U.S. patent application number 10/251309 was filed with the patent office on 2004-04-01 for system and method for screening tissue.
Invention is credited to Ferrari, Mauro, Liu, Jun, Rokhlin, Stanislav I., Sedmak, Daniel D..
Application Number | 20040064050 10/251309 |
Document ID | / |
Family ID | 32028999 |
Filed Date | 2004-04-01 |
United States Patent
Application |
20040064050 |
Kind Code |
A1 |
Liu, Jun ; et al. |
April 1, 2004 |
System and method for screening tissue
Abstract
A system and method for screening tissue is provided. The system
provides a computer-based system for distinguishing between normal
and potentially abnormal tissue. The system includes computer
components for generating and receiving ultrasonic waves, for
storing a tissue model, and for analyzing received ultrasonic waves
in the context of the tissue model.
Inventors: |
Liu, Jun; (Columbus, OH)
; Ferrari, Mauro; (Dublin, OH) ; Rokhlin,
Stanislav I.; (Columbus, OH) ; Sedmak, Daniel D.;
(Columbus, OH) |
Correspondence
Address: |
CALFEE HALTER & GRISWOLD, LLP
800 SUPERIOR AVENUE
SUITE 1400
CLEVELAND
OH
44114
US
|
Family ID: |
32028999 |
Appl. No.: |
10/251309 |
Filed: |
September 20, 2002 |
Current U.S.
Class: |
600/457 |
Current CPC
Class: |
A61B 8/08 20130101; A61B
8/485 20130101 |
Class at
Publication: |
600/457 |
International
Class: |
A61B 008/14 |
Claims
What is claimed is:
1. A tissue screening system, comprising: an ultrasonic wave
producer that produces a first ultrasonic wave that is directed at
a tissue to be screened; an ultrasonic wave receiver that receives
one or more second ultrasonic waves, where the second ultrasonic
waves are produced by the first ultrasonic wave interacting with
the tissue to be screened; and an analyzer operably connected to
one or more of the ultrasonic wave producer and the ultrasonic wave
receiver, where the analyzer differentiates tissue regions in the
tissue to be screened based, at least in part, on analyzing one or
more relationships between the first ultrasonic wave and the second
ultrasonic waves.
2. The system of claim 1, comprising a tissue mechanical properties
model in data communication with the analyzer.
3. The system of claim 2, where the tissue mechanical properties
model stores information concerning one or more of, tissue
reflection, tissue transmission, tissue elasticity, tissue particle
size, tissue micro-architecture, and tissue micromoduli.
4. The system of claim 2, where the analyzer analyzes the second
ultrasonic waves to characterize the mechanical reaction of the
first ultrasonic wave with the tissue to be screened to facilitate
identifying tissue mechanical properties which in turn facilitate
identifying tissue properties and distinguishing healthy tissue
from diseased tissue.
5. The system of claim 2, where the tissue mechanical properties
model is based on nanomechanics.
6. The system of claim 2, where the tissue mechanical properties
model is based on one of a linear or non-linear viscoelastic
model.
7. The system of claim 1, where the tissue mechanical properties
model stores information associated with reflection coefficients of
normal and abnormal tissues.
8. The system of claim 1, where the analyzer is a computer
component.
9. The system of claim 1, where the tissue to be screened is one of
an external tissue surface and an internal tissue surface.
10. The system of claim 1, where the tissue to be screened is a
human tissue.
11. The system of claim 1, where the one or more second ultrasonic
waves include one or more reflected waves.
12. The system of claim 1, where the one or more second ultrasonic
waves include one or more transmitted waves.
13. The system of claim 1, where the analyzer analyzes a reflection
spectrum.
14. The system of claim 1, where the tissue to be sampled is
treated with a nanoparticle contrast agent before having the first
ultrasonic wave directed at the tissue to be sampled.
15. The system of claim 1, where the ultrasonic wave producer and
the ultrasonic wave receiver are both located in a portable device
that can be passed over the tissue to be screened.
16. The system of claim 1, where the ultrasonic wave producer and
the ultrasonic wave receiver are both located in a device under
which the tissue to be screened can be passed.
17. The system of claim 1, where the ultrasonic wave producer is
one of a transducer and a transducer array.
18. The system of claim 1, where the ultrasonic wave receive is one
of a transducer and a transducer array.
19. The system of claim 1, where the first ultrasonic wave is in
the range of 2.5 to 12.5 MHz, with a center frequency of 7.5
MHz.
20. The system of claim 1, where the first ultrasonic wave is in a
range with a lower bound between 2 and 7 MHz and an upper bound
between 8 and 13 MHz.
21. A computer readable medium storing computer executable
components of the system of claim 2.
22. A method for screening tissue, comprising: directing an
ultrasonic wave at a tissue to be screened; receiving one or more
second ultrasonic waves produced by the first ultrasonic wave
interacting with the tissue to be screened; and determining whether
an area of the tissue to be screened should be tagged, where the
determining includes analyzing one or more parameters associated
with the second ultrasonic waves in the context of a tissue
mechanical properties model.
23. The method of claim 22, where the first ultrasonic waves have a
frequency in the range 3 to 13 MHz.
24. The method of claim 22, where the second ultrasonic waves
comprise one or more of, reflected waves, and transmitted
waves.
25. The method of claim 22, comprising: associating a nanoparticle
contrast agent with the tissue to be screened before directing the
ultrasonic wave at the tissue to be screened.
26. The method of claim 22, where analyzing the one or more
parameters associated with the second ultrasonic waves comprises
analyzing one or more of, tissue reflection, tissue transmission,
tissue elasticity, tissue particle size, tissue micromoduli, tissue
micro-architecture, and tissue mechanical response properties.
27. A computer readable medium storing computer executable
instructions operable to perform computer executable portions of
the method of claim 22.
28. A tissue area mapping method, comprising: identifying one or
more first data values for a set of tissue area parameters by
processing a first resultant ultrasonic wave received from a tissue
area, where the first resultant ultrasonic wave is the result of a
first incident ultrasonic wave interacting with the tissue area;
storing the first data values; at a later time, identifying one or
more second data values for the set of tissue area parameters by
processing a second resultant ultrasonic wave received from the
tissue area, where the second resultant ultrasonic wave is the
result of a second incident ultrasonic wave interacting with the
tissue area; and selectively tagging a pathologically interesting
tissue location in the tissue area based, at least in part, on
analyzing tissue mechanical properties discernible by analyzing one
or more of the first data values, the second data values, and
relations between the first and second data values.
29. The system of claim 28, where analyzing the first data values
and the second data values comprises analyzing one or more of
tissue reflection, tissue transmission, tissue elasticity, tissue
particle size, tissue micromoduli, tissue micro architecture, and
tissue mechanical response properties.
30. The system of claim 29, where the tissue area is a mole.
31. An automated pathology slide reader, comprising: a slide holder
for holding a slide on which tissue is located, the tissue being
located between two first layers, the slide being immersed in a
fluid; an ultrasound wave generator for producing incident
ultrasonic waves that are directed at the slide at an incident
angle; an ultrasound wave receiver for receiving a reflected
ultrasonic wave produced by the incident wave interacting with the
tissue on the slide, where the reflected ultrasonic wave is
reflected from the slide at a reflection angle; and a comparison
computer component for comparing one or more of, the incident
angle, the reflected angle, reflection spectra, the incident wave,
and the reflected wave, and determining one or more tissue
properties.
32. The slide reader of claim 31, where the incident angle is an
angle between two mode conversion angles of the first layers and
the fluid in which the slide is immersed.
33. The slide reader of claim 31, where the incident angle is
between 15 and 20 degrees.
34. A tissue screening system, comprising: means for generating a
first ultrasonic wave; means for directing the first ultrasonic
wave at a tissue sample to be screened; means for collecting a
second ultrasonic wave produced from a mechanical interaction of
the first ultrasonic wave and the tissue sample to be screened;
means for modeling one or more modeled mechanical properties of
tissue into one or more modeled mechanical properties of tissue;
means for correlating the one or more mechanical properties with
one or more mechanical interactions between a tissue sample and an
ultrasonic wave; and means for identifying pathologically
interesting regions of the tissue sample based, at least in part,
on correlating the one or more mechanical properties with one or
more mechanical interactions between the tissue sample, the first
ultrasonic wave, and the second ultrasonic wave in the context of
the modeled mechanical properties.
35. A method for building a model that characterizes tissue
response to ultrasonic waves, comprising: acquiring a set of known
normal tissue samples; analyzing the set of normal tissue samples
employing ultrasonic waves and a nanomechanical representation of
the normal tissue samples to produce a first analysis data;
acquiring a set of known malignant tissue samples; analyzing the
set of known malignant tissue samples employing ultrasonic waves
and a nanomechanical representation of the malignant tissue samples
to produce a second analysis data; characterizing tissue based, at
least in part, on the first analysis data, the second analysis
data, and a nanomechanical representation of tissue, where the
characterizing produces a characterization data; and building a
model that stores one or more of the first analysis data, the
second analysis data, and the characterization data.
36. A computer readable medium having stored thereon a data
structure associated with a biomechanical response model,
comprising: a first field that stores information associated with
tissue reflection properties; a second field that stores
information associated with tissue health properties; and a third
field that stores correlation information associated with
correlating the tissue reflection properties of the first field and
the tissue health properties of the second field.
Description
TECHNICAL FIELD
[0001] The methods, systems, and computer readable media described
herein relate generally to screening tissue and more particularly
to analyzing ultrasonic waves interacting with tissue, where the
analysis relies on a biomechanical response model derived from a
quantitative correlation of tissue responses to ultrasonic
interrogation.
BACKGROUND
[0002] Technologies employed for early detection of diseased tissue
(e.g., cancer) include visual inspection, x-ray computer
tomography, ultrasound, positron emission tomography (PET)
scanning, magnetic resonance imaging (MRI) and so on. While such
technologies have had various degrees of success detecting disease
in an early stage, improvements are constantly being sought.
