U.S. patent application number 14/850656 was filed with the patent office on 2016-03-17 for sensor interface device providing digital processing of intravascular flow and pressure data.
The applicant listed for this patent is Volcano Corporation. Invention is credited to Paul Douglas Corl.
Application Number | 20160074005 14/850656 |
Document ID | / |
Family ID | 54238481 |
Filed Date | 2016-03-17 |
United States Patent
Application |
20160074005 |
Kind Code |
A1 |
Corl; Paul Douglas |
March 17, 2016 |
SENSOR INTERFACE DEVICE PROVIDING DIGITAL PROCESSING OF
INTRAVASCULAR FLOW AND PRESSURE DATA
Abstract
Embodiments of the present disclosure are configured to assess
the severity of a blockage in a vessel and, in particular, a
stenosis in a blood vessel. In some particular embodiments, the
devices, systems, and methods of the present disclosure are
configured to assess the severity of a stenosis in the coronary
arteries by monitoring fluid flow. In some embodiments, the
devices, systems, and methods of the present disclosure receive
analog sensor data that includes fluid flow data and digitizes the
analog sensor data according to a quadrature sampling rate. A
weighted accumulator performs a baseband conversion on the
digitized sensor data and may perform other signal processing
steps. The processed data is then provided for use in any one of a
number of diagnostic assessments.
Inventors: |
Corl; Paul Douglas; (Palo
Alto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Volcano Corporation |
San Diego |
CA |
US |
|
|
Family ID: |
54238481 |
Appl. No.: |
14/850656 |
Filed: |
September 10, 2015 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62049205 |
Sep 11, 2014 |
|
|
|
Current U.S.
Class: |
600/463 ;
600/465; 600/505 |
Current CPC
Class: |
A61B 8/461 20130101;
A61B 8/12 20130101; A61B 8/06 20130101; A61B 8/5223 20130101; A61B
8/4483 20130101 |
International
Class: |
A61B 8/06 20060101
A61B008/06; A61B 8/08 20060101 A61B008/08; A61B 8/00 20060101
A61B008/00; A61B 8/12 20060101 A61B008/12 |
Claims
1. A medical diagnostic system comprising: a patient interface
module operable to process analog flow sensor data received from an
intravascular device, wherein the patient interface module
includes: an analog-to-digital converter operable to sample the
analog flow sensor data according to a quadrature sampling rate to
produce digital flow sensor data; a signal processing resource
operable to perform a baseband conversion on the digital flow
sensor data to produce baseband flow sensor data; and an interface
subunit operable to output the baseband flow sensor data.
2. The medical diagnostic system of claim 1, wherein the analog
flow sensor data includes a measurement of fluid flow velocity
within a vessel.
3. The medical diagnostic system of claim 1, wherein the quadrature
sampling rate is defined by the equation: quadrature sampling
rate=4/(2N+1).times.a nominal center frequency where N is an
integer greater than or equal to zero.
4. The medical diagnostic system of claim 3, wherein a carrier
frequency is chosen to be equal to the nominal center frequency of
an ultrasound transducer used to obtain the analog flow sensor
data.
5. The medical diagnostic system of claim 3, wherein N is selected
such that the quadrature sampling rate is at least a Nyquist rate
of the analog flow sensor data.
6. The medical diagnostic system of claim 1, wherein the signal
processing resource includes a weighted accumulator operable to
perform the baseband conversion on the digital flow sensor
data.
7. The medical diagnostic system of claim 6, wherein the weighted
accumulator is further operable to perform: in-phase signal mixing
on the digital flow sensor data to produce an in-phase component;
quadrature signal mixing on the digital flow sensor data to produce
a quadrature component; and interpolation and low-pass filtering on
the in-phase component and the quadrature component.
8. The medical diagnostic system of claim 6, wherein the weighted
accumulator includes an in-phase weighted accumulator and a
quadrature accumulator.
9. The medical diagnostic system of claim 1 further comprising the
intravascular device including a fluid flow sensor disposed at a
distal end of the intravascular device, wherein the fluid flow
sensor is operable to provide the analog flow sensor data to the
patient interface module.
