering the undamped case (.epsilon..mu..sub.sh=.mu.=0), the
linear resonance frequency, f.sub.r, is approximately 3.5 MHz. The
frequency of oscillation does not follow the PSD curve (because the
PSD includes the driving pulse spectral characteristics). Instead,
it drops quickly to zero near .epsilon..mu..sub.sh=1.6 nm Pa s (for
a 1 .mu.m bubble), which appears to imply that smaller bubbles are
over damped and do not resonate.
[0084] The discussion above has been limited to resonances and the
relationship with a particular pressure pulse. However, the most
important reason for mapping the parameter space is to determine if
a solution is unique, because as mentioned above, there are two
unknown parameters to be fitted, namely R.sub.0 and the product
.epsilon..mu..sub.sh. To help solve this problem, it is helpful to
focus on FIG. 4C (the contours of (R.sub.max-R.sub.0) vs. R.sub.0
and .epsilon..mu..sub.sh), bearing in mind that the pressure
amplitude has been measured, which constrains the analysis to the
amplitude of R(t).
[0085] If R.sub.0>3 .mu.m the quantity (R.sub.max-R.sub.0) is
not as sensitive to the shell parameter, making unique fits
difficult. Fortunately, with UCAs, the majority of bubbles are in
the size range from about R.sub.0=1 .mu.m to about 2.5 .mu.m. In
this range, the contours show sensitive dependences (note the
darkened contour line in FIG. 4C). If product .epsilon..mu..sub.sh,
is initially set to 2 nm Pa s, there will be two possible solutions
for R.sub.0 that would result in the same (R.sub.max-R.sub.0)
value, near 1 .mu.m and 2.4 .mu.m. However, R.sub.max itself is
different for these two values. For example, if the condition
R.sub.0=1 .mu.m is chosen, then R.sub.max=1.6 .mu.m, and if
R.sub.0=2.4 .mu.m, then R.sub.max=3.0 .mu.m. The empirical data
will constrain the results to only one of these values. In
conclusion, although the above discussion leads to a two-parameter
fit, the data constrain the solutions to a single parameter. In
this model, the shell parameter is not important for larger
bubbles, but for microbubbles of interest, it is a sensitive
parameter; thus, the task of uniquely fitting the data to the model
is feasible.
[0086] The following empirical study employed a system generally
consistent with that shown in FIG. 2. The study involved injecting
individual microbubbles into a region of interest, insonifying the
microbubble with ultrasound, and collecting light scattered from
the microbubble. The region of interest is the small volume of
liquid where the ultrasound and laser illumination intersect a
microbubble. For most studies, the liquid was filtered (0.2 .mu.m
porosity) and de-ionized (having >18 M.OMEGA. resistance)
water.
[0087] Two methods were used to inject UCAs into the region of
interest. Most often, a highly-diluted UCA solution (calculated to
be on the order of 10.sup.5/ml) was injected into a rectangular
water tank (3.5 cm.sup.2 cross section, filled to a height of about
4 cm) with a syringe pump (at a rate of 10 ml/h) with a 0.5 mm
inner-diameter tube. The ejection of the microbubble was
approximately one-half cm from the laser beam path. Based on the
numbers given above, it might be expected that subsequent bubbles
would generate a scattering "event" about every 3 ms. However, the
actual frequency of events was much less (approximately one event
over several seconds). The most likely reason for this phenomenon
is due to UCA congregation within the syringe, and at curves in the
tubing, especially where the tubing goes up and over a lip. Also,
bubbles ejected from the tip may move away from the laser beam, and
not into it.
[0088] To verify that the measured response curves were for single
microbubbles, UCAs were injected manually into the water-filled
vessel that contained a small amount of a water soluble gum (e.g.,
xanthan gum). The xanthan gum increased the viscosity of the liquid
slightly, so that after injection, the microbubble came to rest and
remained relatively stationary. The microbubble was then imaged
with a back-lit LED, microscope, and CCD camera to verify that
there was indeed a single bubble in the region of interest. The
fluid vessel was then repositioned so that the bubble was at the
center of the laser beam/ultrasound probe focus. Empirical data
indicate that there was no major difference in measurements between
experiments conducted in water and the xanthan gum mixtures, except
that the added xanthan gum yielded higher noise levels.
[0089] The xanthan gum gel preparation was performed as follows:
2.6 grams BT food grade xanthan gum powder, 12 g glycol, and 600 g
water (slightly degassed) were combined. First, the powder and
glycol were mixed and poured into a beaker, and the water was then
poured into the beaker very slowly over a stick to minimize the
trapping of bubbles. The mixture was stirred slowly for up to an
hour using a magnetic stir bar to make it homogeneous. The gel was
finally poured slowly into the experimentation vessel. Because of
the possibilities of contamination and bacterial growth, a new gel
was made prior to the start of each experiment. If more viscous
gels are used, removing trapped bubbles becomes much more difficult
and requires centrifuging the solution for up to 3 hours.
[0090] In the empirical study, a 30 mW HeNe laser was employed as
the light source to illuminate the microbubbles. With a lens, the
beam waist at the region of interest (i.e., where the microbubble,
the laser beam, and the ultrasound intersect) was focused to less
than 100 .mu.m (although, because some scattering occurs through
the plastic water tank and through the water, it is difficult to
accurately measure the beam waist). The light scattered from the
bubble was then focused with a 5 cm lens onto a PMT detector
(Hamamatsu, Model 2027.TM.). The main function of the collecting
lens was to increase the signal/noise (covering the
angles)70.degree.-90.degree.. The PMT was biased at 21000 V. A HeNe
line filter was placed against the PMT cathode window to block
other sources of light. The output of the PMT was conveyed directly
to a high-speed digital oscilloscope (LeCroy), and then to a
personal computer for post-processing. As noted above, the varying
pressure conditions were supplied using an imaging ultrasound probe
(placed directly in the fluid vessel, although an externally
disposed transducer can also be employed, so long as the fluid
vessel wall is acoustically transparent).
[0091] Data collection was performed in a sequence mode, where
high-resolution data files are collected during each ultrasound
pulse. The total data collected are limited by the available memory
of the oscilloscope. For the empirical study, data sequence records
of 40 consecutive acoustic pulses were collected before
transferring the file to the computing device. Each segment
included a 5 ms long window, with a resolution of 4 ns. The
segments were separated by about 1 ms (triggered by the source
transducer). Appropriate delays in triggering were used to ensure
that the bubble response was centered in the segment window.
[0092] The imaging ultrasound probe (the Ultramark 4Plus.TM.) was
operated in the M-Mode at about 1 kHz pulse repetition frequency
(PRF). A calibrated needle hydrophone monitored the acoustic
pressure. In actual experiments, the hydrophone was placed at an
angle relative to the pulse. Thus, the relative angle between the
transducer and hydrophone had to be measured, and then a separate
water tank was used to determine the hydrophone response as a
function of the angle of the ultrasound probe. This hydrophone
response as a function of the angle, expressed as a multiplicative
factor, was then used in all subsequent data analyses.
[0093] Other empirical studies employed a single element high
intensity transducer, which was inserted through the bottom of the
vessel, with the hydrophone being positioned directly above it, so
that the angle problem described above was not an issue. For this
configuration, relevant transducer parameters are center
frequencyf=1.8 MHz, focal length=63 mm, -6 dB for a bandwidth=500
kHz, 2.5 cm active area, 10 cycle bursts, and 10 kHz PRF.
[0094] FIGS. 5A-5B, 6A-6B, and 7A-7B graphically illustrate data
and model fits for Optison.TM. and Sonazoid.TM. UCAs, in water and
diluted aqueous xanthan gum gel. There are two important points to
note. First, the light scattering model and data both produce an
intensity versus time I(t) that must be converted to a radius
versus time R(t). For the experimental data, the scattered
intensity is found by subtracting the background intensity from the
total intensity. The model generates a relative value, so a
multiplicative scaling factor must be found to match the model to
the data. Once the scaling factor is found, it is unchanged for all
subsequent experiments. The relationship described above for FIG.
3B can be used to convert the relative intensity to a radius. A
second point to note is that best fit studies were required in
order to constrain the two unknowns (R.sub.0 and the product
.epsilon..mu..sub.sh). As described above, the expected ranges for
the parameters were defined. Within these ranges, it was determined
that a deviation of about 65% in R.sub.0 would generate a good fit.
In addition, it was determined that shell parameter values
published in the literature would enable good fits to be achieved.
Therefore, for these studies the initial shell parameter for
Optison.TM. was defined as .epsilon..mu..sub.sh=6.0 nm Pa s, and
for Sonazoid.TM., the initial shell parameter was defined as
.epsilon..mu..sub.sh=2.0 nm Pa s. Deviations of up to about +/-1 nm
Pa s also generated good fits. It was determined that UCA
oscillations from pulse to pulse were relatively regular, so
several pulses were grouped together to improve the SNR.
[0095] FIG. 5A graphically illustrates an RT curve generated using
an Optison.TM. UCA pulsed with the Ultramark 4Plus.TM. in water,
while FIG. 58 graphically illustrates an RT curve generated using
an Optison.TM. UCA pulsed with the Ultramark 4Plus.TM. in the
water/xanthan gum solution. For each RT, ten consecutive pulses
(segments) were averaged together to increase the SNR. The measured
peak negative pressure and fitted initial bubble radius are 210 kPa
and 1.47 .mu.m, respectively. For these parameters, the data and
simulations exhibit quasi-linear motion. For these and other data,
very good agreement with the major oscillations is obtained. The
smaller ring-down oscillations are more difficult to fit because of
the lower signal strength. Although the Morgan model discussed
above is arguably not the most accurate model to use for
encapsulated microbubbles, the overall good fit to the data
suggests that the empirical data is indeed based on measuring the
pulsations of individual encapsulated microbubbles. To provide
verification that the light scattering was being performed on
single bubbles, and/or that the bubbles were shelled, the
experiments were repeated in the diluted aqueous/xanthan gum gel
mixture. As noted above, the diluted gel mixture was sufficiently
viscous to enable a UCA to be manually injected into the region of
interest. The UCA could then be examined under a microscope to
ensure that the agent in the region of interest was an individual
microbubble (as opposed to a mass of microbubbles). Due to the
viscosity of the solution, each UCA would remain in the region of
interest for several minutes, indicating that the bubbles were
shelled and stable. As can be seen in FIGS. 5A and 5B, diluted
xanthan gum gel did not affect the dynamics adversely, and the fit
is remarkably good. The measured peak negative pressure and fitted
initial bubble radius, R.sub.0, are 340 kPa and 1.5 .mu.m,
respectively. As with the previous data, these bubbles also exhibit
nearly linear oscillations.
[0096] Sample response curves from a single element transducer
(i.e., a transducer configured for therapeutic ultrasound rather
than for imaging ultrasound) are shown in FIGS. 6A, 6B, 7A, and 7B.