Definitive diagnosis, especially of malignant disease, still
typically includes biopsy, an invasive, costly, time-consuming
procedure.
[0003] It is possible to obtain quantitative information on the
physical characteristics of a material through ultrasound
inspection. Non-destructive ultrasonic testing has been employed
for evaluating engineering structures by the determination of their
relevant material properties. Translating this approach to
biomedical applications (e.g., disease screening) is complicated
due to the lack of appropriate theoretical models that facilitate
reconstructing physical properties of biological tissue. In
particular, models derived from the conventional mechanics of
solids, including biological domains, are based on a continuum
representation. The continuum representation postulates the
existence of a typical dimension or Representative Volume Element
(RVE), below which matter may be assumed to be continuous and fully
homogeneous. On these foundations, mechanical phenomena may then be
represented in a differential equation format. This modeling
strategy breaks down when it is not possible to establish a
continuum RVE. Establishing a continuum RVE is not possible when
phenomena are examined on a length scale at which the discrete,
inhomogeneous nature of the media is evident, as frequently
encountered in biological tissue examination.
[0004] Approaches have been developed that attempt to address these
concerns by representing complex composite domains as continua with
continuum inclusions. These theories, collectively known as
"micromechanics", still suffer from the limitation that they do not
incorporate the discrete nature of matter, while remaining
computationally manageable at domain sizes that are currently
incomparable to lattice dynamics, ab-initio approaches, or
molecular dynamics.
[0005] Additionally, measuring mechanical properties of biological
soft tissue has been elusive because tissue is not well-behaved
material. Indeed, mechanically soft tissue is known as being
inhomogeneous anisotropic, non-linear, and viscoelastic.
SUMMARY
[0006] The following presents a simplified summary of methods,
systems, and computer readable media for screening tissue by
ultrasonic waves to facilitate providing a basic understanding of
these items. This summary is not an extensive overview and is not
intended to identify key or critical elements of the methods,
systems, and computer readable media or to delineate the scope of
these items. This summary provides a conceptual introduction in a
simplified form as a prelude to the more detailed description that
is presented later.
[0007] Early detection of diseased tissue (e.g. cancer) can benefit
patients, physicians, providers, and others. Thus, there have been
efforts to identify and quantify, for example, cancer "signatures"
toward this purpose. One type of signature relates to the physical
properties of the diseased tissue as compared to normal
counterparts. Such signatures are identifiable in part because of
the well-recognized phenomenon that changes in tissue physical
properties are associated with disease inception. Example physical
properties include, but are not limited to, tissue elasticity
(e.g., stiffness, hardness), cellular geometry (e.g., cell size,
cell shape), internodal distance, particle size, tissue
micro-architecture (e.g., spatial distribution of cells and
cellular matrices), and so on.
[0008] One way to mechanically test tissue so that the effects of
the physical properties can be measured is to direct high frequency
ultrasonic waves at the tissue. These waves interact with (e.g.,
reflect from and/or transmit through) the tissue, and the reflected
and/or transmitted waves can then be analyzed to estimate the
physical properties of tissue through its mechanical response to
sound waves. The quantitative information thus obtained offers
beneficial implications for separating normal tissue from abnormal
tissue.
[0009] Thus, in one aspect, the application describes a tissue
screening system. The system includes an ultrasonic wave producer
that produces ultrasonic waves that are directed at a tissue to be
screened. The waves interact with the tissue and produce a set of
resulting ultrasonic waves. The system also includes an ultrasonic
wave receiver that receives resulting ultrasonic waves and an
analyzer operably connected to the ultrasonic wave producer and/or
the ultrasonic wave receiver. In one example, reflected ultrasonic
pulses are transformed to frequency domain through Fast Fourier
Transformation (FFT) and become reflection spectra. The analyzer
differentiates tissue regions by analyzing parameters (e.g.,
reflection spectra) of the resulting ultrasonic waves. In another
example, the system also includes a tissue mechanical properties
model in data communication with the analyzer. The model stores
information associated with quantitative correlations between the
physical properties of inspected tissue and reflection spectra. In
one example, the model stores information derived from previous
measurements and studies on the reflection spectra and/or physical
properties of normal and abnormal tissue. In another example, the
model stores information associated with an inverse algorithm,
which may be implemented in software, that facilitates
reconstructing physical properties of inspected tissue. Thus, the
analyzer utilizes information including, but not limited to,
reflected spectra, and reconstructed physical properties, to
distinguish between normal tissue and malignant tissue.
[0010] In another aspect, the application describes a method for
screening tissue. In one example the method includes directing an
ultrasonic wave at a tissue to be screened, receiving second
ultrasonic waves produced by the first ultrasonic wave interacting
with the tissue to be screened, and determining whether an area of
the tissue to be screened should be tagged. The determining may
include, for example, analyzing one or more parameters associated
with the second ultrasonic waves in the context of a tissue
mechanical properties model. In one example, the tissue may first
be treated with a nanoparticle contrast agent to facilitate
identifying and differentiating tissue areas.
[0011] While this summary describes in general the propagation and
analysis of high frequency elastic waves, it is to be expected that
one skilled in the art will have an understanding of such waves,
and thus further discussion is limited herein for the sake of
brevity. A discussion of reflection coefficients of tissue is
included in "A Discrete Model For The High Frequency Elastic Wave
Examination On Biological Tissue", which is incorporated herein by
reference as the type of material with which one skilled in the art
would be familiar. Similarly, while this summary describes in
general the theory of nanomechanics, it is to be expected that one
skilled in the art will have an understanding of nanomechanics. A
discussion of nanomechanics can be found in "Advances in Doublet
Mechanics", Ferrari et. al, ISBN 3-540-62061-3, Springer 1997,
which is incorporated herein by reference as the type of material
with which one skilled in the art would be familiar.
[0012] Certain illustrative example methods, systems, and computer
readable media are described herein in connection with the
following description and the annexed drawings. These examples are
indicative, however, of but a few of the various ways in which the
principles of the methods, systems, and computer readable media may
be employed and thus are intended to be inclusive of equivalents.
Other advantages and novel features may become apparent from the
following detailed description when considered in conjunction with
the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 illustrates two simulated tissue samples, one of
normal tissue, and one of tissue affected by cancer (e.g., invasive
ductal carcinoma).
[0014] FIG. 2 illustrates an example experimental apparatus
employed to analyze reflection coefficients.
[0015] FIG. 3 illustrates an example incident wave and example
waves that result from the interaction of the incident wave and a
tissue sample.
[0016] FIG. 4 is a schematic of a thin, discrete-structured
(granular) layer embedded between two substrates modeled as
isotropic elastic continua.
[0017] FIG. 5 illustrates an example nanomechanical microstructure
representation.
[0018] FIG. 6 illustrates reflection spectra from an example
continuum model and an example nanomechanics model for "larger"
internodal distances (e.g., .eta.=8-.mu.m).
[0019] FIG. 7 illustrates an effect on mechanical response due to
varying A.sub.11 to facilitate studying the reflection spectra.
[0020] FIG. 8 illustrates an effect on mechanical response due to
varying A.sub.44 to facilitate studying the reflection spectra.
[0021] FIG. 9 illustrates an effect on mechanical response due to
varying particle size to facilitate studying reflection
spectra.
[0022] FIG. 10 illustrates an example translation of doublet nodes
as characterized in nanomechanics.
[0023] FIG. 11 illustrates an example system for screening
tissue.
[0024] FIG. 12 illustrates an example system for screening
tissue.
[0025] FIG. 13 illustrates example sampling times and locations
employed in a method for screening tissue.
[0026] FIG. 14 illustrates an example method for screening tissue
using ultrasonic waves.
[0027] FIG. 15 illustrates another example method for screening
tissue using ultrasonic waves.
[0028] FIG. 16 illustrates an example method for mapping tissue
areas using ultrasonic waves.
[0029] FIG. 17 is a schematic block diagram of an example computing
environment with which computer executable portions of the systems
and methods described herein can interact.
[0030] FIG. 18 illustrates an example tissue screening system.
[0031] FIG. 19 illustrates an input-output system.
[0032] FIG. 20 illustrates a series of input-output systems.
DETAILED DESCRIPTION
[0033] Example methods, systems, and computer media are now
described with reference to the drawings, where like reference
numerals are used to refer to like elements throughout. In the
following description, for purposes of explanation, numerous
specific details are set forth in order to facilitate thoroughly
understanding the methods, systems and computer readable media. It
may be evident, however, that the methods, systems, and computer
readable media can be practiced without these specific details. In
other instances, well-known structures and devices are shown in
block diagram form in order to simplify description.
[0034] As used in this application, the term "computer component"
refers to a computer-related entity, either hardware, firmware,
software, a combination thereof, or software in execution. For
example, a computer component can be, but is not limited to being,
a process running on a processor, a processor, an object, an
executable, a thread of execution, a program, and a computer. By
way of illustration, both an application running on a server and
the server can be computer components. One or more computer
components can reside within a process and/or thread of execution
and a computer component can be localized on one computer and/or
distributed between two or more computers.
[0035] "Logic", as used herein, includes but is not limited to
hardware, firmware, software, and/or combinations of each to
perform a function(s) or an action(s). For example, based on a
desired application or needs, logic may include a software
controlled microprocessor, discrete logic such as an application
specific integrated circuit (ASIC), or other programmed logic
device. Logic may also be fully embodied as software.
[0036] An operable connection is one in which signals and/or actual
communication flow and/or logical communication flow may be sent
and/or received. Usually, an operable connection includes a
physical interface, an electrical interface, and/or a data
interface, but it is to be noted that an operable connection may
consist of differing combinations of these or other types of
connections sufficient to allow operable control.