10. The medical diagnostic system of claim 1 further comprising a
processing system operable to obtain a measurement of fluid flow
volume based on the baseband flow sensor data and to display the
measurement of fluid flow volume on a user display.
11. The medical diagnostic system of claim 1 further comprising: a
signal separator operable to separate the analog flow sensor data
from other received analog sensor data.
12. A diagnostic method comprising: receiving analog sensor data
including a measurement of fluid flow; digitizing the analog sensor
data to obtain digital sensor data using a quadrature sampling rate
corresponding to a center frequency of an ultrasound transducer
used in obtaining the analog sensor data; performing a baseband
conversion of the digital sensor data using a computing system to
obtain digital baseband sensor data; and outputting a
representation of a measurement of fluid flow based on the digital
baseband sensor data to a display for use in a diagnostic
assessment.
13. The method of claim 12, wherein the quadrature sampling rate is
defined by the equation: quadrature sampling
rate=4/(2N+1).times.the center frequency where N is an integer
greater than or equal to zero.
14. The method of claim 12, wherein the quadrature sampling rate is
equal to four times the center frequency of the ultrasound
transducer.
15. The method of claim 12, wherein the performing of the baseband
conversion includes supplying first and second sets of coefficients
to at least one weighted accumulator, wherein the first and second
sets of coefficients are selected to: mix the digital sensor data
with a signal cos (2.pi.f.sub.ct), where f.sub.c is substantially
equal to the center frequency in order to obtain an in-phase
component; mix the digital sensor data with a signal sin
(2.pi.f.sub.ct), where f.sub.c is substantially equal to the center
frequency in order to obtain a quadrature component; and low-pass
filter the in-phase component and the quadrature component.
16. The method of claim 15, wherein the first and second sets of
coefficients are further selected to perform at least one of: a
sample-and-hold process and clutter filtering.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority to and the benefit
of the U.S. Provisional Patent Application No. 62/049,205, filed
Sep. 11, 2014, which is hereby incorporated by reference in its
entirety.
TECHNICAL FIELD
[0002] The present disclosure relates generally to the assessment
of vessels and, in particular, to systems and techniques for the
assessment of the severity of a blockage or other restriction to
the flow of fluid through a vessel. Aspects of the present
disclosure are particularly suited for evaluation of biological
vessels in some instances. For example, some particular embodiments
of the present disclosure are specifically configured for the
evaluation of a stenosis of a human blood vessel.
BACKGROUND
[0003] Technology innovations and trends in the healthcare industry
are driving increased adoption of intravascular diagnostic
procedures in interventional cardiology practice. The use of
intravascular diagnostic tools to complement the traditional
external diagnostic procedures (e.g., angiography) has been shown
to promote more appropriate and effective application of
interventional procedures, leading to improved patient satisfaction
and increased cost-effectiveness. In particular, diagnostic
equipment and methods have been developed for assessing vascular
obstructions and other vascular disease using miniature sensors
placed at the distal end of a flexible elongate member such as an
intravascular catheter or guide wire used for catheterization
procedures. For example, known intravascular medical sensing
techniques include intravascular pressure measurement for
calculation of fractional flow reserve (FFR) or instantaneous
wave-free ratio (iFR), Doppler ultrasound blood flow measurement
for determination of coronary flow reserve (CFR) or other
flow-related parameters, intravascular ultrasound (IVUS) imaging,
and optical coherence tomography (OCT) imaging. Each of these
techniques may be suited for different diagnostic situations.
[0004] To facilitate improved diagnosis to guide more appropriate
treatment, healthcare facilities typically maintain a multitude of
intravascular diagnostic, and sensing modalities for use during an
interventional procedure in the catheter lab. Recently, processing
systems have been developed that collect medical data from a
plurality of different imaging, treatment, diagnostic, and sensing
tools and process the multi-modality medical data. Such
multi-modality systems are valued for reducing the clutter in the
cath lab by reducing the number of separate systems that are needed
to support the multitude of technologies, and for the synergies
that arise from combining information from different sensing
technologies into a single unit. In these multi-modality systems,
efficient and flexible signal processing systems are valued for
facilitating a more compact system with greater capabilities and
adaptability.