These experiments were performed both in water and aqueous xanthan
gum gel mixtures. In FIG. 6A, (Optison.TM.), data from a ten-cycle
tone burst were averaged over 37 pulses. In FIG. 6B, (Optison.TM.),
data from a ten-cycle tone burst were averaged over 40 pulses. In
FIG. 7A, (Sonazoid.TM.), data from a ten-cycle tone burst were
averaged over 5 pulses. In FIG. 7B, (Sonazoid.TM.), data from a
ten-cycle tone burst were averaged over 40 pulses. The measured
peak negative pressures and fitted ambient bubble sizes are
summarized in Table I (FIG. 8). The relatively poor SNR in FIG. 6A
is likely due to the bubble not being in the center of the laser
beam. Non-linear bubble oscillations were especially evident in
FIGS. 6B and 7B, presumably due to the increased pressure
amplitudes. Although these longer tone bursts are not very relevant
to imaging applications (which utilize short diagnostic pulses),
variable pulse lengths can be used to explore issues such as shell
fatigue and microbubble stability.
[0097] The fitted ambient sizes (from Table I in FIG. 8) are
consistent with known UCA bubble sizes. Based on FIGS. 5A-7B, it
can be concluded that the good fit of the Morgan model to the
experimental data, both in water and diluted gel, is evidence that
light scattering can be beneficially employed in measuring
individual UCA dynamics. Further experiments indicated that these
techniques can also be applied to UCA clusters (i.e., masses of
microbubbles), not just individual bubbles.
[0098] One of the advantages of the light scattering technique
discussed above is its ability to make high temporal resolution
measurements over long time scales. The following results are based
on observations of UCA microbubbles subjected to consecutive pulses
from the Ultrannark 4Plus.TM.. For slowly evolving microbubbles,
the data were combined data for groups of ten pulses, while for
quickly evolving microbubbles, the data were examined for each
individual pulse.
[0099] When fitting the evolution data to the Morgan dynamic model,
there is always the question of which of the two unknown parameters
(R.sub.0 and the product .epsilon..mu..sub.sh) to change in order
to obtain a good fit. Because the shell data that are collected
might be compromised (e.g., from dislodging, or crumpling, or due
to changes in permeability), varying the shell parameter (product
.epsilon..mu..sub.sh) was preferred.
Slowly Evolving Agents:
[0100] To follow the slow evolution of UCAs, the pressure amplitude
employed was approximately 130 kPa. This pressure amplitude is
lower than the fragmentation thresholds found in the literature for
the UCAs utilized. The studies providing the thresholds were
looking at relatively fast destruction mechanisms, not slow decay
mechanisms. A more relevant comparison is likely to be the slow
decay of backscattered signals for UCAs subjected to clinical
ultrasound.
[0101] FIGS. 9A-9C graphically illustrate the slow evolution of a
Sonazoid.TM. microbubble in water, collected in three successive
groups of ten pulses. Significantly, a good fit was obtained
without having to change the shell parameter; it was kept constant
at 2.0 nm Pa s. However, R.sub.0 was increased between groups (from
1.2 to 1.9 nm; see Table II in FIG. 10). That is, the Sonazoid.TM.
microbubble appears to be growing with successive pulses. This slow
growth phenomenon was not observed with Optison.TM. bubbles. Two
physical interpretations can be made. First, the lipid shell may
have been partially compromised before the experiment began.
Although possible, this trend has been observed from many different
datasets. Second, during expansion or compression, the lipid shell
may alternatively become semi-permeable. If it is assumed that the
bubble is filled initially with perfluorobutane (PFB) and the water
contains air; then because of the higher diffusivity of air,
diffusion of air into the bubble will occur at a faster rate than
diffusion of PFB out of the bubble. Thus, at least initially, the
bubble can grow. Again, it is emphasized that FIGS. 9A-9C show
successive segments in one sequence of data. At about 1 ms between
segments (equal to the burst PRF), the total elapsed time is about
30 ms.
[0102] A summary of the parameters for FIGS. 9A-9C is provided in
Table II (FIG. 10). Previously reported data indicates that
Sonazoid.TM. microbubbles dissolve after each pulse; however, such
data were generated using about twice the pressure amplitude. It is
likely that at those higher pressures, gas may be forced through
the shell during compression.
Quickly Evolving Agents:
[0103] For this study, the pressure amplitude was increased to 340
kPa for Optison.TM. and 390 kPa for Sonazoid.TM.. Previous studies
report that the decay rate of the backscattered signal for
Optison.TM. increased at these higher pressures, and that
Sonazoid.TM. also showed a decay, although at a slower rate. Other
studies indicate that these pressures are above the fragmentation
threshold.
[0104] FIGS. 11A-11D graphically illustrate Optison.TM. response
curves (i.e., RT curves) for individual (i.e., non-averaged)
pressure pulses from the Ultramark 4Plus.TM. in the diluted
aqueous/xanthan gum solution/gel. In FIGS. 11A-11C, the microbubble
response comes from consecutive pulses. A single pulse is skipped,
and then the data illustrated in FIG. 11D were collected. In terms
of pulses, the Figures illustrate the dynamical response from
pulses 1, 2, 3, and 5.
[0105] There appears to be a second series of oscillations
developing in FIGS. 11B-11D. These signals may be due to the
arrival of a second microbubble. Referring to the first major peak,
in FIG. 11A the Morgan model corresponds to the data rather well.
The fit is for a 1.5 .mu.m radius bubble, with a shell parameter of
6.0 nm Pa s. In FIG. 11B, the fit is still acceptable; however,
there are large amplitude "spikes" in the dataset. Such spikes were
often observed immediately before, or during microbubble
destruction, and may be related to a crumpling of the shell (shell
crumpling has been previously observed). In FIGS. 11C and 11D, the
model must be adjusted by decreasing the shell parameter (keeping
the radius fixed). That is, the shell of the microbubble from which
the scattered light was collected appears to be compromised. The
parameters for this dataset are summarized in Table III (FIG.
12).
[0106] FIGS. 13A-13F graphically illustrate RT curves for optical
scattering data collected from a Sonazoid.TM. bubble in water,
while the bubble was undergoing an evolution during consecutive
pulses (one pulse is not shown between the last two pulses, i.e., a
pulse was skipped between the RT curves of FIGS. 13E and 13F).
Referring to FIGS. 13A-13D, the shell parameter is fixed, but the
ambient bubble radius was increased from 0.8 .mu.m to 1.2 .mu.m to
maintain a good fit (i.e., to achieve the fit indicated by the
dashed line). The Sonazoid.TM. bubble appears to absorb air from
its surroundings before the shell is broken. By pulse number 5
(i.e., FIG. 13E), the shell is compromised. Also note the apparent
non-linearity of the bubble motion. The parameters for this data
set are summarized in Table IV (FIG. 14). To summarize the data
discussed above, at these modest pressures, Sonazoid.TM.
microbubbles appear to have a semi-permeable shell when insonified,
allowing air to be absorbed, and causing the bubble to grow. Both
Optison.TM. and Sonazoid.TM. UCAs appear to show damage to the
shell after two or more pulses. It should be noted that these
results are examples involving individual microbubbles. It would be
necessary to examine many such cases before a conclusion could be
drawn as to the "average" response of a particular microbubble.
Spectral Analysis:
[0107] Light scattering data may also be suited to fast analysis by
examining the spectrum of the signals. Toward this eventual goal,
the power spectral densities (PSD) of the R(t) curves discussed
above were examined. FIG. 15 graphically illustrates normalized
PSDs. There are significant fundamental peaks in the PSDs of FIG.
15, related to the (real) resonance frequency of the system.
Furthermore, apparent sub-harmonic and harmonic components were
often observed. A more thorough analysis of these signals may
eventually lead to better information about the response curves, or
R(t) that could be used to optimize the agents for imaging and
therapy.
[0108] The above empirical studies evaluated the feasibility of
using light scattering to measure the radial pulsations of
individual ultrasound contrast microbubbles (Optison.TM. or
Sonazoid.TM.) subjected to pulsed ultrasound. Experiments performed
in a highly diluted xanthan gum mixture were used to verify that
individual encapsulated microbubbles could be investigated. The
evolution of individual contrast microbubbles was observed over
several consecutive acoustic pulses, suggesting that shell
permeability and/or shell fatigue are important consequences in the
evolution of microbubbles. It appears that light scattering can be
used to better understand the physical interaction between
ultrasound pulses and contrast agents, and eventually be used to
evaluate shell parameters and explore shell fatigue, leading to
better agent design.
Summary of Initial Study of Optison.TM. and Sonazoid.TM.
Bubbles:
[0109] Scattered light was collected from single UCA bubbles while
the individual bubbles were oscillated with a diagnostic ultrasound
machine. The empirical data were fitted with the Morgan model with
good success. It was assumed that the thickness of the shell was
relatively constant for a range of bubble sizes. Based on the trial
fitting of the empirical data, it was determined that the shell
parameters .epsilon..mu..sub.sh=6 nm Pa s for Optison.TM. and
.epsilon..mu..sub.sh=2 nm Pa s for Sonazoid.TM. are acceptable.
Those parameters were then input in the Morgan model so that the
model was fitted to the empirical data with the initial value being
the only variable. The empirical data were filtered using a 10 MHz
low-pass filter. It was observed that the Morgan model correctly
described the UCA bubble's response to longer acoustic tone bursts
(i.e., therapeutic ultrasound) as well the bubble's response to
short pulses from a diagnostic ultrasound instrument. The empirical
data collected while oscillating UCAs to destruction indicate that
it usually takes some time or several cycles for ultrasound pulses
to disrupt the UCA bubbles. The destruction process appears to
include distortion of bubble shape, the generation of partial
defects or ruptures of the UCA shell, and an increase in the
magnitude of this distortion, with the expansion of the UCA shell
followed by the complete rupture of the UCA shell, yielding a free
gas bubble. It is likely that shell fragments may still affect the
nearby acoustic field and scattering field. It was observed that
the damping characteristic of a UCA shell contributes to the
resonance frequency shift to a lower frequency. When a UCA bubble
is broken, the resonance frequency of the bubble is observed to
increase (based on spectral analysis of the data).
Different Shelled UCA Models:
[0110] As noted above, many different dynamic models have been
developed to describe the motion of microbubbles or spheres. A
significant aspect of the light scattering technique disclosed
herein is that the collected data (i.e., the RT curves) can be
fitted to many different models. The number of variables being
fitted can be minimized by acquiring data corresponding to as many
of the model variables as possible. As discussed above, ambient
pressure can be measured using a hydrophone while the scattered
light is collected, eliminating pressure as a variable. The initial
radius of a microbubble can be measured optically (i.e., using a
microscope and a camera), or literature-based values can be used
for the initial radius, eliminating yet another variable.
Preferably, the only unknown variables involved in the fitting
process relate to shell parameters, which to date, have been
difficult to empirically measure. The following discussion is
related to additional models.