[0037] "Signal", as used herein, includes but is not limited to one
or more electrical or optical signals, analog or digital, one or
more computer instructions, a bit or bit stream, or the like.
[0038] "Software", as used herein, includes but is not limited to,
one or more computer readable and/or executable instructions that
cause a computer or other electronic device to perform functions,
actions and/or behave in a desired manner. The instructions may be
embodied in various forms like routines, algorithms, modules,
methods, threads, and/or programs. Software may also be implemented
in a variety of executable and/or loadable forms including, but not
limited to, a stand-alone program, a function call (local and/or
remote), a servelet, an applet, instructions stored in a memory,
part of an operating system or browser, and the like. It is to be
appreciated that the computer readable and/or executable
instructions can be located in one computer component and/or
distributed between two or more communicating, co-operating, and/or
parallel processing computer components and thus can be loaded
and/or executed in serial, parallel, massively parallel and other
manners.
[0039] Biological tissue is usually granular or cellular by nature.
The onset of disease (e.g., cancer) may cause changes in tissue
microstructures. For example, in FIGURE One, two simulated slides
that compare normal tissue with diseased tissue are illustrated.
The two tissues may have differences in properties including but
not limited to, micromoduli, internodal distance, and/or particle
size. Micromoduli and internodal distance are physical parameters
of matter at its granular or node level, possibly down to the
nanoscale. Micromoduli refers to the constants that appear in the
constitutive relationships between the microstresses and the
microstrains. Internodal distance refers to the distance between
two granules or nodes that have effective mechanical interaction.
In one example, internodal distance can be equivalent to particle
size if the matter is composed of space-filling granular
components.
[0040] The types of tissue that can be examined by the systems and
methods described herein include, but are not limited to, external
surface tissue (e.g., skin surface), and internal surface tissue
(e.g., stomach lining). The varied physical properties impact
mechanical responses like reflectivity and transmissivity of
tissue, which facilitates identifying pathologically interesting
tissue areas.
[0041] Two example mechanical parameters of tissue--micromoduli and
particle size--can be analyzed to facilitate screening for
diseased/altered tissue. Particle size and/or internodal distance
is relevant, for example, to cell size. Referring again to FIG. 1,
sample 100 simulates healthy tissue, which may exhibit a first set
of responses to mechanical waves due to a first set of mechanical
properties. While healthy tissues may exhibit a range of responses
to ultrasonic waves, various measurable parameters (e.g.,
reflection coefficients) and physical properties for healthy
tissues can be experimentally measured and theoretically solved for
using various mathematical and engineering techniques described
and/or referred to herein. Sample 110 simulates unhealthy tissue
that has been affected by cancer (e.g., invasive ductal carcinoma).
Sample 110 will, therefore, likely exhibit a second set of
responses to mechanical waves due to a second set of mechanical
properties. Again, while unhealthy tissues may exhibit a range of
responses to ultrasonic waves, various measurable parameters (e.g.,
reflection coefficients) and physical properties for unhealthy
tissues can be experimentally measured and theoretically solved for
using various mathematical and engineering techniques described
and/or referred to herein. With parameters like reflection
coefficients experimentally measured and theoretically solved for,
systems and methods can employ a tissue mechanical properties model
based on the measured and solved for parameters (e.g., reflection
coefficients) to facilitate screening tissue for areas that are
(un)healthy.
[0042] Developing a model based on the reflection coefficients
and/or other measurable parameters required studying physical
properties (e.g., micromoduli, particle size) of tissue in an
experimental setting. To facilitate characterizing mechanical
properties of healthy and unhealthy tissues and/or the mechanical
responses of tissues to ultrasonic waves, experiments were
conducted that measured, among other things, relationships
discovered by analyzing, for example, the reflected waves, and the
spectra created by such waves after interacting with tissue
samples. Mechanical properties of tissue can be analyzed by
examining, for example, the stresses and strains in a tissue sample
as revealed by the relationships between incident and reflected
waves. Both continuum mechanics and nanomechanics can be employed
to study the mechanical properties and responses of tissue, from
which theoretical models of such properties and responses can be
constructed.
[0043] Mechanical waves cause displacements of particles in a
sample to be studied. Nanomechanics is distinguishable from
micromechanics and continuum mechanics based on its multi-scale
nature and its ability to model discrete nodes at finite distances
as small as the nanometer range. Thus, models can be built for
analyzing and characterizing plane elastic wave propagation in
tissue. To construct the theoretical models, displacements were
assumed to be in the forms:
u.sup.(i)=A.sub.i exp(ik.sub.i(x.sub.1 sin.theta..sub.i-x.sub.2
cos.theta..sub.i-c.sub.it))
[0044] where u.sup.(i) is the displacement of the ith wave, A.sub.i
is the amplitude of the displacement, k.sub.i is the wave number,
.theta..sub.i is the propagation angle with respect to the
perpendicular direction, and c.sub.i is the wave speed. These forms
of the displacements satisfy equilibrium conditions in both
continuum and nanomechanical models. Thus, the displacements
represent a complete solution under appropriate boundary and/or
continuity conditions.
[0045] Nanomechanics provides a framework for studying the node
level microstresses and microstrains, as revealed by relationships
between incident and reflected waves, that facilitates solving for
the reflection coefficients and for building a model that
facilitates real-time tissue screening. Under the theoretical
framework of nanomechanics, physical domains are composed of
discrete entities or nodes that are geometrical points relating to
each other through finite distances and specific orientations. In a
linear elastic context, node-level properties relating axial stress
and strain can be characterized by: 1 p = A
[0046] where p.sub..alpha. is the overall nodal stress in the
.alpha.-doublet, and .epsilon..sub..beta. is the axial nodal strain
associated with .beta.-doublet, and A.sub..alpha..beta. is the
micromodulus between nodes .alpha. and .beta.. Thus,
A.sub..alpha..beta.'s are the node-level counterparts of Lame's
constants in continuum mechanics. The strain in the .alpha.-doublet
can be computed as follows: 2 = i , j = 1 3 i j u i x j + 1 2 i , j
, k = 1 3 i j k 2 u i x j x k
[0047] where .epsilon..sub.a is the nodal strain associated with
node .alpha., .tau.'s are the direction cosines of the unit vectors
connecting two nodes, u.sub.1 is the displacement at x.sub.1
direction, u.sub.2 is the displacement at x.sub.2 direction, and
.eta..sub..alpha. is the internodal distance associated with node
.alpha.. .eta..sub..alpha. may be interpreted as the effective
radius of penetration of the mechanical contact forcing along the
.alpha. node direction.
[0048] Nanomechanics facilitates expressing macro (e.g., continuum)
stresses in terms of micro stresses and architecture. This feature
facilitates analyzing tissue microstructural characteristics based
on information measured at the macro-level. One example transition
relationship is: 3 ij = = 1 n ( i j p - 2 i j k p x k )
[0049] where .sigma..sub.ij is the symmetric, second-rank continuum
stress tensor.
[0050] The governing equation for elastic plane wave propagation
also takes a different format compared to continuum mechanics. The
non-scale (at scale one) wave equation presented here is
essentially equivalent to the equation in continuum mechanics At
scale two, the wave equations in nanomechanics are not equivalent
to those in continuum mechanics. For example, the scale two
equation incorporates the effect of the internodal distance a. It
also has a fourth order differential term for the displacement with
regard to spatial coordinates. 4 For scale 1 , = 1 n = 1 n A i j k
l 2 u i x l x j = 2 u i t 2 , Forscale2 , = 1 n = 1 n A [ i a k 1 j
p 1 2 u j x k 1 x p 1 - ( ) 2 4 i a k 1 a k 2 j p 1 p 2 4 u j x k 1
x k 2 x p 1 x p 2 ] = 2 u i t 2 ,
[0051] In continuum mechanics, the retrievable properties are
limited, by necessity, to macro elastic constants such as Young's
modulus E, the shear modulus .mu., and the corresponding
attenuation coefficients. Thus, information about tissue scaling
and micro-architecture is not provided through the continuum model.
In the nanomechanical reconstruction, the node-level elastic
constants A.sub..alpha..beta.(e.g., A.sub.11 and A.sub.44) and
their attenuation counterparts are reconstructed along with
parameters of the micro architecture (e.g., internodal distance
.eta.). In one example, the orientation of the nodes (the .tau.'s)
are defined to maintain a three dimensional isotropic arrangement
at the macro level. However, it is to be appreciated that other
orientations can be employed and/or reconstructed in accordance
with aspects of the present invention.
[0052] To facilitate solving for reflection coefficients and
building a model, an elastic, discrete-structured (granular) layer
sample of thickness d, was embedded between two infinite,
isotropic, elastic domains with perfect bonding as in FIG. 4. In
one experiment, thin tissue samples were embedded between glass
plates and subjected to incident ultrasonic waves with known
properties. The sample was then analyzed in an apparatus like that
illustrated in FIG. 2.
[0053] Referring to FIG. 2, a first ultrasonic transmitter/receiver
210 may generate a first ultrasonic wave 240 (or set of waves) that
is directed at a sample 270. The first wave(s) 240 will interact
with (e.g., reflect, transmit) the sample 270 producing waves that
can then be detected by, for example, the first
transmitter/receiver 210 and/or other receivers (e.g., receivers
220, 230). Similarly, a second ultrasonic transmitter/receiver 220
may generate a second ultrasonic wave 250 (or set of waves) that
interacts with the sample 270 and a third ultrasonic
transmitter/receiver 230 may generate a third ultrasonic wave 260
(or set of waves) that interacts with the sample 270. In one
example, 210 could be an ultrasonic transmitter only, 230 could be
an ultrasonic receiver only, and 220 could act as both transmitter
and receiver. While three transmitter/receivers are illustrated in
apparatus 200, it is to be appreciated that a greater and/or lesser
number of transmitter/receivers can be employed, and that other
components (e.g., separate transmitters and receivers) can also be
employed. Furthermore, waves generated by a first transmitter may
be received by one or more receivers. In one example, the
transmitter/receiver is a transducer, however, it is to be
appreciated that other ultrasonic transmitters, receivers, and/or
transmitter/receivers can be employed in accordance with aspects of
the present invention. Additionally, while oblique and normal
transmitters are illustrated, it is to be appreciated that the
transmitters and/or receivers can be arranged in a variety of
orientations (e.g., a ring of transducers, an array of
transducers).