[0005] While existing diagnostic systems have proved useful, they
are not without drawbacks. Many legacy systems rely on highly
specialized analog circuitry to process sensor data. Compared to
traditional analog approaches, modern digital signal processing
systems may provide increased flexibility and adaptability, reduced
power consumption, more compact packaging, enhanced stability, and
improved performance.
[0006] Accordingly, there remains a need for improved devices,
systems, and methods for obtaining diagnostic information, such as
vascular data used to assess the severity of a blockage in a vessel
and, in particular, a stenosis in a blood vessel. Improved
diagnostic systems that perform signal processing in the digital
domain have the potential to bring improved performance to existing
diagnostic and therapeutic practices and to pave the way for
further advances.
SUMMARY
[0007] Embodiments of the present disclosure are configured to
assess the severity of a blockage in a vessel and, in some
examples, a stenosis in a blood vessel utilizing an intravascular
device having one or more sensing devices measuring flow, pressure,
and/or temperature.
[0008] In some instances, a diagnostic system is provided that
receives sensor information from the sensors of the intravascular
device. The diagnostic system includes a patient interface module
that is operable to process analog flow sensor data received from
the intravascular device. The patient interface module includes an
analog-to-digital converter operable to sample the analog flow
sensor data according to a quadrature sampling rate to produce
digital flow sensor data. The patient interface module also
includes a signal processing resource operable to perform a
baseband conversion on the digital flow sensor data to produce
baseband flow sensor data. The patient interface module also
includes an interface subunit operable to output the baseband flow
sensor data. The analog flow sensor data may include a measurement
of fluid flow velocity within a vessel. In such embodiments, the
quadrature sampling rate may correspond to a center frequency of
the ultrasound transducer. For example, in one such embodiment, the
quadrature sampling rate is approximately equal to four times the
center frequency. This quadrature sampling method simplifies many
of the steps and calculations involved in the baseband conversion
of the digital sensor data and may make digital baseband conversion
an attractive alternative to analog baseband conversion.
[0009] In some instances, a method of assessing a vessel of a
patient is provided that includes: receiving analog sensor data
including a measurement of fluid flow; digitizing the analog sensor
data to obtain digital sensor data using a quadrature sampling rate
corresponding to a center frequency of an ultrasound transducer
used in obtaining the analog sensor data; performing a baseband
conversion of the digital sensor data using a computing system to
obtain digital baseband sensor data; and outputting a
representation of a measurement of fluid flow based on the digital
baseband sensor data to a display for use in a diagnostic
assessment. In some embodiments, the quadrature sampling rate is
defined by the equation:
quadrature sampling rate=4/(2N+1).times.the center frequency
where N is an integer greater than or equal to zero. For example,
in one such embodiment, the quadrature sampling rate is
substantially equal to four times the center frequency of the
ultrasound transducer. In some instances, the baseband conversion
may include mixing the digitized samples with in-phase and
quadrature reference signals to produce in-phase and quadrature
components, and then performing interpolation and low-pass
filtering on the in-phase component and on the quadrature
component.
[0010] Other devices, systems, and methods specifically configured
to interface with such devices and/or implement such methods are
also provided. Additional aspects, features, and advantages of the
present disclosure will become apparent from the following detailed
description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] Illustrative embodiments of the present disclosure will be
described with reference to the accompanying drawings, of
which:
[0012] FIG. 1 is a diagrammatic schematic view of a medical
diagnostic system showing a distal portion of an associated
intravascular device according to an embodiment of the present
disclosure.
[0013] FIG. 2 is a diagrammatic schematic view of a patient
interface monitor (PIM) of a diagnostic system according to an
embodiment of the present disclosure.
[0014] FIG. 3 is a diagrammatic schematic view of a signal
processor of a PIM according to an embodiment of the present
disclosure.