[0111] The de Jong's model, Church's model, Hoff s model, and
Sarkar's model are each based on the general RPNNP equation (i.e.,
Eq. (1)), which as noted above, describes the response of a
spherical bubble to a time-varying pressure field in an
incompressible liquid.
[0112] The assumptions for the RPNNP equation are: (1) the motion
of the bubble is symmetric; (2) the wavelength of ultrasound is
much larger than the bubble radius; (3) no rectified diffusion
occurs; and, (4) the bubble contains gas or vapor, which is
compressed and behaves according to the gas law, with the
polytropic parameter held constant.
de Jong's Model:
[0113] De Jong modified the RPNNP equation to account for shell
friction (.delta..sub.f, included in .delta..sub.tot) and
elasticity (S.sub.p) parameters as follows:
.rho. L R R + 3 2 .rho. L R . 2 = P g ( R 0 R ) 3 .kappa.1 + P v -
P 0 - 2 .sigma. R - 2 S p ( 1 R 0 - 1 R ) - .delta. tot
.omega..rho. L R R . - P a cos ( .omega. t ) ( 4 ) ##EQU00004##
where S.sub.p=6G.sub.sd.sub.se(R/R.sub.0).sup.3, and G.sub.s is the
shell shear modulus, and d.sub.se is the shell thickness. The total
damping parameter is given by:
.delta..sub.tot=.delta..sub.th+.delta..sub.R+.delta..sub..eta.+.delta..s-
ub.f (5)
and thermal damping constant is given by:
.delta. th = 1 .omega. 0 .omega. P 0 .rho. L a 2 Im ( 1 .PHI. ) ( 6
) ##EQU00005##
[0114] The formula of .PHI. is adapted from Devin. The radiation
resistance damping constant is given by:
.delta. R = .omega. 2 a .omega. 0 c ( 7 ) ##EQU00006##
and the viscosity damping constant is given by:
.delta. .eta. = 4 .eta. L .omega. 0 .rho. L R 2 ( 8 )
##EQU00007##
where .eta..sub.L, is the liquid shear viscosity. The shell
friction parameter is:
.delta. f = 12 .eta. s d se .omega. 0 .rho. L R 3 9 )
##EQU00008##
where .eta..sub.s is the shell shear viscosity. The polytrophic
exponent is:
.kappa. 1 = Re [ 1 .PHI. ( R , .omega. ) ] ( 10 ) ##EQU00009##
Church's Model:
[0115] In Church's work, a Rayleigh-Plesset-like equation
describing the dynamics of shelled gas bubbles was derived. It was
assumed that a continuous layer of incompressible, solid elastic
shell with damping separates the gas bubble from the bulk Newtonian
liquid. The elastic surface layer stabilizes the bubble against
dissolution by supporting a strain that counters the Laplace
pressure. Viscous damping is considered in this model, which is as
follows:
.rho. s R 1 R 1 [ 1 + ( .rho. L - .rho. s .rho. s ) R 1 R 2 ] +
.rho. s R . 1 2 { 3 2 + ( .rho. L - .rho. s .rho. s ) [ 4 R 2 3 - R
1 3 2 R 2 3 ] R 1 R 2 } = P G , eq ( R 01 R 1 ) 3 .gamma. - P
.infin. ( t ) - 2 .sigma. 1 R 1 - 2 .sigma. 2 R 2 - 4 R . 1 R 1 [ V
s .eta. s + R 1 3 .eta. L R 2 3 ] - 4 V s G s R 2 3 ( 1 - R e 1 R 1
) ( 11 ) ##EQU00010##
where .rho..sub.s is the shell density, .sigma..sub.1 is the
surface tension of the gas-shell interface, .sigma..sub.2 is the
surface tension of the shell-liquid interface, P.sub.G,eq=P.sub.0
for the surface layer permeable to gas, and:
P .infin. ( t ) = P 0 - P a sin ( .omega. t ) ( 12 ) V s = R 02 3 -
R 01 3 ( 13 ) R e 1 = R 01 [ 1 + ( 2 .sigma. 1 R 01 - 2 .sigma. 2 R
02 ) R 01 3 V s 4 G s ] ( 14 ) ##EQU00011##
Hoff's Model:
[0116] A simplified equation was derived from Church's equation by
Hoff, for the case of thin shell, d.sub.se(t)<<R.sub.2:
.rho. L R R + 3 2 .rho. L R . 2 P 0 [ ( R 0 R ) 3 .gamma. - 1 ] - 4
.eta. L R . R - 12 .eta. s d se R 0 2 R 3 R . R - 12 G s d se R 0 2
R 3 ( 1 - R 0 R ) - P i ( t ) ( 15 ) ##EQU00012##
Sarkar's Model:
[0117] Chatterjee and Sarkar developed a new model for encapsulated
contrast agent microbubbles, as follows:
P L ( R R + 3 2 R . 2 ) = ( P 0 + 2 .sigma. i R 0 ) ( R 0 R ) 3
.gamma. - 4 .eta. L R . R - 2 .sigma. i R 0 - 4 K s R . R 2 - [ P 0
+ P drive ( t ) ] ( 16 ) ##EQU00013##
This model assumes the encapsulation of a contrast agent to be an
interface of infinitesimal thickness with complex interface
rheological properties. The interfacial tension, .sigma..sub.i, and
dilatational viscosity .kappa..sup.s are unknown interface and
shell parameters.
Marmottant's Model:
[0118] Most shelled UCA models assume constant surface tension
coefficients and small deformations of the microbubble surface.
However, for phospholipid monolayer coatings, the surface area
available per phospholipid molecule apparently varies as the
microbubble oscillates. Thus, Marmottant derived an improved model
(Eq. 6) specifically for microbubbles with lipid monolayer
coatings. The model considers the microbubble shell as a
two-dimensional viscoelastic medium and suggests that the shell
elasticity can be modeled with a radius-dependent surface tension.
There are two parameters introduced to model the shell properties:
the shell elastic compression modulus .chi., and a shell
dilatational viscosity k.sub.s.
.rho. L ( R R + 3 2 R . 2 ) = ( P 0 + 2 .sigma. i R 0 ) ( R 0 R ) 3
.gamma. ( 1 - 3 .gamma. c R . ) - 2 .sigma. w R - 4 .chi. ( 1 R 0 -
1 R ) - 4 .eta. L R . R - 4 .kappa. s R . R 2 - P 0 - P drive ( t )
( 17 ) ##EQU00014##
[0119] Marmottant's model (i.e., Eq. (17) has been applied very
successfully to the following UCAs: SonoVue.RTM. and BR14.TM.
(Bracco Diagnostics).
[0120] Additional light scattering empirical studies were performed
measure the dynamic response of individual Sonovue.TM. bubbles to
the driving acoustic pulse using a system 40 schematically
illustrated in FIG. 16. Note that the system of FIG. 16 is based on
the system of FIG. 2, and includes an optional microscope 42, an
optional CCD camera 44, and an optional monitor 46 to enable the
radius of the microbubble to be empirically measured, generally as
discussed above. In brief, the highly diluted Sonovue.TM.
suspensions were injected into the region of interest (ROI defined
herein as being a small volume 52 where the ultrasound and laser
beams intersected with Sonovue.TM. bubbles) using a syringe pump 68
(e.g., a 74900.TM. series, Cole-Fanner Instrument Co., Vernon
Hills, Ill., USA) at a rate of 10 ml/h with a tube (0.5 mm
inner-diameter). The driving acoustic pulses were sent from a probe
of a diagnostic ultrasound instrument 58 (e.g., an Ultramark
4Plus.TM., ATL-Philips, USA) operated in M-Mode at 1-kHz
pulse-repetition-frequency (PRF) and monitored using a calibrated
needle hydrophone 60 (e.g., from NTR Systems Inc., Seattle, Wash.,
USA). An HeNe laser 54 (Melles Griot, Carlsbad, Calif., USA) was
used as a light source. The waist of the laser beam was focused to
less than 100 .mu.m at the ROI by a lens (not shown). The scattered
light signals from the microbubbles in the ROI were collected and
focused by another lens 66 onto a photo-multiplier tube (PMT)
detector 56 (e.g., a Hamamatsu, Model 2027.TM.). The output signals
from the PMT and the hydrophone were recorded using a high-speed
digital oscilloscope 64 (e.g., from LeCroy, Chestnut Ridge, N.Y.,
USA) in sequence mode provided by a function generator 70, and then
transferred to a computer 62 waiting from post-processing using a
MatLab program (Mathworks Inc., Natick, Wash., USA). Optionally, a
pulse generator 72 can be triggered by the function generator to
produce a pulse signal applied to a phase adjuster 74, to produce
light pulses with an LED 76 that are focused by a lens 78 into
volume 52.
Results and Discussion:
[0121] Four of the models discussed above (de Jong's model,
Church's model, Hoff's model and Sarkar's model) were "run" with
the same modified Gaussian pulse,
P.sub.div=P.sub.0 sin
[2.pi.f(t-t.sub.c)]exp[-.pi..sup.2h.sup.2f.sup.2(t-t.sub.c).sup.2]
(18)
with t.sub.c=5 .mu.s and h=1/3. The results indicate that each
model appears to provide substantially similar results in a certain
parameter range. FIGS. 17A and 17B graphically illustrate the
response of a 1.5 .mu.m radius bubble subject to a 2.5 MHz modified
Gaussian pulse with a peak negative pressure of 0.2 MPa. FIG. 17A
illustrates the modified Gaussian pulse is used to simulate the
driving signal, while FIG. 17B illustrates typical bubble responses
using the four above noted dynamic bubble models. The parameters
used for the simulation are given below. [0122]
.rho..sub.L=10.sup.3 kg/m, density of a Newtonian liquid [0123]
P.sub.0=101300 Pa, ambient pressure [0124] P.sub.b=2330 Pa, vapor
pressure (Chang et al, 1999) [0125] .sigma.=0.07275 N/m, surface
tension [0126] .rho..sub.g=1.161 kg/m.sup.3, gas density [0127]
C.sub.p=240.67, heat capacity at constant [0128] K.sub.g=0.00626,
thermal conductivity (for air at 300K and 1 atm) [0129] c=1500 m/s,
acoustic velocity [0130] .gamma.=1, gas adiabatic constant [0131]
.eta..sub.L=0.001 Pa.times.s, liquid shear viscosity (Church et al,
1994) [0132] .rho..sub.s=1100 Kg/m.sup.3, shell density (Church et
al, 1994) [0133] .rho..sub.1=0.04 N/m, surface tension of the
gas-shell interface (Church et al, 1994) [0134] .rho..sub.2=0.005
N/m, surface tension of the shell-liquid interface (Ibid.)