[0054] Referring to FIG. 3, a time-harmonic plane wave 300 is
directed at a tissue layer at an angle .theta. from the upper
substrate to the structure. The incident wave 300 portions into
reflections 310 and transmissions 320 when it hits the first
material discontinuity (e.g., the upper interface between the thin
layer and the substrate, for example, the glass). The transmitted
waves 320 will further encounter the upper and lower interfaces
(substrate/tissue, tissue/substrate) and cause formation of a
series 340 of longitudinal and shear waves propagating up and down
within the tissue. Multiple reflections at the top and bottom
interfaces between tissue and substrates facilitate multiple waves
with a phase lag to propagate within the thin layer. The
constructive and destructive summations of these waves give rise to
characteristic reflection spectra measurable within a range of
frequencies.
[0055] As shown in FIG. 3, the reflected 310 and transmitted 330
waves propagate as longitudinal or shear waves with different
angles and velocities. The angles of reflection and transmission at
the interface are dictated by Snell's law, which holds that the
ratio of the wave number and the sine of the propagation angle
should remain constant at each interface. For example, at the upper
interface, the following relationships hold: 5 k 0 sin 0 = k 1 sin
1 = k 2 sin 2 = k 3 sin 3 = k 4 sin 4 = k 5 sin 5 = k 6 sin 6
[0056] where k.sub.i is the wave number of the ith wave and
.theta..sub.i is the propagation angle of the ith wave. Therefore,
there is only one possible angle for each type of wave
(longitudinal or shear) propagating in one direction (up or
down).
[0057] If the displacement vector of the incident wave 300 is known
(e.g., the amplitude and the incident angle are known), the eight
other waves in the system can be uniquely determined assuming the
material properties of the substrates and the layer are known.
Furthermore, if the mechanical bonds between the substrate and the
tissue are perfect, the following continuity conditions hold at
each interface: continuity of the normal displacement; continuity
of the normal stress; continuity of the shear displacement, and
continuity of the shear stress. Imposing these conditions, a system
of linear equations can be obtained, from which the unknown
magnitudes of wave displacements u.sup.(i) can be obtained. The
reflection/transmission coefficients, which are defined as the
ratios of magnitudes of the reflection/transmission wave over the
incident wave, can be computed according to:
R.sub.s(f)=M.sub.R(f)/M.sub.1(f)
[0058] where R.sub.s(f) is the reflection/transmission coefficient
at frequency f, M.sub.R(f) is the magnitude of the
reflection/transmission at frequency f, and M.sub.1(f) is the
magnitude of the incidence at frequency f. The reflection spectrum
is generated by computing the reflection coefficients for multiple
frequencies within a certain range, which is therefore a function
of the set of physical properties of the tissue.
[0059] In the continuum mechanics model, both the layers of the
substrates and the tissue were assumed to be isotropic and elastic
continua. But in the nanomechanics model, the thin layer of tissue
was represented as discrete nodes while the glass layers remained
as continua for simplicity. A spatial arrangement (see FIGS. 4 and
5) of the nodes was chosen to yield a three dimensional isotropic
medium at the macro scale. Within this micro architecture, each
node relates to six other nodes at each octant. Micro level
physical properties including, but not limited to, the internodal
distance .eta., the orientation vector .tau. and the micro elastic
constants A.sub.11 and A.sub.44 were specified for each pair of
nodes ("doublets"). The reflection spectra in nanomechanics were
thus obtained by specifying the micro level physical properties of
the tissue layer.
[0060] The reflection coefficient for the layer can be defined as
the magnitude of the ratio of the displacement of the reflected
wave 310 from the layer over that of the incident wave 300. These
equations facilitate analyzing the ratio: 6 R L = A 2 A 0 R S = A 1
A 0
[0061] where R.sub.L is the reflection coefficient of a reflected
longitudinal wave, and R.sub.S is the reflection coefficient of a
reflected shear wave. If the incident wave 300 is pulsed, (e.g.,
contains a range of frequency components), the reflection
coefficients corresponding to that range of frequency generate a
reflection spectrum. The reflection spectrum can then be analyzed,
stored, and characterized, for example.
[0062] For the purpose of characterizing the mechanical properties
of the thin layer with respect to its microstructural features, a
discrete-structured layer, is illustrated in FIG. 4. The layer is
identified by its density .rho., the micro elastic constants
A.sub..alpha..beta., and internodal distances (or particle
diameter).eta.. The substrates can be modeled with continuum
elasticity, and the corresponding material properties are density
p, and Lame's constants: .lambda. and .mu.. The thin layer can thus
be conceived of as comprising arrangements of nodes like
arrangement 400.
[0063] In one example, the continuum properties of malignant and
normal tissues did not differ in a statistically significant
fashion (P>0.05). However, the same set of experimental
measurements analyzed through the nanomechanical model yielded
parameters that differed in a statistically significant manner
(P<0.05) between adjacent normal and diseased tissue from the
same person. Significant parameters included both node-level
elastic constants A.sub.11 and A.sub.44, as well as the effective
internodal distance .eta.. Thus, a scanner being passed over a
tissue area may detect a boundary between tissue with different
mechanical characteristics, which facilitates identifying and/or
tagging pathologically interesting tissue areas and/or
locations.
[0064] FIG. 5 illustrates a particle arrangement as modeled by
nanomechanics at a micro-structural level. Nanomechanics assumes
material is composed of discrete nodes. The nodes are basically
geometrical points in space. They are related to each other with a
distance specified by .eta. and an orientation specified by vector
.tau.. The micro-level physical properties are governed by the
micro moduli matrix A.sub..alpha..beta.. Thus, FIG. 5 illustrates
an arrangement of particles that facilitate characterizing axial
constitutive relationships between micro-stress and micro-strain.
Once the tissue has been characterized, an inverse algorithm can be
applied to reconstruct quantitative information for parameters of
the tissue properties. These parameters can include, but are not
limited to, density, Young's modulus, and shear modulus for
continuum model, and density, micro elastic constants, tissue
micro-architecture and internodal distance, for nanomechanics. A
least square minimization method can be employed to search for
optimally estimated values on the parameters by solving: 7 min x i
R " 1 2 i = 1 m ( R i e - R i s ) 2
[0065] where x.sub.i's are the reconstructed parameters, n is the
number of the parameters to be found, m is the number of data
points at different frequencies, and R.sup.e and R.sup.s are the
experimental reflection coefficients and simulated reflection
coefficients, respectively.
[0066] Numerical analysis of the data acquired during the
experiments illustrates that by using the nanomechanics model,
statistically significant different mechanical responses can be
measured between normal and diseased tissues. For example, the
location of minima, the distance between minima, and the depth of
the minima are different between normal and diseased tissue. Since
different mechanical responses can be measured between normal and
diseased tissue, ultrasonic waves can be employed to distinguish
between such tissues.
[0067] Experiments employed to characterize non-continuum response
features that may be significant to developing a discrete model for
biological tissue can be carried out at various scales. In one
example, characterization may be reached by limiting considerations
to the approximation degree M=2. In one example, a simplified
version of governing equations can be derived with the following
assumption: that the particle interactions are longitudinal
(central), so that the shear and torsional microstresses vanish
everywhere. At different scales (e.g., M=1, M=2) the continuum
model and nanomechanical models yield similar, yet appreciably
differing results. As illustrated in FIG. 6, for a "large"
internodal distance (e.g., .eta.=8 .mu.m), reflection spectra are
appreciably different.