[0135] As noted above, and as illustrated in FIG. 17B, each model
appears to provide substantially similar results within a certain
parameter range. However, if the selected shell parameters (e.g.,
shell viscosity .eta..sub.s and shell shear modulus Gs), are out of
a certain range, these models will likely produce different
responses. Since the same shell parameters are used in the de Jong,
Church, and Hoff models, the studies here are focused on these
three models. FIGS. 18A-18C graphically illustrate results provided
by these three models with varying parameters. Each of the three
models provides substantially the same result with appropriately
selected parameters (FIG. 18A), whereas the simulation results
become different from each other with increasing Gs (e.g., Gs>50
Mpa; FIG. 18C) or decreasing .eta..sub.s (e.g., .eta..sub.s<0.1
Pa*s; FIG. 18B). Additional non-linear behaviors can be observed
with the changed shell parameters, which suggests that each of the
three models might have similar linear responses, while their
non-linear responses differ. Therefore, although the acoustic
driving parameters are controllable, it is still difficult to tell
which model is `better` without knowing the shell parameters a
priori. Further studies on UCA shell parameters are important and
necessary to make it possible to rank the various models.
[0136] Although as noted above different models can give similar
simulations results with appropriately selected parameters, to
verify the accuracy of the these models (i.e., de Jong's model,
Church's model, Hoff's model, and Sarkar's model) the
experimentally measured Sonovue.TM. bubble RT curve can be fitted
to each of the four models with selected fitting parameters.
Literature reports that Sonovue.TM. bubbles have a very thin lipid
shell whose thickness is assumed to be 4 nm. Three unknown fitting
parameters were chosen for present work: R.sub.0, Gs, and
.eta..sub.s, in de Jong, Church, and Hoff's models, and R.sub.0,
.sigma..sub.i, and .kappa..sup.s in Sarkar's model. Minimum
standard deviation evaluation is applied to determine the best
fitting.
[0137] FIGS. 19A and 19B graphically illustrate a comparison
between the experimental data and simulated results, with the
Sonovue.TM. bubble oscillating with a driving pressure amplitude of
about 0.15 MPa. The results indicate that Sonovue.TM. bubbles
behave in a strongly non-linear motion. The likely explanation for
this observation is that the lipid shells of Sonovue.TM. bubbles
are very thin and relatively permeable. Therefore, the properties
of the Sonovue.TM. bubble shell likely change when the bubble is
driven by acoustic pulses, which induces the observed non-linear
behavior.
[0138] FIG. 19B graphically illustrates that the empirically
measured scatter light RT curve for a Sonovue.TM. bubble can be
fitted reasonably well to each of the four models. The fitted
initial radius for each model converts to 1.54 .mu.m, which agrees
with the manufacturer's data. However, at the later stages of the
driving pulse, the fitting results can not follow the measured
non-linear response, which suggests that a better model is needed
in order to satisfactorily account for the non-linear response of a
Sonovue.TM. bubble. The Sonovue.TM. bubble's non-linear behavior
might result from the change of bubble shell parameters during its
oscillation. If it is assumed that the fitting results for
experimental data are acceptable, the relationship between
Sonovue.TM. shell parameters, e.g., shear modulus Gs and shear
viscosity .eta..sub.s, and bubble initial radius (R.sub.0), can be
obtained by fitting the pooled experimental data with the selected
numerical model. Since all four of these models yield similar
simulation results for the experimental data (as shown in FIG.
18B), it is reasonable to select any one of the four models to
quantify the bubble shell parameters. In this study, Hoff's model
was ultimately selected, since it is based on a thin-shell
assumption. In order to simplify the computational process, the
lipid shell thickness for the Sonovue.TM. bubble was assumed to be
a constant value of 4 nm, as reported in the literature.
[0139] FIGS. 20A and 20B graphically illustrate that both shell
shear modulus and shear viscosity increase with increasing initial
radius, which implies the shell properties of UCA bubbles are not
homogeneous, and may be related the bubble size. FIG. 20A
graphically illustrates the change of the shell shear modulus as a
function of radius, while FIG. 20B graphically illustrates the
change of the shell viscosity as a function of radius. These
results suggest that UCA shell properties will significantly affect
bubble dynamic behaviors. However, considering the lack of
effective methods to measure the shell properties directly, further
efforts on the study of UCA shell parameters using the light
scattering techniques disclosed herein are imperative for improving
UCA development and applications.
[0140] The results of further studies involving three of the models
(Marmottant, Sarkar, and Hoff) are summarized in Table IV (below).
The results suggest that all three models perform equally well in
describing the experimental data in the central region, while all
models show deviations from the experimental data at the beginning
and end stages. The minimum STD values are similar for all three
sets of shell parameters. The relative equality between the models
suggests that it would be difficult to rank the models without a
priori knowledge of the shell parameters.
TABLE-US-00001 TABLE IV Best fit shell parameters and minimum STD
UCA Model Shell Elasticity Shell Viscosity Minimum STD Marmottant
shell elasticity dilatational viscosity 0.054 .chi. = 0.25 N/m
k.sub.s = 4 nm Pa s Sarkar interfacial tension dilatational
viscosity 0.054 .sigma..sub.i = 0.32 N/m k.sub.s = 4 mn Pa s Hoff
Shear Modulus Shear viscosity 0.059 G.sub.s = 23 MPa .eta..sub.s =
0.5 Pa s
Measuring Multiple UCA Bubble Dynamics Using Light Scattering:
[0141] The single Optison.TM. and Sonazoid.TM. studies discussed
above prove the value of light scattering in studying the dynamics
of single bubbles. Additional empirical studies were performed to
study a group of UCA bubbles using scattered light. Such group
dynamics are important, as in clinical conditions, masses of UCAs
(as opposed to individual bubbles) are employed. Such research has
indicated that at a relatively low driving power, UCA bubbles are
observed responding to the acoustic driving wave and oscillate. At
relatively higher driving powers, the destruction of UCA bubbles is
observed (as expected). Significantly, the harmonic response of UCA
bubbles can be observed at varied driving powers.
[0142] When studying multiple UCA bubbles, the analysis is more
complex, concerning both optics and acoustics. Statistically
speaking, the properties of UCA bubbles can be estimated by its
distribution profile, provided by the manufacturers. The profiles
can be described with a known statistics algorithm, such as
Gaussian distribution, to make it simpler to model distribution of
UCA bubbles, and therefore analyze the statistical characteristics
of UCA bubbles. Once the statistics package is determined, the
properties, such as mean and variation, can be applied to the model
to perform simulation. UCA bubbles are so small that there are
about half a billion of them in a single milliliter. For
Optison.TM., there are 5.times.10.sup.8-8.times.10.sup.8 individual
bubbles per milliliter. Since so many bubbles are involved, it is
difficult to know the number of bubbles in the region of
interest.
[0143] From an acoustical standpoint, the measured acoustic
pressure may not correctly describe the actual acoustic field that
activates the UCA bubbles, since the UCA cloud alters the acoustic
driving field. Further, the driving pressure is attenuated inside
the UCA cloud. This obvious impact is indeed observed in the
measurement of acoustic pressure in the field. However, the
acoustic measurement is necessary to monitor the pressure level
outside the targeted UCA cloud. The pressure signals also trigger
data collecting events, which means that the pressure measurement
cannot be used to describe the pressure on each individual UCA
bubble for modeling and data fitting, as it was in the single UCA
studies described above.
[0144] From an optical standpoint, the laser beam is affected
similarly, in that a mass of bubbles scatters light differently
than an individual bubble. As a whole, the UCA bubbles in the path
of the laser beam are not homogeneously distributed. The laser beam
itself is not homogeneous either, having a transverse intensity
distribution. This does not impact individual UCA bubbles; however,
the inhomogeneous cross-distribution of the laser beam means that a
UCA bubble at the center of the laser beam encounters more light
than a UCA bubble near the edge of the laser beam.
[0145] To address these issues, the RT curves discussed above are
modified, to achieve an effective RT curve. The effective RT curves
are computed from the light scattering data, based on the
assumption that each UCA bubble is separated far enough from its
neighboring bubbles such that there is no attenuation to the
incident light intensity on each UCA bubble. It is also assumed
that the laser beam is homogeneous, which means each UCA bubble
scatters laser light as if there is only one UCA bubble in the
region. The effective scattered light intensity of the collected
data combines the contributions from every bubble.
[0146] The multiple bubble study employed an HDI 5000.TM.
ultrasound system, which is able to operate in many modalities,
including B-Mode, M-Mode and Pulse-Doppler Mode, each of which was
used in the multiple bubble study. The HDI 5000.TM. system (probe)
functions as an acoustic source; and, each modality features a
different pulse length and central frequency. Every modality can
provide either low or high power. The intact UCA bubbles' response
to acoustic driving pulses, and the destruction of the UCA bubbles,
are of great interest in revealing the UCA bubbles' properties.
Since today's diagnostic ultrasound systems perform harmonic
imaging with UCA bubbles mainly in a B-Mode at extremely low power,
much of the data collected in the multiple bubble study were
obtained using the B-Mode at a low driving power (MI).
[0147] FIG. 21A graphically illustrates a typical effective RT
curve of a mass of UCA bubbles (i.e., the effective RT curve of UCA
bubbles in B-Mode at MI=0.04), and FIG. 21B graphically illustrates
the power spectrum of the UCA bubbles' response. These Figures
provide an understanding of how the light scattering data were
processed. The data utilized in these Figures were collected in the
B-Mode with MI=0.04, where the fundamental frequency in B-Mode is
about 2 MHz. The effective RT curve was generated from a light
scattering signal that monitors the dynamic oscillation of the UCA
bubbles. In FIG. 21B, the power spectrum of the UCA bubbles'
dynamics is lower than 2 MHz. This shift of fundamental frequency
between the bubbles' response and that of the driving pressure is
discussed below. The main focus of the multiple bubble study was on
the spectrum properties of UCA bubbles' response. However, the
destruction properties of UCA bubbles are of interest as well. In
the following discussion, where the focus is on spectral analysis,
the RT curves may not be provided.
[0148] It is recognized that the UCA cloud will scatter some of the
incident acoustic beam, which will result in the attenuation of the
acoustic pressure on the UCA bubbles in the path of acoustic beam.
Thus, the UCA bubbles are not homogeneously driven, which further
complicates the analysis of the collected data. Because the region
of interest upon which the PMT is focused is very small, it is
assumed that all the UCA bubbles are homogeneously activated. Note
that as indicated in FIG. 21A, the effective expansion of the UCA
bubbles is relatively small (i.e., the expansion is only about 5%
of their ambient sizes).
The B-Mode:
[0149] In the B-Mode, the driving power (MI) starts as low as 0.03
(the lowest HDI 5000.TM. power setting). FIG. 21C graphically
illustrates two examples (i.e., one a solid line, and one a dashed
line) of the power spectrum at MI=0.03. The harmonic response of
each example is slightly different, possibly due to the difference
of the specific local UCA bubbles. Significantly, as indicated by
each example, the UCA bubbles respond to the acoustic source, even
though the driving pulses are very weak. Further, the power
spectrum reveals strong fundamental components, while a harmonic
component is observed as well. The harmonic component can be
particularly significant, as the solid line indicates. When
compared with the driving pulse central frequency (2 MHz--not
shown), it is evident that the fundamental frequency of the UCA
bubbles' response is shifted relative to the frequency of the
driving pressure, as will be noted in FIG. 21B. It is also noted
that the response of the second group of UCA bubbles (i.e., the
second example, whose data are shown in the graph with a dash line)
indicates different spectral characteristics in the power spectrum.
That is, the feature of the fundamental component in the power
spectrum of the UCA bubbles' response is different.
[0150] FIG. 22 is a composite graph illustrating the power spectrum
of UCA bubbles driven by different acoustic power (MI) settings,
using the B-Mode. As indicated in the Figure, the fundamental
frequency shifts to a lower frequency when the driving power is
lower than MI=0.05, clearly, responding differently to different
acoustical driving powers. Because higher acoustic power settings
can destroy UCA bubbles, lower power settings are generally
preferred (unless UCA destruction is desirable, as in the case of
using microbubbles to deliver therapeutic agents encapsulated in
the bubbles). It should be noted that the data graphically
illustrated in FIG. 22 were generated from intact UCA bubbles
oscillating due to acoustic pulses.
[0151] FIG. 22 shows the power spectrum of UCA bubbles' response
with a driving power at MI=0.03, 0.03+, 0.04, 0.04+, 0.05, 0.05+,
0.06 0.07, and 0.11, respectively. The + sign refers to the middle
level of MI for the HDI 5000.TM. system, between two consecutive
defined MI settings. The selection of MI is based on the smallest
step of power increase starting from the lowest available value,
but the step is greater between the last two highest powers.
[0152] Referring to FIG. 22, it should be noted that the
fundamental frequency of the bubbles' response varies. When the
driving power is lower than MI=0.05, the fundamental component (at
about 2 MHz) of the bubbles' response shifts to a lower frequency.
When the driving power is 109 (equal to or greater than MI=0.05),
the fundamental frequency seems to match satisfactorily (though not
perfectly) the acoustic driving frequency. Further, the harmonic
frequency components vary based on driving power, which can be
observed in both low and high power drive settings. There seems no
great advantage to use higher driving power settings to generate
harmonic components. A second harmonic frequency shift (at about 4
MHz) basically follows the trend of the fundamental frequency. When
the fundamental frequency shifts toward a lower frequency, the
corresponding second harmonic frequency also shifts toward a lower
frequency. Finally, the sub-harmonic component (at about 1 MHz) can
be identified in most of the examples, but the power of
sub-harmonic component is very weak. In most of the examples, the
harmonic components at 3 MHz, 5 MHz and 7 MHz can be identified,
especially for examples employing a higher driving power. Overall,
the various examples illustrated in FIG. 22 do not suggest that the
generation of harmonic components in the response of the bubbles to
an acoustic driving is highly dependent on the power of operation
employed during the insonification, while the bubbles are
intact.
[0153] Further statistical analysis of the data to determine why
the fundamental frequency, as well as harmonic frequencies, of the
response of the bubbles shifts when a low driving power is employed
did not indicate any dependence of fundamental frequency of the
bubbles' response to the driving powers. A higher driving power
increases the chance of generating more (and stronger) harmonic
components, as well as sub-harmonic components, even though the
power of sub-harmonic components are usually much smaller than that
of harmonic components. The statistical analysis confirmed that
both the shift of harmonic frequencies (second harmonic and third
harmonic frequencies) and the shift in the fundamental frequency
are in the same direction (i.e., a shift to lower frequencies),
although the shifts in the harmonic frequencies are greater in
magnitude.
[0154] It appears that there is a pressure threshold (MI=0.05 in
the examples of FIG. 22) in the B-Mode, which impacts the UCA
bubbles' response. Because the frequency shift of UCA bubbles'
response does not relate to the driving power, when the driving
power is low, the cause of the frequency shift must come from the
UCA bubbles themselves. The frequency shift (to a lower frequency
at a low driving power) is likely due to a size distribution of the
UCA bubbles, since the majority of the UCA bubbles are in the range
between 1 .mu.m and 2 .mu.m in radius. When the driving pressure is
small, the UCA bubbles may oscillate with the driving wave, as well
as experience self-resonance. The coupling of the oscillations
could result in the change of spectral features. When the driving
pressure is sufficient, the forced oscillation overcomes the
self-oscillation, to emerge as a main contributor to the spectra,
resulting in the fundamental frequency resembling that of the
driving power.
[0155] Regardless of the spectral features, it is noteworthy that
at an MI as low as 0.03, the harmonic components in the responses
of UCA bubbles can still be generated. This finding indicates that
the harmonic component in the response of UCA bubbles can be
generated as long as the bubbles are forced to oscillate. However,
it is recognized that UCA bubbles will not oscillate strongly when
the driving power is low, and the signal level indeed could be
extremely low. Thus, when the driving power is low, the harmonic
components may not be distinguishable from noise.
[0156] FIGS. 23A and 23B are composites. The development of UCA
bubbles during consecutive insonification in one data sequence
(collection) is graphically illustrated as RT curves in FIG. 23A,
and as power spectrums in FIG. 23B. Each sub-figure in the
composite represents an averaged result over the duration of a
consecutive activation.
[0157] When multiple UCA bubbles are involved, if they are in close
proximity, they are likely interact with one another to some
degree. The data collected to generate FIGS. 23A and 23B were
collected with an MI of 0.05+. In the RT curves of FIG. 23A, the
effective radius of the UCA bubbles continues to increase during
insonification. The magnitude of the forced oscillation builds up
initially and then slows. It appears that none of the UCA bubbles
were burst during the activation, because there is no significant
sudden increase of effective radius that would indicate breeching
of the shell. In the corresponding power spectrums of FIG. 23B, the
fundamental frequency of responses of the UCA bubbles does not
change, which suggests that there is no significant physical change
to the bubbles in this area. Under this condition, the gain of
ambient effective radius of the UCA bubbles in the area suggests
that the increase in light scattering is primarily due to an
adjustment of the spatial distribution of bubbles in the area. If
the bubbles are closely packed, their expansion is limited by the
proximity of neighboring bubbles. As the UCA bubbles oscillate with
the acoustic wave, they also interact with one another as well, and
thereby alter the spatial distribution characteristics.
[0158] It can be noted from FIG. 23A that the effective radius of
the bubbles, after an acoustic pulse, is larger than before the
acoustic pulse, although the difference is not particularly
significant in these examples. FIG. 24 graphically illustrates data
averaged over 100 consecutive pulses. The ambient effective radius
before the acoustic pulses is about 9 .mu.m, and the ambient
effective radius after the acoustic pulses is about 9.5 .mu.m. The
data provide no indication that any bubbles are being ruptured,
which suggests that the acoustic driving power contributes to the
increase of the UCA bubbles' scattering capability. That there is
no bubble destruction, or physical change in the bubbles, suggests
that the change of the spatial distribution of the bubbles is the
key factor in the observed increase in the light scattering
capability of the UCA bubbles in the area.
[0159] It was also observed that the magnitude of the response of
the UCA bubbles falls gradually after segment 220 in FIG. 23A,
while these bubbles' ambient scattering capability is still
increasing. It is believed that the bubbles' scattering capability
is increasing with respect to both light and sound. When the UCA
bubbles spread out, they can oscillate more freely, with less
interaction, so that both ambient scattering capability and the
magnitude of oscillation increase. Meanwhile, the bubbles that
encounter the acoustic wave first are able to scatter more energy,
and bubbles that encounter the acoustic wave later are driven by
weaker acoustic pulses, which results in the decreasing magnitude
of oscillation. However, the ambient scattering capability is not
affected. This combined effect results in the phenomenon in the RT
curves of FIG. 23A that ambient scattering capability keeps
increasing, while the amplitude of oscillation first increases and
then falls.
[0160] The data set graphically illustrated in FIGS. 23A and 23B
shows the development of UCA bubbles during insonification at
extremely low driving power. It should be noted that UCA bubbles
can break even at very low acoustic power levels. As is known, when
gas bubbles are released (by the breaking of a shell) and are
driven by acoustic pulse, such gas bubbles can suddenly grow in
size tremendously. The data set graphically illustrated in FIGS.
25A and 25B relates to such a condition, where UCA bubbles start to
break and dissolve during insonification at low MI (=0.05). FIG.
25A includes RT curves of the same group of UCA bubbles during the
consecutive insonification, while FIG. 25B shows the corresponding
power spectrums. Four consecutive segments (UCA bubbles' response
to acoustic pulses) are shown in the Figures. In segments 260 and
262, the RT curves actually reveal a change in the dynamics of the
UCA bubbles, when compared with the RT curve of FIG. 20A. The
breaking of the UCA bubbles is clearly illustrated with the sudden
increase of effective radius in segments 261 and 263. The breakage
can also be visualized in the power spectrums of FIG. 25B. The
fundamental, harmonic, and sub-harmonic frequencies are clearly
seen in segments 260 and 262. The broad increases of the power
spectrum in segments 261 and 263 are symbolic of the sudden
increases of the effective radius and the breakups of UCA bubbles.
Even though the whole sequence is not shown here, the sequence data
indicates that additional bubbles are breaking during the
insonification. This data set also shows that UCA bubbles may break
in groups or individually at a low driving power, depending on the
actual input power. The fact that UCA bubbles break gradually at a
low MI is also proven in this example. Note that the ambient
effective radius does not change from segments 260 to segment 263,
which suggests that the number of UCA bubbles broken in segments
261 and 263 is not significant.
[0161] It has been shown that the harmonic frequency is generated
by UCA bubbles responding to acoustical pressure. If the second
harmonic component is sufficiently strong, the RT curve should
reflect this phenomenon, which is shown in FIGS. 26A and 26B, with
FIG. 26A corresponding to the RT curves, and FIG. 26B corresponding
to the power spectrum. The driving power for this data set was
MI=0.06. In the RT curve, the waveform of the second harmonic
component can clearly be observed, coupled with the fundamental
frequency waveform. In the power spectrum, the second harmonic
component is apparent. The power level of the second harmonic
component is comparable with that of the fundamental component.
Pulse-Doppler Mode:
[0162] A typical response of masses of UCA bubbles to the
Pulse-Doppler Mode is graphically illustrated as an effective RT
curve in FIG. 27. Pulse-Doppler Mode is different than the B-Mode,
in both its higher central frequency (about 2.4 MHz) and longer
pulse length. In the Pulse-Doppler Mode, the lowest available
acoustic driving power (MI) in the instrument employed in the
multiple bubbles testing is 0.04. The change of modality applies
acoustic pulses of different fundamental frequency and pulse length
to the UCA bubbles. FIG. 28 graphically illustrates the
corresponding power spectrum, enabling the harmonic responses of
UCA bubbles to be observed when stimulated in the Pulse-Doppler
Mode. The second harmonic power and even higher harmonic power
sometimes could be very strong. The detailed profile varies and may
reflect the specialty of the local UCA bubbles.