[0068] In a nanomechanics model, the continuum stresses are
directly derived from micro level physical and geometrical
parameters such as A.alpha..beta., .tau.'s and .eta.. Thus, the
macro level observable and/or measurable (e.g., reflection
coefficients from the thin layer) are directly related to micro
level parameters. The expressions for the displacement of waves in
the system can be written in the following format: 8 Incident S
wave : u ( 0 ) = { u 1 ( 0 ) u 2 ( 0 ) } = { A 0 cos 0 exp ( k 0 (
x 1 sin 0 - x 2 cos 0 - c 0 t ) ) A 0 sin 0 exp ( k 0 ( x 1 sin 0 -
x 2 cos 0 - c 0 t ) ) } Incident P wave : u ( 0 ) = { u 1 ( 0 ) u 2
( 0 ) } = { A 0 sin 0 exp ( k 0 ( x 1 sin 0 - x 2 cos 0 - c 0 t ) )
- A 0 cos 0 exp ( k 0 ( x 1 sin 0 - x 2 cos 0 - c 0 t ) ) }
Reflected S wave : u ( 1 ) = { u 1 ( 1 ) u 2 ( 1 ) } = { - A 1 cos
1 exp ( k 1 ( x 1 sin 1 + x 2 cos 1 - c 1 t ) ) A 1 sin 1 exp ( k 1
( x 1 sin 1 + x 2 cos 1 - c 1 t ) ) } Reflected P wave : u ( 2 ) =
{ u 1 ( 2 ) u 2 ( 2 ) } = { A 2 sin 2 exp ( k 2 ( x 1 sin 2 + x 2
cos 2 - c 2 t ) ) A 2 cos 2 exp ( k 2 ( x 1 sin 2 + x 2 cos 2 - c 2
t ) ) } Transmitted S wave in layer : u ( 3 ) = { u 1 ( 3 ) u 2 ( 3
) } = { A 3 cos 3 exp ( k 3 ( x 1 sin 3 - x 2 cos 3 - c 3 t ) ) A 3
sin 3 exp ( k 3 ( x 1 sin 3 - x 2 cos 3 - c 3 t ) ) } Transmitted P
wave in layer : u ( 4 ) = { u 1 ( 4 ) u 2 ( 4 ) } = { A 4 sin 4 exp
( k 4 ( x 1 sin 4 - x 2 cos 4 - c 4 t ) ) - A 4 cos 4 exp ( k 4 ( x
1 sin 4 - x 2 cos 4 - c 4 t ) ) } Reflected S wave in layer : u ( 5
) = { u 1 ( 5 ) u 2 ( 5 ) } = { - A 5 cos 3 exp ( k 3 ( x 1 sin 3 +
x 2 cos 3 - c 3 t ) ) A 5 sin 3 exp ( k 3 ( x 1 sin 3 + x 2 cos 3 -
c 3 t ) ) } Reflected P wave in layer : u ( 6 ) = { u 1 ( 6 ) u 2 (
6 ) } = { A 6 sin 4 exp ( k 4 ( x 1 sin 4 + x 2 cos 4 - c 4 t ) ) A
6 cos 4 exp ( k 4 ( x 1 sin 4 + x 2 cos 4 - c 4 t ) ) } Transmitted
S wave : u ( 7 ) = { u 1 ( 7 ) u 2 ( 7 ) } = { A 7 cos 0 exp ( k 0
( x 1 sin 0 - x 2 cos 0 - c 0 t ) ) A 7 sin 0 exp ( k 0 ( x 1 sin 0
- x 2 cos 0 - c 0 t ) ) } Transmitted P wave : u ( 8 ) = { u 1 ( 8
) u 2 ( 8 ) } = { A 8 sin 2 exp ( k 2 ( x 1 sin 2 - x 2 cos 0 - c 2
t ) ) - A 8 cos 2 exp ( k 2 ( x 1 sin 2 - x 2 cos 2 - c 2 t ) )
}
[0069] where u.sub.i.sup.(j) is the displacement associated with
the (j)th wave that propagates along x.sub.i axis.
[0070] The reflection coefficients may be solved for by enforcing
the following continuity conditions at x.sub.2=0 (Equations
(A)-(D)) and x.sub.2=d (Equations (E)-(H)): 9 Continuity of normal
displacement at x 2 = 0 : u 1 ( 0 ) + u 1 ( 1 ) + u 1 ( 2 ) = u 1 (
3 ) + u 1 ( 4 ) + u 1 ( 5 ) + u 1 ( 6 ) ( A ) Continuity of normal
stress at x 2 = 0 : 22 ( 0 ) + 22 ( 1 ) + 22 ( 2 ) = 22 ( 3 ) + 22
( 4 ) + 22 ( 5 ) + 22 ( 6 ) ( B ) Continuity of shear displacement
at x 2 = 0 : u 2 ( 0 ) + u 2 ( 1 ) + u 2 ( 2 ) = u 2 ( 3 ) + u 2 (
4 ) + u 2 ( 5 ) + u 2 ( 6 ) ( C ) Continuity of shear stress at x 2
= 0 : 21 ( 0 ) + 21 ( 1 ) + 21 ( 2 ) = 21 ( 3 ) + 21 ( 4 ) + 21 ( 5
) + 21 ( 6 ) ( D ) Continuity of normal displacement at x 2 = - d :
u 1 ( 7 ) + u 1 ( 8 ) = u 1 ( 3 ) + u 1 ( 4 ) + u 1 ( 5 ) + u 1 ( 6
) ( E ) Continuity of normal stress at x 2 = - d : 22 ( 7 ) + 22 (
8 ) = 22 ( 3 ) + 22 ( 4 ) + 22 ( 5 ) + 22 ( 6 ) ( F ) Continuity of
shear displacement at x 2 = - d : u 2 ( 7 ) + u 2 ( 8 ) = u 2 ( 3 )
+ u 2 ( 4 ) + u 2 ( 5 ) + u 2 ( 6 ) ( G ) Continuity of shear
stress at x 2 = - d : 21 ( 7 ) + 21 ( 8 ) = 21 ( 3 ) + 21 ( 4 ) +
21 ( 5 ) + 21 ( 6 ) ( H )
[0071] where .sigma..sup.(n).sub.ij is the stress associated with
the nth wave and u.sup.(n).sub.m is the displacement associated
with the nth wave. The above boundary conditions give an 8.times.8
matrix by which the reflection coefficients (R.sub.L or R.sub.S)
can be solved for assuming the incident wave 300 and the material
properties of the substrate and the thin layer are known. Once the
reflection coefficients have been solved for, they can be stored in
a computer data store, alone and/or with other wave and/or tissue
data, in a model. The model facilitates distinguishing tissue based
on their reflection properties (e.g., coefficients, spectra).
[0072] Numerical solutions for a nanomechanics modeling problem can
be obtained by employing the example arrangement illustrated in
FIG. 5. This sample arrangement results in three dimensional
macroscopic isotropy if the order of the scale is chosen to be one
(M=1). It also reduces the number of the independent micromoduli in
the example to two: A.sub.11 and A.sub.44.
[0073] Given the arrangement in FIG. 5, a direction cosine matrix
can be computed:
[0074] .tau..sub.1=(1,0,0) .tau..sub.4=(0,1/{square root}{square
root over (2)},1/{square root}{square root over (2)})
[0075] .tau..sub.2=(0,1,0) .tau..sub.5=(1/{square root}{square root
over (2)},0,1/{square root}{square root over (2)})
[0076] .tau..sub.3=(0,0,1) .tau..sub.6=(1{square root}{square root
over (2)},1/{square root over (2)},0)
[0077] The micromoduli of a tissue are estimated from macro elastic
moduli based on the fact that the multi-scale model reduces to the
continuum model when the scale factor is equal to one. The
micromoduli are related to Lame's constants as follows:
[0078] .lambda.=A.sub.11-A.sub.44 10 = 1 4 A 44
[0079] In one example, the values of the macro elastic moduli of
the thin tissue layer were adopted from the averaged values for
human breast tissue. In the example, it was also hypothesized that
the dimension of the nodes in the discrete model for the biological
tissue corresponds to that of the cells. Additionally, the example
assumed that the cells were close-packed so that the internodal
distance was equivalent to the cell diameter. The typical dimension
of human breast epithelial cells is at the 10-.mu.m scale. The
example further assumed that the internodal distances were the same
for substantially all doublets. Therefore .eta. is at the scale of
10-.mu.m. Therefore, in one example it can be assumed that the set
of parameters for the biological tissue thin layer as follows:
[0080] Density: 1.0 g/cm.sup.3
[0081] A.sub.11=3.0 GPa
[0082] A.sub.44=0.5 GPa
[0083] .eta.=1.about.10-.mu.m
[0084] In one example, the thickness of the thin layer was assumed
to be 150-.mu.m, which is thin enough to be considered a "thin"
layer compared to the dimension of the substrates (glass slides)
and thick enough to accommodate several nodes (cells) cross the
thickness. The incident angle can be a variety of arbitrary angles
if the angle's magnitude is between those of the two mode
conversion angles for the glass-tissue interface.
[0085] The nanomechanics model offers the opportunity to correlate
the response of a medium to its microstructural characteristics.
For example, if the size of the particles is varied while other
properties remain the same, a change in the reflection coefficient
is observed, as illustrated in FIG. 9. Thus, the nanomechanics
model gives insight to the upper limit of the size of the particles
before they become "visible" for elastic waves propagating at a
certain range of frequencies.
[0086] In one experiment, with other parameters fixed, a
micromodulus A.sub..alpha..beta. (e.g., A.sub.11) is varied to
study its effect on the reflection spectrum. FIG. 7 illustrates
that an increase in micromodulus A.sub.11 results in shifting of
the overall spectrum to the right (higher frequency), and vice
versa. The changes in micromodulus A.sub.11 change the location of
the minima in the curves. Nevertheless, the magnitude of the minima
and the distance between the minima remain unaffected. Similarly
the effect of changing another modulus A.sub..alpha..beta. (e.g.,
A.sub.44) is studied by varying its magnitude. The result is shown
in FIG. 8. FIG. 8 shows that A.sub.44 affects the overall
reflection spectra less than compared to A.sub.11. In other words,
A.sub.44 is a less sensitive parameter in terms of determining the
reflection spectrum. But, FIG. 8 does show that increase in
micromodulus A.sub.44 results in shifting of the second minimum to
the left (lower frequency), and vice versa. Therefore changes in
micromodulus A.sub.44 change the distance between the two minima,
and also the magnitude of the minima. Therefore, systems and
methods for screening tissue can employ a response model that
stores information associated with micromodulus changes to
facilitate screening for (un)healthy tissue.
[0087] While the micromodulus effects on reflected waves can be
employed to distinguish tissue, so too can the effects on reflected
waves due to particle size, or internodal distance related to cell
size and micro-architecture be employed to distinguish tissue. In
one experiment employed to develop a model, the particle size of
the thin layer is varied to study its effect on the reflection
spectrum and to facilitate solving for reflection coefficients.
These properties (e.g., micromodulus, particle size) affect the
reflection spectra. The reflection spectra are fully defined by the
properties of the tissue. Therefore, the properties can be
determined through inversion from the experimental data in
combination with a response model.
[0088] At higher frequency ranges (e.g., ultrasonic) particle size
has an effect on the reflection spectrum by shifting it to the left
with increased magnitude of the particle size as illustrated in
FIG. 9. This effect is similar to that of micromodulus A.sub.11,
however, the effects of the two parameters differ. Changes in
micromodulus A.sub.11 have the same effect on the reflection
spectra regardless of the frequency range, while the effect of
particle size is frequency dependent.
[0089] FIG. 10 illustrates translations of the doublet nodes
.alpha. and b.sub..alpha.. .eta..sub..alpha. is the distance
between the two doublet nodes, .tau..sub..alpha. is the unit vector
along the original direction from node a to b.sub..alpha., and
.zeta..sub..alpha. is the new direction vector after the
deformation. The nanomechanical model assumes that particle
displacements vary little at the lengths on the order of their
separations. Thus, in one example model, a smooth vector field of
the translation function u(X,t) is employed, where X is the
position vector of an arbitrary point in the body and t is time.
The vector field of the translation displacement is assumed to
coincide with the real translation of the granular body particles
at the node a, where X=x.
[0090] An incremental vector .DELTA.u.sub..alpha. is introduced,
which is defined as:
.DELTA.u.sub..alpha.=u(x+.zeta..sub.60 ,t)-u(x, t)
[0091] which represents an increment of the translation vector u in
a transition from all arbitrary node a to the adjacent node
b.sub..alpha.. The increment vector may be expanded in a convergent
Taylor series in a neighborhood of an arbitrary node .alpha. whose
position vector is x. Truncating this series at the M-th term
yields: 11 u = = 1 M ( ) ! ( ) u ( x , t )
[0092] when X=x.
[0093] Based on the above assumptions, the axial microstrain is: 12
= i = 1 M ( ) - 1 ! k 1 k u i x k 1 x k
[0094] Thus, the first approximation (M=1) for the axial
microstrain takes the form:
[0095]
.epsilon..sub..alpha.=.tau..sub..alpha.i.tau..sub..alpha.j.epsilon.-
.sub.ij
[0096] where 13 ij = 1 2 ( u i x j + u j x i )
[0097] And the second approximation (M=2) takes the form: 14 = i j
u i x j + 1 2 i j k 2 u i x j x k
[0098] in expansion, it becomes: 15 = 1 2 u 1 x 1 + 1 2 ( u 1 x 2 +
u 2 x 1 ) + 2 2 u 2 x 2 + 2 ( 1 3 2 u 1 x 1 2 + 2 1 2 2 2 u 1 x 1 x
2 + 2 2 1 2 u 1 x 2 2 + 1 2 2 2 u 2 x 1 2 + 2 2 2 1 2 u 2 x 1 x 2 +
2 3 2 u 2 x 2 2 )
[0099] Thus, microstress p.sub..alpha. that is associated with
.epsilon..sub..alpha. is defined and a microstress-microstrain
constitutive relationship can be examined via: 16 p = = 1 n A
[0100] where A.sub..alpha..beta. are the micro-level elastic
moduli. The transition from microstresses to macrostresses is
achieved by applying equilibrium equations and the resulting
relationship is: 17 k 1 t ( M ) = = 1 n k 1 = 1 M ( - 1 ) + 1 ( ) -
1 ! k 2 k - 1 ( p i ) x k 2 x k
[0101] The first approximation (M=1) for stress takes the form: 18
ij = = 1 n j c p
[0102] It is further derived that the macromoduli for the M=1 case
becomes: 19 C ijkl = , = 1 n A i j k l
[0103] The second approximation (M=2) for continuum stress takes
the form: 20 ij = = 1 n j ( i p - 1 2 k k p x k )
[0104] Thus, systems and methods that screen tissue, substantially
in real-time, can rely on a reflection coefficients and/or
biomechanical response model based on the approximations,
equations, assumptions, and experimental results detailed above. It
is to be appreciated that the approximations, equations,
assumptions and experimental results described above illustrate
examples that can be employed to characterize the mechanical
response of tissue to ultrasonic waves. Accordingly, other systems
that rely on other models that characterize the mechanical response
of tissue to ultrasonic waves, where the models are based on
solving for reflective coefficients and nanomechanics are
contemplated. In one example, a tissue mechanical properties model
stores information concerning one or more of, tissue reflection,
tissue transmission, tissue elasticity, tissue particle size,
tissue micromoduli, tissue micro-architecture, and tissue
mechanical responses.
[0105] One sample tissue screening system 1100 is illustrated in
FIG. 11. The system 1100 includes an ultrasonic wave producer 1110
that produces an ultrasonic wave 1120 that is directed at a tissue
sample 1130 that is to be screened. The system 1100 also includes
an ultrasonic wave receiver 1150 that receives ultrasonic waves
1140. The ultrasonic waves 1140 are produced by the ultrasonic wave
1120 interacting with the tissue sample 1130. Thus the waves 1140
can be, for example, reflected and/or transmitted waves. As
described above, different tissues with different mechanical
properties due to their state of (un)healthiness will interact
differently with incident waves and lead to differences in
reflected waves, which facilitates screening for diseased tissue.
The system 1100 also includes an analyzer 1160 operably connected
to the ultrasonic wave producer 1110 and/or the ultrasonic wave
receiver 1150. The analyzer 1160 differentiates tissue regions in
the tissue sample 1130 by, at least in part, analyzing one or more
parameters of the ultrasonic waves 1140 and/or the ultrasonic waves
1120. For example, the analyzer 1160 may analyze one or more of an
incident angle of wave 1120, a reflected angle of wave 1140, a
reflection spectrum, and/or a relationship between them. While the
example system 1100 has one producer 1110, one receiver 1150, and
one analyzer 1160 illustrated, it is to be appreciated that a
greater number of such components can be employed in other
examples. In one example, the analyzer 1160 is a computer
component.
[0106] In one example, the system 1100 can include a tissue
mechanical properties model 1170 in data communication with the
analyzer 1160. The tissue mechanical properties model 1170 can
store information concerning, but not limited to, tissue
reflection, tissue transmission, tissue elasticity, tissue particle
size, tissue micromoduli, tissue micro-architecture, and reflection
coefficients of normal and abnormal tissues. The model 1170 can be
based on theories including, but not limited to, nanomechanics. The
tissue sample 1130 can be, for example, from an external tissue
surface (e.g., skin), an internal tissue surface (e.g., stomach
lining), human tissue, animal tissue, and so on.
[0107] Another example system 1200 is illustrated in FIG. 12. The
system 1200 includes a wand 1220 that can be passed over a tissue
sample. The tissue sample is illustrated as having three
representative sections, 1240, 1250 and 1260. As can be seen,
sections 1240 and 1260 are normal, healthy tissue, and are likely
to produce a similar set of reflected waves due to their similar
nanomechanical properties. However, section 1250 is illustrated as
being affected by a malignant disease (e.g., invasive ductal
carcinoma), and thus will likely produce a set of reflected waves
that differ from those produced when the wand 1220 sends waves 1230
over sections 1240 and 1260. Thus, reflected waves received by the
wand 1220 may be processed by an analyzer 1210 that can identify
the locations in the tissue sample where the reflected waves
changed from one set of properties to a second set and back again.
Based on such analysis and identification, a pathologically
interesting area can be tagged for subsequent analysis. The tagging
can include, but is not limited to, recording coordinates of the
tissue where the waves changed, changing the physical appearance of
an image generated from the reflected waves, raising an alarm so
that an operator can manually mark the location of the changes, and
so on. It is to be appreciated that other approaches to "tagging"
an identified tissue area can be employed in accordance with
aspects of the systems and methods described herein. While FIG. 12
illustrates a wand 1220 being passed over a tissue sample, it is to
be appreciated that other physical arrangements of wave
transmitter/receiver and tissue sample can be employed in
accordance with aspects of the present invention. For example, a
portable system may be arranged so that a patient could pass a
tissue area (e.g., mole on hand) over the system, or a portable
system may be arranged so that it can be passed over a tissue area
(e.g., breast).
[0108] In one example, the tissue may be treated with a
nanoparticle contrast agent. The nanoparticle contrast agent can be
prepared so that a higher concentration of the agent would localize
in, for example, section 1250, with a lower concentration in
sections 1240 and 1260. Thus, the analyzer 1210 could more easily
distinguish healthy from malignant tissue.
[0109] FIG. 13 illustrates a series 1300 of example sampling times
and locations employed in a method for screening tissue. At a first
time T.sub.1, a wave generator may be positioned so that a first
wave is directed at a sample 1310 at a location X.sub.1. Later, at
a second time T.sub.2, the wave generator may be positioned (or the
tissue may be moved) so that a second wave is directed at a sample
1320 at a location X.sub.2. Similarly, tissue may be sampled at
other times (e.g., T.sub.3 through T.sub.7) and other locations
(e.g., X.sub.3 through X.sub.7). As described above, the reflection
of the waves is likely to differ based on the mechanical properties
of the tissue with which the wave interacts. Thus, as the tissue is
scanned from left to right, an analyzer could identify the location
of reflection changes, which facilitates marking pathologically
interesting areas. Furthermore, if the waves received at various
times and locations is stored, then when the same tissue sample is
analyzed at a later point in time, differences between waves, and
changes between reflections, can be identified. This facilitates,
for example, tracking mole growth.
[0110] While the waves are illustrated being directed normal to the
surface of the tissue sample, it is to be appreciated that the
waves may be directed at various angles. For example, waves may be
directed at a tissue sample located between glass slides immersed
in water so that the incident angle is an angle between the two
mode conversion angles of the glass slides and the fluid in which
the slide is immersed. Or, in another example, the incident angle
may be between 15 and 20 degrees. Furthermore, waves of various
frequencies can be employed. For example, a first ultrasonic pulse
may be in the range of 2.5 to 12.5 MHz, with a central frequency of
7.5 MHz, while another ultrasonic wave may be in the range of about
7 to about 13 MHz.