[0163] FIG. 28 includes three data sets, collected using a driving
power of MI=0.04 in the Pulse-Doppler Mode. The fundamental
frequency component profiles resemble one another in these
examples, while their details differ between data sets, as
discussed above with respect to FIG. 21 (B-Mode). The second
harmonic component (at about 5 MHz) can be strongly visualized at
MI=0.04; and even the third harmonic component (at about 6.5 MHz)
can be very significant. At a very low driving pressure, the
fundamental frequency of the response of the UCA bubbles does not
shift, as was observed in the B-Mode.
[0164] As noted above, UCA bubbles start to break or dissolve even
at particularly low driving powers. While the extremely strong
harmonic components FIG. 28 could possibly arise due to breaking
bubbles, because the effective radius (not shown) in the data sets
of FIG. 28 does not change before and after the acoustic pulses,
and because there is no sudden increase of effective radius during
the entire sequence, it appears that none of the UCA bubbles were
broken during the insonification of the data sets. Thus, it is
believed that the stronger harmonic components are due to intact
UCA bubbles. Some vulnerable UCA bubbles that could be destroyed
near a power at MI=0.04 might be driven extremely non-linearly to
generate strong a harmonic power, even though there is no bubble
that is destroyed.
[0165] FIG. 29 is a composite graphically illustrating the power
spectrum of multiple UCA bubbles being driven by different acoustic
power (MI) in the Pulse-Doppler Mode. In order to visualize the
impact of the variation in the power levels, FIG. 29 illustrates
the power spectrums of multiple UCA bubbles at the following
different MI: 0.04, 0.05, 0.06, 0.08, 0.09, and 0.10. The UCA
bubbles are intact in these examples. The following conclusions can
be made. First, it is obvious that the fundamental frequency and
second harmonic frequency components can be very strong. Again,
there is no frequency shift as was observed in B-Mode at a low
driving power. Second, sub-harmonic components can be identified
easily in some examples. Third, as in the B-Mode, the generation of
harmonic components in the responses of masses of UCA bubbles does
not depend on the driving power when the targeted bubbles are
intact.
M-Mode:
[0166] FIG. 30A graphically illustrates a typical response from a
mass of UCA bubbles to M-Mode stimulation (i.e., an effective RT
curve of UCA bubbles in M-Mode at MI=0.04), while FIG. 30B
graphically illustrates a corresponding power spectrum. The M-Mode
features a central frequency of 2.4 MHz, with a shorter pulse
length. In the M-Mode, only extremely high power (MI) is applied,
to focus on the destruction of UCA bubbles. FIG. 30C is a composite
image that graphically illustrates consecutive effective RT curves
of a mass of UCA bubbles responding to M-Mode stimulation. The
driving power employed to collect the data for FIG. 31 was MI=0.7.
In FIG. 30C, the first acoustic pulse (segment 1) destroys a
significant amount of UCA bubbles, and brings down the effective RT
from about 28 .mu.m, before the acoustic pulse, to about 20 .mu.m
just after. It was enlightening to note that a single pulse can
indeed destroy UCA bubbles. It is also observed that the UCA
bubbles oscillate with the driving pulse while they are breaking.
The second acoustic pulse (segment 2) causes more breakage, and
brings down the effective RT from about 21 .mu.m to about 18 .mu.m.
Even though the degree of destruction decreases, the third acoustic
pulse (segment 3) and the fourth acoustic pulse (segment 4)
continue breaking bubbles.
[0167] The data suggest that some UCA bubbles remain unbroken, even
at very high driving powers. This phenomenon can be observed in the
data for the fourth pulse (segment 4, FIG. 30C), and in subsequent
data (after segment 4) in the same data sequence, which is not
shown. Compared with the data corresponding to FIGS. 25A and 25B,
it is clear that higher acoustic pressure destroys UCA bubbles
faster.
[0168] The multiple UCA bubble testing discussed above indicates
that masses of UCA bubbles respond to acoustic waves, oscillate at
even very low acoustic pressures, and generate a harmonic signal.
The fundamental frequency of the response of masses of UCA bubbles
can shift from that of a driving wave when the driving power is
particularly low, which may reflect the characteristics of the
local UCA bubbles. It was shown that the higher driving power does
not provide an advantage with respect to generating harmonic
responses of masses of UCA bubbles, when the driven UCA bubbles are
intact. UCA bubbles can start to break at an extremely low driving
power, as is known based on clinical practice. Higher acoustic
driving levels will destroy UCA bubbles faster as expected, and
such levels can destroy most UCA bubbles in a single pulse.
Summary and Conclusions:
[0169] To date, UCA bubbles have been studied mainly using
acoustical methods. Significantly, in acoustical methods, the
acoustic driving source will increase the background noise in the
signal corresponding to the response of the UCA bubbles. An
intrinsic property of acoustic transducers is the band-pass
filtering of detected signals (the response of the UCA bubbles),
which causes the spectral characteristics outside the pass band to
be lost. To overcome these problems, the light scattering technique
discussed above has been developed. The light scattering technique
disclosed herein can be used to study the properties of individual
UCA bubbles, or masses of UCA bubbles, when such bubbles are driven
by acoustic pulses. Because UCA bubbles are so small, it is
difficult to use light scattering techniques in UCA research,
because the light scattering data collected are so noisy. Several
techniques can be used to reduce noise. One technique involves
focusing a laser beam to increase the incident light intensity,
changing the beam width from about 3 mm to about 0.2 mm in
diameter, which results in a 225-times increase in the incident
light power density. Another technique is to use a collecting lens
to cover a wide angle, and to collect more scattered light. The SNR
can also be increased using signal processing techniques in data
processing, including both averaging and filtering techniques.
[0170] The foundation of the light scattering technique is the Mie
scattering theory. Empirical data indicates that the Mie theory is
valid not only for homogeneous spheres, but also for coated
spheres, such as UCA bubbles. Empirical data have confirmed that
the thin-shelled UCA bubbles resemble homogeneous spheres in regard
to scattering light. This result facilitates the processing and
modeling of the light scattering data.
[0171] The empirical data discussed above with respect to single
bubble studies show that the light scattering technique is a
powerful tool for studying UCA bubbles, even though the SNR is
challenging. Overcoming the SNR issue using the techniques noted
above enables a response of UCA bubbles to different levels of
acoustic driving signals to be observed successfully. One or more
of the dynamic models discussed above can be used to fit the
empirical data to the model, enabling UCA parameters to be
calculated using the model. The empirical data demonstrated that
UCA bubbles respond to acoustic driving pulses, and that UCA
bubbles may undergo physical property changes. For example,
Sonazoid.TM. bubbles increase in size during insonification, while
other parameters, such as shell properties, remain unchanged. This
phenomenon was confirmed in the corresponding power spectra of the
response of the UCA bubble responses, where the fundamental
frequency of the response of the bubbles decreases during the
insonification. The increase in the UCA bubble's ambient radius
suggests that the thin-shelled UCA bubbles can exchange gas through
the shell membrane. They intake more gas from the surrounding
medium, resulting in bigger bubbles.
[0172] The single bubble study also illustrates that UCA bubbles
oscillate with driving pulses stably, even when the driving
strength is weak. When the pulse length of the acoustic driving is
longer, such as the examples with a single element transducer (HIFU
transducer), the UCA bubble's oscillation tends to be stable when
the acoustic driving pressure is stable. However, when the driving
strength is strong, UCA bubbles will eventually be destroyed. By
interpreting experimental data with the dynamic model, the
destruction of UCA bubbles is well illustrated. The data indicate
that the shells of UCA bubbles are distorted before the bubbles are
destroyed. The ratio of the maximum radius to the ambient radius of
UCA bubbles remains relatively constant when the UCA bubbles are
intact. A sudden increase in this ratio occurs when UCA bubbles
start to break up, and the ratio increases further afterwards. From
the power spectra of the response of the UCA bubbles, it can be
concluded that both harmonic and sub-harmonic components are
generated when acoustic pulses drive UCA bubbles. Sometimes, the
higher harmonic power is strong enough to be comparable with that
of fundamental and second harmonic components.
[0173] Additional studies directed to using scattered light from
masses of UCA bubbles employs an effective radius to account for
interaction among the mass of bubbles. The empirical data indicate
multiple UCA bubbles behave similarly to individual UCA bubbles,
while due to the spatial distribution of the bubbles, interaction
among the UCA bubbles and scattering of incident light and
ultrasound, variations between individual UCA bubbles are also
observed. Thus, the techniques disclosed herein can also be applied
to study masses of UCA bubbles.
[0174] The results from the multiple bubble study indicate that the
harmonic components of UCA bubbles' response can be generated at an
extremely low driving pressure. This finding indicates that
harmonic components can be generated whenever bubbles are forced to
oscillate. Indeed, the oscillation will be slight when the driving
pressure is weak. Therefore, the SNR becomes a critical factor at
relatively lower driving pressure levels. In some cases, higher
harmonic components, such as second, third, and even fourth
harmonic components, can be very significant, compared to the
fundamental components. The multiple bubble study also revealed
that the response of a group of UCA bubbles can be different at a
low driving pressure as compared with a higher driving pressure. In
the B-Mode, the fundamental frequency of the response of the mass
of UCA bubbles shifts to a lower frequency, when the driving power
is lower than MI=0.05, which indicates that the self-resonant
oscillation of UCA bubbles plays a role in this phenomenon. When
the oscillation due to the acoustic wave is not strong, the
self-resonant oscillation is comparable to the forced oscillation,
so that the power spectrum of the combined oscillation of UCA
bubbles is different than that of the acoustic driving pressure.
However, when the forced oscillation is strong, it dominates, and
the power spectrum of the response of the mass of UCA bubbles
resembles that of the driving pressure.
[0175] In practice, UCA bubbles are vulnerable, and are easy to
break, even at an extremely low pressure. Some of the UCA bubbles
in a mass of bubbles start to break at MI=0.04. This phenomenon can
be successfully observed using the light scattering technique
disclosed above. A sudden increase of effective radius indicates
the destruction of one or more UCA bubbles, and the release of
their inner gas core. The corresponding power spectrum confirms
this finding. When the driving power is strong, more UCA bubbles
are expected to break during a given time interval. A particularly
strong acoustic driving pressure can destroy many UCA bubbles with
a single pulse. The surviving bubbles are further destroyed in a
second pulse. Significantly, the UCA bubbles respond to the driving
pulse even while they are being destroyed.
[0176] In conclusion, the light scattering technique disclosed
herein can be used as a powerful tool to study and determine UCA
shell parameters. The empirical data discussed above demonstrate
the following: [0177] The light scattering technique disclosed
herein is an excellent tool to study UCA bubbles. [0178] UCA bubble
dynamics are correctly modeled with various dynamic models. [0179]
Individual UCA bubbles respond to acoustic driving pressure and
undergo development during insonification. [0180] Both the harmonic
and sub-harmonic components of the response of an individual UCA
bubble can be generated when it is forced to oscillate. [0181]
Imaging an individual UCA bubble with diagnostic ultrasound is
feasible. [0182] The harmonic component of the response of masses
of UCA bubbles can be generated when bubbles in the mass are forced
to oscillate. [0183] Light scattering can be used to observe UCA
bubbles breaking at an MI=0.04. [0184] Very strong acoustic
pressure can destroy most UCA bubbles in a mass of bubbles in a
single acoustic pulse.
[0185] It should be recognized that existing particle sizing
instruments can be modified to implement the concepts disclosed
herein. Conventional particle sizing instruments use light
scattering to determine the radius of one or more particles.
Significantly, these instruments are designed to collect light
scattering data from particles while the particles are static
(i.e., while the particles are not experiencing changing pressure
conditions). These instruments will be referred to herein and the
claims that follow as static light scattering particle sizing
instruments.
[0186] Such static light scattering particle sizing instruments can
be modified by incorporating a pressure generator configured to
induce pressure changes in a sampling volume in which the particles
from which the scattered light is being collected are disposed. For
example, an ultrasound imaging probe can be inserted into the
sampling volume, such that when the ultrasound imaging probe is
energized, the particles in the sampling volume will experience
changing pressure conditions. Ultrasound instruments or ultrasound
transducers can be also positioned externally of, but acoustically
coupled to, the sampling volume. Preferably, a sensor configured to
measure the pressure changes in the sampling volume (such as the
hydrophone described above) will also be added to the existing
static light scattering particle sizing instruments.
[0187] The processing required to generate the RT curves, to fit
the curves to dynamic models, and to derive shell parameters can be
implemented by an additional processor, or the processor for the
static light scattering particle sizing instrument can be modified
(i.e., reprogrammed) to implement the additional functions.
[0188] Yet another aspect of collecting scattered light from one or
more microbubbles during changing pressure conditions, is that the
resulting data can be used to differentiate different types of
microbubbles based on their different compressibility (as
microbubbles of different compressibility will exhibit different
changes in their respective diameters), because as discussed above,
light scattering can be used to detect changes in diameters.
Bubbles having a larger radius will scatter more light than bubbles
having a smaller radius, and bubbles that are less compressible
will exhibit larger radii than bubbles which are more compressible,
during increased pressure conditions, enabling light scattering
data to be used to differentiate microbubbles based on their
compressibility.
Determining Particle Parameters Using a Modified Flow
Cytometer:
[0189] FIG. 31 schematically illustrates an exemplary flow
cytometer 100, modified to implement the concepts disclosed herein.
The modification involves adding an acoustic transducer 130 to
direct ultrasound energy toward a particle (such as a microbubble,
a UCA, a microsphere, or a cell) immediately before or while light
scattered by the particle is being collected. FIG. 35A graphically
illustrates data collected from an unmodified flow cytometer, while
FIGS. 35B-D graphically illustrate data collected from a flow
cytometer modified to include the acoustic transducer noted
above.
[0190] Referring to FIG. 31, flow cytometer 100 further includes a
sample fluid delivery component 102, a fluid channel 104, a sample
volume 110, a region of interest 106 in the sample volume, a laser
light source 108, a scattered light collection component 112, a
beam splitter 120, a first filter 122, a second filter 126, a first
detector 124, and a second detector 128. Not specifically shown are
fluid recovery components downstream of the sample volume for
collecting fluid exiting the sample volume, and a system
controller.
[0191] With the exception of the use of acoustic transducer 130 and
additional data processing steps to analyze the data shown in FIGS.
35B-35D, flow cytometer 100 operates much as do conventional flow
cytometers. Fluid delivery component 102 is used to direct a
particle (or a population of particles) into fluid channel 104 at
an appropriate flow rate and encompasses the elements required to
provide that function. Those elements can include fluid lines,
fluid reservoirs, one or more fluid pumps, and one or more valves.
Those of ordinary skill in the art of flow cytometry will readily
recognize how to implement fluid delivery component 102. Fluid
channel 104 represents a fluid line coupling fluid delivery
component 102 with sample volume 110. A quartz flow cell or cuvette
represents an exemplary sample volume.
[0192] Defined within the sample volume is a region of interest.
The region of interest is generally a cylindrical or cubical
volume. Light from laser 108 is directed into the region of
interest, and light scattered by an object or particle entrained in
the flow of fluid passing through the region of interest is
collected by scattered light collection component 112. While light
sources other than a laser can be used, narrow waveband light
sources are convenient, in that a corresponding filter can be
placed in front of the sensor to remove light in wavebands outside
that of the light source, efficiently reducing noise from other
light sources.
[0193] The artisan of ordinary skill will recognize that many
different combinations of optical elements can be used to implement
scattered light collection component 112. The function of scattered
light collection component 112 is simply to collect light scattered
by the object in the region of interest and direct that light to
one or more light sensors. Significantly, the scattered light will
be used to provide intensity or amplitude information, as opposed
to being used for imaging, so relatively simple optical components
can be employed. An exemplary, rather than limiting scattered light
collection component 112, includes a microscope objective 114, a
lens 116, and a field stop 118. The artisan of ordinarily skill in
optics will recognize that many modifications can be made to
scattered light collection component 112 to successfully direct
light scattered from an object in the region of interest to an
appropriate light detector.
[0194] Exemplary modified flow cytometer 100 includes a first
detector 124 and a second detector 128, and further includes a beam
splitter 120 to direct light to each detector. The use of two
detectors, and filters 122 and 126, enables flow cytometer 100 to
collect both scattered light and fluorescent light from the same
particle at the same time. The artisan of ordinarily skill in flow
cytometry will readily recognize the utility of collecting
fluorescence data. In this exemplary embodiment, detector 124 is
used to collected scattered light, and filter 122 is used to remove
light that has a wavelength different than the light emitted by
laser 108 (the scattering of light by the object will not
appreciably change the wavelength provided by the laser). Detector
128 is used to collect fluorescent light (if any) emitted from the
particle, and filter 126 is used to remove light having a
wavelength different than that emitted from a fluorescent dye used
to tag the particles. (Note that fluorescent tagging is not
required to implement the concepts disclosed herein, but such
tagging is often found useful in flow cytometry. Accordingly, flow
cytometer 100 represents a tool that can be used to simultaneously
collect dynamic data from scattered light as well as fluorescence
data). However, it should be understood that only a single light
detector (for collecting scattered light) is required to implement
the novel approach disclosed herein.
[0195] While not shown in FIG. 31, data from sensor 124 can be
manipulated by a processor to determine one or more characteristics
of the particle, generally as discussed above. It should also be
recognized that flow cytometer 100 is intended to be exemplary, and
that many different flow cytometer designs can be modified by the
inclusion of an acoustic transducer to direct energy at a particle
being interrogated by the flow cytometer.
[0196] Note that flow cytometer 100 does not specifically include
an element to measure the ambient pressure conditions in the sample
volume, as is employed in system 20 of FIG. 2 (see hydrophone 30).
However, it should be recognized that such a pressure sensor can be
incorporated into flow cytometer 100, if desired.
[0197] Some existing flow cytometer designs include a plurality of
light sources (generally lasers) and a plurality of detectors, with
different laser/detector combinations configured to collect light
scattered by a particle from different portions of a sample volume.
It should be recognized that the concepts disclosed herein can also
be used to modify such flow cytometer designs.
[0198] The position of transducer 130 relative to the sample volume
can be varied. The most significant requirement is that the
transducer be disposed close enough to the region of interest that
the particle will be vibrating (or oscillating, or otherwise
responding to the pressure pulse) in response to the acoustic
energy while in the region of interest (if the particle were no
longer vibrating because the acoustic energy was directed at the
particle too early, then the scattered light data would correspond
to the data shown in FIG. 35A, as opposed to the data shown in
FIGS. 35B-35D). It should be understood that the power source and
control elements for transducer 130 have not been separately shown.
The artisan of ordinary skill in the acoustic arts will readily
recognize how to energize and control transducer 130.
[0199] Exemplary, but not limiting operating parameters for flow
cytometer 100 are as follows: a flow channel having a diameter of
about 150 microns; a flow rate of about 2 meters/second; a 200 mW
488 nm laser light source with a beam diameter of about 20 microns;
and an acoustic transducer operating in the range of about 100 kHz
to about 50 MHz. Where the particles are UCAs, the acoustic
transducer can be operated in the range of from about 1 MHz to
about 5 MHz. Where the particles are biological cells, the acoustic
transducer can be operated in the range of from about 10 MHz to
about 40 MHz.
[0200] FIGS. 32A and 32B are plan views of sample volume 110,
showing the relative locations of the sample volume, the
transducer, and laser 108. In these exemplary embodiments, the
transducer is attached to one of four faces of the sample volume.
The laser is disposed proximate a first face, and the face opposite
and parallel to the first face is un-obstructed, to allow light
scattered by the particle to exit that sample volume. The third and
fourth faces are orthogonal to the first and second faces. In FIG.
32A, a transducer 130a is attached to the third face, while in FIG.
32B, a transducer 130b is attached to the fourth face. In a related
embodiment, transducer 130a is attached to the third face and
transducer 130b is attached to the fourth face. Using multiple
transducers has the benefit of enabling different acoustic
frequencies and intensities to be easily directed toward the
particle in the sample volume. Of course, transducers whose output
can be varied are available, and use of two transducers is thus not
the only way to achieve such variability.
[0201] FIG. 32C is also a plan view of sample volume 110, showing
the relative locations of the sample volume, the transducer, and
laser 108. In this Figure, a transducer 130c having an annular
opening 131 is attached to the first face of the sample volume,
between laser 108 and the sample volume. The annular opening allows
light from the laser to be directed into the region of interest of
the sample volume.
[0202] FIGS. 33A-33C are side views of region of interest 106,
where acoustic transducer 130 is disposed generally as shown in
FIGS. 32A and 32B. Note that while the region of interest
represents a portion of sample volume 110, for simplicity sake, the
transducers in FIGS. 33A-33C are shown as attached to the region of
interest (i.e., an inner portion of a flow cell or cuvette), as
opposed to the sample volume (i.e., the flow cell or cuvette). The
particle moving through the region of interest is illuminated by
light from the light source (such as laser 108) while in region of
interest 106, and light scattered by the particle while in the
region of interest is collected by the detector (such as detector
124 of FIG. 31). As noted above, FIG. 35A graphically illustrates
scatter intensity data collected from a conventional flow cytometer
(i.e., a flow cytometer that does not include an acoustic
transducer to direct acoustic energy toward the particle while (or
immediately before) the light scattered by the particle is
collected.