[0111] In view of the exemplary systems shown and described herein,
example methodologies that are implemented will be better
appreciated with reference to the flow diagrams of FIGS. 14, 15,
and 16. While for purposes of simplicity of explanation, the
illustrated methodologies are shown and described as a series of
blocks, it is to be appreciated that the methodologies are not
limited by the order of the blocks, as some blocks can occur in
different orders and/or concurrently with other blocks from that
shown and described. Moreover, less than all the illustrated blocks
may be required to implement an example methodology. Furthermore,
additional and/or alternative methodologies can employ additional,
not illustrated blocks. In one example, methodologies are
implemented as computer executable instructions and/or operations,
stored on computer readable media including, but not limited to an
application specific integrated circuit (ASIC), a compact disc
(CD), a digital versatile disk (DVD), a random access memory (RAM),
a read only memory (ROM), a programmable read only memory (PROM),
an electronically erasable programmable read only memory (EEPROM),
a disk, a carrier wave, and a memory stick.
[0112] In the flow diagrams, rectangular blocks denote "processing
blocks" that may be implemented, for example, in software.
Similarly, the diamond shaped blocks denote "decision blocks" or
"flow control blocks" that may also be implemented, for example, in
software. Alternatively, and/or additionally, the processing and
decision blocks can be implemented in functionally equivalent
circuits like a digital signal processor (DSP), an ASIC, and the
like.
[0113] A flow diagram does not depict syntax for any particular
programming language, methodology, or style (e.g., procedural,
object-oriented). Rather, a flow diagram illustrates functional
information one skilled in the art may employ to program software,
design circuits, and so on. It is to be appreciated that in some
examples, program elements like temporary variables, routine loops,
and so on are not shown.
[0114] FIG. 14 illustrates an example method 1400 for screening
tissue. The method 1400 includes, at 1410, directing an ultrasonic
wave at a tissue to be screened. The waves may be, for example,
waves with frequencies between 3 and 13 MHz, with a center
frequency of 8 MHz. It is to be appreciated that waves with other
bandwidths and center frequencies can be employed in accordance
with aspects of the present invention. The method 1400 includes, at
1420, receiving one or more second ultrasonic waves produced by the
first ultrasonic wave interacting with the tissue to be screened.
The second ultrasonic waves may include, for example, reflected
waves, transmitted waves, and the like.
[0115] The method 1400 includes, at 1430, analyzing one or more
parameters associated with the second ultrasonic waves in the
context of a tissue mechanical properties model. The parameters can
include, but are not limited to, tissue reflection, tissue
transmission, tissue elasticity, tissue particle size, tissue
micromoduli, tissue micro-architecture, and tissue mechanical
response properties. Furthermore, the waves can be analyzed as they
relate to the first ultrasonic waves of 1410.
[0116] The method 1400 also includes, at 1440, determining whether
an area of the tissue to be screened should be tagged. Determining
whether an area should be tagged may include analyzing the results
of analyzing the wave parameters. If the determination at 1440 is
YES, then at 1450, the area can be tagged, otherwise processing can
conclude.
[0117] FIG. 15 illustrates an example method 1500 for screening
tissue, where a nanoparticle contrast agent is associated with the
tissue to be screened before an ultrasonic wave is directed at the
tissue. The ability of a scanner being passed over a tissue area to
detect a boundary between tissue with different mechanical
characteristics can be enhanced by biologically targeted micro- or
nano-particles. The particles can be employed as mechanical signal
amplifiers, or nanomechanical `smart contrast agents`. Particles
may be conjugated to biologically targeting agents with pronounced
affinity for molecular biomarkers associated with pathologies of
interest. The particles facilitate maximizing nanomechanical
contrast with respect to normal adjacent tissue, which in turn
facilitates detecting the boundary between tissue with different
mechanical characteristics.
[0118] At 1510, the tissue is treated with a nanoparticle contrast
agent. By way of illustration, particles of certain sizes (e.g.,
<10 microns) may be injected intravenously. Tumor marker
targeting nanoparticles in this size range may then be employed as
ultrasound contrast agents for in vivo molecular imaging. In one
example, particles may be coated with an antibody that targets a
biomarker on a cancer and/or an angiogenic blood vessel, which
facilitates concentrating the nanoparticles in pathologically
interesting areas. The material of the particles could be bioglass
and/or silicon, or other materials with a Young's modulus greater
than normal and/or malignant tissue. In one example, the
nanoparticles may also be void (e.g., with air inside) which will
also enhance the contrast.
[0119] At 1520, an ultrasonic wave is directed at a tissue to be
screened. The waves may be, for example, waves with frequencies
between 3 and 13 MHz, with a center frequency of 8 MHz. It is to be
appreciated that waves with other bandwidths and center frequencies
can be employed in accordance with aspects of the present
invention. The method 1500 includes, at 1530, receiving one or more
second ultrasonic waves produced by the first ultrasonic wave
interacting with the tissue to be screened. The second ultrasonic
waves may include, for example, reflected waves, transmitted waves,
and the like.
[0120] At 1540, the method 1500 includes analyzing wave parameters.
The wave parameters can include, but are not limited to, tissue
reflection, tissue transmission, tissue elasticity, tissue particle
size, tissue micromoduli, tissue micro-architecture, and tissue
mechanical response properties.
[0121] At 1550, the method 1500 also includes determining whether
an area of the tissue to be screened should be tagged. If the
determination at 1550 is YES, then at 1560 the area can be tagged.
Subsequently, at 1570, a determination can be made concerning
whether another area should be analyzed. If the determination is
YES, then processing returns to 1520, otherwise processing can
conclude.
[0122] FIG. 16 illustrates an example tissue area mapping method
1700. One example area that can be mapped is a mole. The method
includes, at 1710, identifying first data values for a set of
tissue area parameters. The first data values are acquired by
processing a first resultant ultrasonic wave received from a tissue
area, where the first resultant ultrasonic wave is the result of a
first incident ultrasonic wave interacting with the tissue area.
Thus, a first wave or set of waves is directed at a tissue sample,
the resulting waves are acquired, and data values associated with
the incident and/or reflected waves are generated. Then, at 1720,
the first data values are stored.
[0123] At a later time, at 1730, the method 1700 includes
identifying one or more second data values for the set of tissue
area parameters. The second data values are acquired, for example,
by directing ultrasonic waves at the same tissue sample and
collecting reflected waves. Data values are then generated by
processing the second resultant ultrasonic wave that is the result
of a second incident ultrasonic wave interacting with the tissue
area. In this way, two sets of data concerning the same tissue area
can be acquired. This facilitates, for example, mole mapping,
through comparing the first and second set of data values.
[0124] The method 1700 includes, at 1740, selectively tagging a
pathologically interesting tissue location in the tissue area
based, at least in part, on analyzing tissue mechanical properties
discernible by analyzing one or more of the first data values, the
second data values, and relations between the first and second data
values. Thus, by comparing values acquired at a first point in time
with values acquired at a second point in time, differences between
the data values can be ascertained. These differences may indicate
a pathologically significant event, and thus the area where the
differences are noted may be tagged for further processing.
[0125] At 1750, a decision is made concerning whether to continue
mapping an area. If the determination at 1750 is YES, then
processing returns to 1710, otherwise processing can conclude.
While two data sets are acquired in 1700, it is to be appreciated
that a greater number of data sets can be acquired. While such data
sets may be acquired at varying points in time, pathologically
relevant periods of time (e.g. six months) may elapse between
gathering the data sets.
[0126] In one example, analyzing the first data values and the
second data values includes analyzing properties including, but not
limited to, tissue reflection, tissue transmission, tissue
elasticity, tissue particle size, tissue micromoduli, and tissue
mechanical response properties.
[0127] FIG. 17 illustrates a computer 1800 that includes a
processor 1802, a memory 1804, a disk 1806, input/output ports
1810, and a network interface 1812 operably connected by a bus
1808. Executable components of the systems described herein may be
located on a computer like computer 1800. Similarly, computer
executable methods described herein may be performed on a computer
like computer 1800. It is to be appreciated that other computers
may also be employed with the systems and methods described herein.
The processor 1802 can be a variety of various processors including
dual microprocessor and other multi-processor architectures.
[0128] The memory 1804 can include volatile memory and/or
non-volatile memory. The non-volatile memory can include, but is
not limited to, ROM, PROM, EPROM, electrically erasable
programmable read only memory (EEPROM), and the like. Volatile
memory can include, for example, RAM, synchronous RAM (SRAM),
dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate
SDRAM (DDR SDRAM), and direct RAM bus RAM (DRRAM). The disk 1806
can include, but is not limited to, devices like a magnetic disk
drive, a floppy disk drive, a tape drive, a Zip drive, a flash
memory card, and/or a memory stick. Furthermore, the disk 1806 can
include optical drives like, compact disk ROM (CD-ROM), a CD
recordable drive (CD-R drive), a CD rewriteable drive (CD-RW drive)
and/or a digital versatile ROM drive (DVD ROM). The memory 1804 can
store processes 1814 and/or data 1816, for example. The disk 1806
and/or memory 1804 can store an operating system that controls and
allocates resources of the computer 1800.
[0129] The bus 1808 can be a single internal bus interconnect
architecture and/or other bus architectures. The bus 1808 can be of
a variety of types including, but not limited to, a memory bus or
memory controller, a peripheral bus or external bus, and/or a local
bus. The local bus can be of varieties including, but not limited
to, an industrial standard architecture (ISA) bus, a microchannel
architecture (MSA) bus, an extended ISA (EISA) bus, a peripheral
component interconnect (PCI) bus, a universal serial (USB) bus, and
a small computer systems interface (SCSI) bus.
[0130] The computer 1800 interacts with input/output devices 1818
via input/output ports 1810. Input/output devices 1818 can include,
but are not limited to, a keyboard, a microphone, a pointing and
selection device, cameras, video cards, displays, and the like. The
input/output ports 1810 can include but are not limited to, serial
ports, parallel ports, and USB ports.