[0203] Referring to FIG. 33A, acoustic transducer 130 is providing
a constant acoustic field 136 directed toward a particle 134 as it
moves through the region of interest. FIG. 35B graphically
illustrates data collected under such conditions. Note the
differences between the smooth curve of FIG. 35 A, and the
fluctuating curve of FIG. 35B. The fluctuations can be analyzed
(generally as discussed above) to determine one or more properties
of the particle scattering the light.
[0204] Referring to FIG. 33B, acoustic transducer 130 is only
energized while the particle initially enters the region of
interest, such that particle 134 encounters an acoustic field 138
only as it initially enters the region of interest. After a period
of time, the vibrations induced by the acoustic pulse decay, and
the scattering intensity will no longer be dynamically varied. FIG.
35C graphically illustrates data collected under such conditions.
Note the differences between the curve in FIG. 35C, the smooth
curve of FIG. 35 A, and the fluctuating curve of FIG. 35B. The
intensity curve of FIG. 35C looks like a composite of the intensity
curves of FIGS. 35A and 35B, with the fluctuating portion of the
curve corresponding to the particle vibrating due to the acoustic
pulse, and the smooth portion of the curve corresponding to the
vibrating induced by the acoustic pulse fading away. Note that the
speed of the particle, the intensity of the acoustic energy, and
the duration of the initial acoustic pulse will effect the shape of
the composite (or decay) curve of FIG. 35C. The decay curve of FIG.
35C can also be obtained using a transducer that does not extend
along the entire height of the region of interest, but rather is
limited to an initial upper portion of the region of interest.
Furthermore, as long as the flow rate of the fluid passing through
the region of interest is sufficiently high, the transducer can be
located just upstream of the region of interest, such that the
particle encounters the acoustic energy before entering the region
of interest.
[0205] Referring to FIG. 33C, acoustic transducer 130 is utilized
to direct a first acoustic field 140 toward particle 134 as it
enters the region of interest, and a second different acoustic
field 142 toward the particle in a different portion of the region
of interest. Either a single transducer can be operated under
different conditions to provide acoustic fields 140 and 142, or two
different transducers can be employed.
[0206] FIG. 34A is a functional block diagram of another exemplary
flow cytometer system including an acoustic transducer to direct
acoustic energy toward a particle before or while light scattered
by the particle is detected. In this Figure, dash lines indicate a
logical connection between elements, and a solid line indicates a
fluidic or optical connection. Such an exemplary flow cytometer
system includes a sample fluid delivery component 140, a sample
volume 142, an acoustic transducer 144, a light source 146, a
scattered light sensor 148, a controller 150, and an optional
sorting component 149.
[0207] Fluid delivery component 140 is used to direct a particle
(or a population of particles) into the sample volume at an
appropriate flow rate. Fluid delivery component 102 is intended to
encompass the elements required to provide that function. Those
elements can include fluid lines, fluid reservoirs, one or more
fluid pumps, and one or more valves. Those of ordinary skill in the
art of flow cytometry will readily recognize how to implement fluid
delivery component 140. A quartz flow cell or cuvette represents an
exemplary sample volume 110.
[0208] As discussed above, acoustic transducer 144 is positioned to
direct acoustic energy toward a particle (such as a microbubble, a
UCA, a microsphere, or a cell) immediately before, or while, light
scattered by the particle is being collected.
[0209] Light from light source 146 is directed into the sample
volume, and light scattered by an object entrained in the flow of
fluid passing through the sample volume is collected by light
sensor 148. As noted above, many different types of light sources
can be used, laser light sources being exemplary, but not a
limiting example of the type of light sources.
[0210] Controller 150 performs a plurality of functions. Data from
light sensor 148 can be manipulated to determine one or more
characteristics of the particle, generally as discussed above.
Controller 150 can also be used to control the fluid delivery
component (i.e., pumps and valves), the light source, and the
acoustic transducer (of course, if desired, one or more additional
controllers can be dedicated to control such elements).
[0211] Optional sorting component 149 can be used as follows.
Dynamic scattering intensity spectrums (i.e., intensity spectrums
collected as the particle is responding to an acoustic pressure
pulse) for specific particles can be obtained and saved. A
population of mixed particles can be introduced into the flow
cytometer. As dynamic scattering intensity data for each particle
is collected, controller 150 will send a control signal to sorting
component 149 for each particle whose dynamic intensity spectrum
corresponds to the previously determined dynamic intensity spectrum
of a target particle. Sorting component 149 then directs that
particle to a reservoir dedicated to collecting the target
particles. In an exemplary embodiment, sorting component 149
includes one or more valves and a plurality of particle reservoirs
and fluid lines. Sorting component 149 uses the dynamic scattering
intensity profile determined by controller 150 for each particle
and manipulates the one or more valves as required to direct
particles to specific reservoirs. For example, assume that dynamic
scattering intensity spectrums have been identified for three
different particle types. A population of particles that may
include one or more of those three different particles is
introduced into the modified flow cytometer (i.e., a flow cytometer
including an acoustic transducer to enable dynamic scattering
intensity spectrums to be collected). In such an embodiment,
sorting component 149 can include four reservoirs, one for each of
the three particle types, and one generic reservoir for all other
types of particles. As the dynamic scattering intensity spectrum
for each particle is determined, sorting component 149 can direct
the particle to the appropriate reservoir.
[0212] While a power supply for components such as the controller,
the light source, the sensor, and the transducer are not
specifically shown, the artisan of ordinary skill will readily
recognize how to incorporate such elements into the system.
[0213] FIG. 34B schematically illustrates an exemplary relationship
between a light source used to illuminate a particle in a sample
volume, and a light collection system used to collect light
scattered by the particle and direct that light to a sensor. As
indicated in FIG. 34B, the exemplary light collection system is
disposed at an angle ranging from about 70 degrees to about 90
degrees relative to the laser source light path. An angle of about
82 degrees is particularly useful. It will be understood that this
exemplary range is not required, and the concepts disclosed herein
can be used in flow cytometers having different relative
angles.
[0214] FIGS. 35A-35D graphically illustrate scattering intensity
spectrums collected using a flow cytometer as discussed above. FIG.
35A graphically illustrates a static scattering intensity spectrum,
where the particle from which light is being collected is not
reacting to an applied pressure pulse or acoustic pulse. FIG. 35B
graphically illustrates a dynamic scattering intensity spectrum,
where the particle from which light is being collected is reacting
to an applied pressure pulse or acoustic pulse, where that pulse is
being applied to the particle continuously during the collection of
light scattered by the particle. FIG. 35C graphically illustrates a
dynamic scattering intensity spectrum, where the particle from
which light is being collected is reacting to an applied pressure
pulse or acoustic pulse, and where the pulse is initially applied
and then terminated, so that pressure is not being applied to the
particle during a latter portion of the light collection process.
FIG. 35D graphically illustrates a plurality of dynamic scattering
intensity spectrums, where each particle from which light is being
collected is reacting to an applied pressure pulse or acoustic
pulse. A dynamic scattering intensity spectrum 200 is reacting to a
relatively small pressure pulse, while dynamic scattering intensity
spectrum 202 is reacting to a relatively large pressure pulse. The
pressure pulse employed was gradually increased for each dynamic
scattering intensity spectrum between dynamic scattering intensity
spectrum 200 and dynamic scattering intensity spectrum 202.
[0215] The concepts disclosed herein can be used in many different
ways. Manufactures of UCA can use the techniques disclosed herein
to characterize a new UCA under development. These techniques can
also be beneficially employed to sort particles based on their
dynamic scattering intensity spectrums (the term "dynamic"
indicating that the scattering intensity profile is being collected
while the particle is reacting to the application of a pressure
wave or acoustic pulse). It is believed that dynamic scattering
intensity spectrums can provide better differentiation of particles
than static scattering intensity spectrums (the term "static"
indicating that the scattering intensity profile is being collected
while the particle is exposed to a constant pressure
condition).
Exemplary Computing Environment
[0216] As noted above, the concepts disclosed herein involve
analysis of a plurality of dynamic scattering intensity spectrums
collected from particles in a flow of fluid, using a flow cytometer
configured to direct a pressure pulse or acoustic pulse toward the
particle. Reference has been made to an exemplary controller for
performing the analysis. FIG. 36 and the following related
discussion are intended to provide a brief, general description of
a suitable computing environment for practicing the concepts
disclosed herein, where the analysis required is implemented using
a computing device generally like that shown in FIG. 36. Those
skilled in the art will appreciate that the required image
processing may be implemented by many different types of computing
devices, including a laptop and other types of portable computers,
multiprocessor systems, networked computers, mainframe computers,
hand-held computers, personal data assistants (PDAs), and on other
types of computing devices that include a processor and a memory
for storing machine instructions, which when implemented by the
processor, result in the execution of a plurality of functions used
for implementing the present novel approach.
[0217] An exemplary computing system 151 suitable for implementing
the analysis required includes a processing unit 154 that is
functionally coupled to an input device 152, and an output device
162, e.g., a display. Processing unit 154 includes a central
processing unit (CPU) 158 that executes machine instructions
comprising a dynamic scattering intensity spectrum analysis program
for implementing the functions disclosed herein (analyzing dynamic
scattering intensity spectrums to enable at least one
characteristic of a particle to be determined, and/or to sort
particles in a population of particles). Those of ordinary skill in
the art will recognize that processors or CPUs suitable for this
purpose are available from Intel Corporation, AMD Corporation,
Motorola Corporation, and other sources.
[0218] Also included in processing unit 154 are a random access
memory 156 (RAM) and non-volatile memory 160, which typically
includes read only memory (ROM) and some form of memory storage,
such as a hard drive, optical drive, etc. These memory devices are
bi-directionally coupled to CPU 158. Such data storage devices are
well known in the art. Machine instructions and data are
temporarily loaded into RAM 156 from non-volatile memory 160. Also
stored in memory are the operating system software and ancillary
software. While not separately shown, it should be understood that
a power supply is required to provide the electrical power needed
to energize computing system 151.
[0219] Input device 152 can be any device or mechanism that
facilitates input into the operating environment, including, but
not limited to, a mouse, a keyboard, a microphone, a modem, a
pointing device, or other input devices. While not specifically
shown in FIG. 36, it should be understood that computing system 151
is logically coupled to a modified flow cytometer system, such as
that schematically illustrated in FIG. 31, so that the dynamic
scattering intensity spectrums collected are available to computing
system 151 to achieve the desired processing. Of course, rather
than logically coupling the computing system directly to the flow
cytometer system, data collected by the imaging system can simply
be transferred to the computing system by means of many different
data transfer means, such as portable memory media, or via a
network (wired or wireless). Output device 162 will most typically
comprise a monitor or computer display designed for human visual
perception of an output image.
[0220] Although the concepts disclosed herein have been described
in connection with the preferred form of practicing them and
modifications thereto, those of ordinary skill in the art will
understand that many other modifications can be made thereto within
the scope of the claims that follow. Accordingly, it is not
intended that the scope of these concepts in any way be limited by
the above description, but instead be determined entirely by
reference to the claims that follow.
* * * * *