[0131] The computer 1800 can operate in a network environment and
thus is connected to a network 1820 by a network interface 1812.
Through the network 1820, the computer 1800 may be logically
connected to a remote computer 1822. The network 1820 includes, but
is not limited to, local area networks (LAN), wide area networks
(WAN), and other networks. The network interface 1812 can connect
to local area network technologies including, but not limited to,
fiber distributed data interface (FDDI), copper distributed data
interface (CDDI), ethernet/IEEE 802.3, token ring/IEEE 802.5, and
the like. Similarly, the network interface 1812 can connect to wide
area network technologies including, but not limited to, point to
point links, and circuit switching networks like integrated
services digital networks (ISDN), packet switching networks, and
digital subscriber lines (DSL).
[0132] The systems, methods, and objects described herein may be
stored, for example, on a computer readable media. Media can
include, but are not limited to, an ASIC, a CD, a DVD, a RAM, a
ROM, a PROM, a disk, a carrier wave, a memory stick, and the like.
Thus, an example computer readable medium can store computer
executable instructions for a method for screening tissue. The
method can include directing an ultrasonic wave at a tissue to be
screened, receiving a second ultrasonic wave produced by the first
ultrasonic wave interacting with the tissue to be screened, and
determining whether an area of the tissue to be screened should be
tagged. The determining can include analyzing a parameter
associated with the second ultrasonic waves in the context of a
tissue mechanical properties model. In one example, the method can
also include associating a nanoparticle contrast agent with the
tissue to be screened.
[0133] Furthermore, a computer readable medium may store a data
structure associated with a biomechanical response model and/or a
reflection coefficient model. An example model may include, a first
field that stores information associated with tissue reflection
properties. For example, the tissue reflection properties may
include, but are not limited to, reflection coefficients, or
reflection spectra. The model may also include a second field that
stores information associated with tissue health properties. For
example, the tissue health properties may include, but are not
limited to, status, degree of invasion, and the like. The model may
also include a third field that stores correlation information
associated with correlating the tissue reflection properties of the
first field and the tissue health properties of the second field.
Thus, the model can facilitate retrieving a tissue health property
given a tissue reflection property, and/or can facilitate
retrieving a tissue reflection property given a tissue health
property.
[0134] Similarly, an example computer readable medium can store
computer executable components of a tissue screening system. The
tissue screening system can include, for example, an ultrasonic
wave producer that produces a first ultrasonic wave that is
directed at a tissue to be screened, an ultrasonic wave receiver
that receives one or more second ultrasonic waves, where the second
ultrasonic waves are produced by the first ultrasonic wave
interacting with the tissue to be screened, and an analyzer
operably connected to one or more of the ultrasonic wave producer
and the ultrasonic wave receiver, where the analyzer differentiates
tissue regions in the tissue to be screened based, at least in
part, on analyzing one or more relationships between the first
ultrasonic wave and the second ultrasonic wave. In another example,
the system may include a tissue mechanical properties model in data
communication with the analyzer.
[0135] Computer readable media may be employed in a system like
that illustrated in FIG. 18. The elements of an example basic
ultrasound nondestructive evaluation system 1900 are shown in FIG.
18. Such a nondestructive evaluation system 1900 could be employed,
for example, in developing a nanomechanical bioresponse model, a
reflection coefficient model, and/or in an automated slide reader.
The energy-provider of the system 1900 is the pulser section 1930
of a pulser-receiver. The pulser 1930 may generate short (e.g., 0.1
.mu.sec), repetitive (e.g., 1 msec apart) electrical pulses. The
electrical pulses may be, for example, on the order of several
hundred volts, and they drive a transducer 1920 (e.g.,
piezoelectric material) to produce mechanical vibrations, which
then transmit as a beam of ultrasound in media.
[0136] If the ultrasound beam hits a material discontinuity while
propagating in a medium, a portion of the energy will be
reflected/scattered and transmitted along another direction of wave
propagation. The reflected/scattered or transmitted signals can be
detected by a receiving transducer 1950, which transforms
mechanical pulses into electrical pulses. The receiver transducer
1950 can transform mechanical energy into electrical energy, which
is then processed by receiver 1940.
[0137] The electrical energy transferred from ultrasound vibrations
is usually small, for example, on the order of 0.001 volt. The
electrical energy can be amplified, for example, to the order of 1
volt through an amplifier. The waves of the electrical pulses can
be displayed as a voltage versus time trace on an oscilloscope or
the screen of a computer 1970.
[0138] Further processing and quantitative evaluation on the
received signals is facilitated by capturing and storing the
received signals. This is achieved through a digitizer 1960 (e.g.,
digital oscilloscope, external digitizer with high sampling rate
(e.g., above 100 MHz)) to preserve details in the signals. Once in
the digital form, the signals can be readily stored in the computer
1970 for analysis.
[0139] Systems like those described in FIG. 18 can be generically
classified as input-output systems. Thus, FIG. 19 illustrates an
example of a generic input-output system 2000, where the system
2000 takes input i(t) to produce an output o(t). Both i(t) and o(t)
are functions of time t. One example ultrasonic measurement system
for biological tissue samples may have components that are
themselves complex electromechanical systems. A simplification can
be achieved by treating the components, and therefore the overall
system, as Linear Time-Shift Invariant (LTI) systems, where
o(t)=L[i(t)]
[0140] The linearity of the system depends on:
o(t)=L(c.sub.1i.sub.1(t)+c.sub.2i.sub.2(t)]=c.sub.1L[i.sub.1(t)]+c.sub.2L[-
i.sub.2(t)]
[0141] where i.sub.1 and i.sub.2 are two arbitrary inputs and
c.sub.1 and c.sub.2 are two arbitrary constants.
[0142] The time-shift invariant property of LTI system depends
on:
o(t-t.sub.0)=L[i(t-t.sub.0)]
[0143] which means that a delay in the input signal produces a
corresponding delay in the output.
[0144] For a system comprised of multiple LTI systems, (e.g.,
input-output systems 2100, 2110, 2120), like the system illustrated
in FIG. 20, the overall response in time-domain is determined by
the convolution of the impulse response of each component:
o(t)=g.sub.1(t)*g.sub.2(t)* . . . *g.sub.n(t)*i(t)
[0145] where g.sub.k(t) is the impulse response of the kth
component.
[0146] Convolution in time-domain is equivalent to product in
frequency domain. A useful technique in analyzing such a system is
Fourier Transformation that describes the responses in terms of the
decomposition of a pulse (time domain signal) into a distribution
of sinusoids with different magnitudes at different frequencies.
Through Fourier transformation, the response of the overall system
can be obtained from products of the component responses rather
than from complicated multiple convolution integrals in time
domain.
O(.omega.)=G.sub.1(.omega.)G.sub.2(.omega.) . . .
G.sub.n(.omega.)I(.omega- .)
[0147] One example input-output system may be an automated
pathology slide reader. The slider reader may include a slide
holder for holding a slide on which tissue is located, and an
ultrasound wave generator for producing incident ultrasonic waves
that are directed at the slide at an incident angle. The slide may
include, for example, a tissue sample sandwiched between two plates
of substrates (e.g., glass). In one example, the slide may be
immersed in a fluid (e.g., water) when it is subjected to
ultrasonic wave analysis. The system may also include an ultrasound
wave receiver for receiving a reflected ultrasonic wave produced by
the incident wave interacting with the tissue on the slide, where
the reflected ultrasonic wave is reflected from the slide at a
reflection angle. The system may also include a comparison computer
component for comparing data associated with items including, but
not limited to, the incident angle, the reflected angle, the
reflection spectrum, the incident wave, and the reflected wave. The
comparison computer component, and/or other computer components may
also determine one or more tissue properties based, at least in
part, on the analyzed data in the context of a biomechanical
response model.
[0148] In one example, the slide reader may be configured so that
the incident angle is an angle between the two mode conversion
angles of the first layers and the fluid in which the slide is
immersed. In another example, the slide reader may be configured so
that the incident angle is between 15 and 20 degrees.
[0149] The systems and methods described herein can be employed to
build models that facilitate characterizing tissue responses to
ultrasonic waves. One method for building a model that
characterizes tissue response to ultrasonic waves includes
acquiring a set of known normal tissue samples. For example,
healthy tissue can be collected from a patient or set of patients.
The method then includes analyzing the set of normal tissue samples
employing ultrasonic waves and a nanomechanical representation of
the normal tissue samples to produce a first analysis data. The
first analysis data can include, for example, reflection
coefficients, reflection spectra, elasticity information, tissue
micro-architecture information, and so on. The method then includes
acquiring a set of known malignant tissue samples and analyzing the
set of known malignant tissue samples employing ultrasonic waves
and a nanomechanical representation of the malignant tissue samples
to produce a second analysis data. The second analysis data can
include, for example, reflection coefficients, reflection spectra,
elasticity information, tissue micro-architecture information, and
the like.
[0150] The method also includes characterizing tissue based, at
least in part, on the first analysis data, the second analysis
data, and the nanomechanical representation of tissue. The
characterization can produce a characterization data that can be
stored in a computer data store and/or processed by a computer
component. The method also includes building a model that stores
data including, but not limited to, the first analysis data, the
second analysis data, and the characterization data.
[0151] What has been described above includes several examples. It
is, of course, not possible to describe every conceivable
combination of components or methodologies for purposes of
describing the methods, systems, computer readable media, and so on
employed in screening tissue. However, one of ordinary skill in the
art may recognize that further combinations and permutations are
possible. Accordingly, this application is intended to embrace
alterations, modifications, and variations that fall within the
scope of the appended claims. Furthermore, to the extent that the
term "includes" is employed in the detailed description or the
claims, it is intended to be inclusive in a manner similar to the
term "comprising" as that term is interpreted when employed as a
transitional word in a claim.
* * * * *