[0015] FIG. 4 is a flow diagram illustrating steps for receiving
and processing medical sensor data by the diagnostic system
according to an embodiment of the present disclosure.
DETAILED DESCRIPTION
[0016] For the purposes of promoting an understanding of the
principles of the present disclosure, reference will now be made to
the embodiments illustrated in the drawings, and specific language
will be used to describe the same. It is nevertheless understood
that no limitation to the scope of the disclosure is intended. Any
alterations and further modifications to the described devices,
systems, and methods, and any further application of the principles
of the present disclosure are fully contemplated and included
within the present disclosure as would normally occur to one
skilled in the art to which the disclosure relates. In particular,
it is fully contemplated that the features, components, and/or
steps described with respect to one embodiment may be combined with
the features, components, and/or steps described with respect to
other embodiments of the present disclosure. For the sake of
brevity, however, the numerous iterations of these combinations
will not be described separately.
[0017] Referring to FIG. 1, shown therein is a diagnostic system
100 according to an embodiment of the present disclosure. In
general, the diagnostic system 100 may be a single modality medical
system or a multi-modality medical system. In that regard, a
multi-modality medical system provides for coherent integration and
consolidation of multiple forms of acquisition and processing
elements designed to be sensitive to a variety of methods used to
acquire and interpret physiological and morphological information
and/or coordinate treatment of various conditions in the human
vasculature.
[0018] As shown, the diagnostic system 100 includes a sensing
instrument 102. The sensing instrument 102 may be any form of
device, instrument, or probe sized and shaped to be positioned
within a vessel. In the illustrated embodiment, the instrument 102
is generally representative of a guide wire. In other embodiments,
the instrument 102 may be a catheter, including a rapid-exchange
catheter or an over-the-wire catheter. Generally, the instrument
102 is sized such that it can be positioned within a vessel without
significantly impairing fluid flow through the vessel. Accordingly,
in some instances the instrument 102 has an outer diameter of
0.035'', 0.018'', 0.014'' or less.
[0019] As shown in FIG. 1, the distal end of the instrument 102 is
advanced into a vessel 104. Vessel 104 represents fluid filled
structures, both natural and man-made, within a living body and can
include for example, but without limitation, structures such as:
blood vessels (arteries and veins), portions of the heart, heart
valves, stents, shunts, filters and other natural or artificial
devices positioned within the body.
[0020] The instrument 102 is configured to obtain diagnostic
information about fluid flow within the vessel 104 (or about the
vessel 104 itself) in which the instrument 102 is positioned. In
that regard, the instrument 102 includes one or more sensing
elements, transducers, and/or other monitoring elements (referred
to generally as sensors 106) positioned within a distal portion of
the instrument 102. For example, one or more sensors may be
disposed at the distal tip 108 of the instrument. The sensor(s) 106
are configured to obtain the diagnostic information such as one or
more of flow velocity, volumetric flow , intravascular pressure,
temperature, images (including images obtained using intravascular
ultrasound, optical coherence tomography, thermal, and/or other
imaging techniques), and/or combinations thereof.
[0021] In the illustrated embodiment, the instrument 102 includes
at least one sensor 106 configured to monitor fluid flow within a
vessel. Some exemplary flow monitoring sensors 106 incorporate one
or more ultrasound transducers. For example in some instances, the
flow monitoring sensor 106 is an ultrasound transducer configured
to detect Doppler-shifted ultrasound echo signals indicative of
blood flow velocity.
[0022] Additionally or in the alternative, the instrument 102 may
include at least one pressure monitoring sensor 106. Exemplary
pressure monitoring sensors 106 include a piezoresistive pressure
sensor, a piezoelectric pressure sensor, a capacitive pressure
sensor, an electromagnetic pressure sensor, a fluid column (the
fluid column being in communication with a fluid column sensor that
is separate from the instrument and/or positioned at a portion of
the instrument proximal of the fluid column), an optical pressure
sensor, and/or combinations thereof. In some instances, one or more
features of the pressure monitoring element are implemented as a
solid-state component manufactured using microelectromechanical
systems (MEMS) technology and/or other suitable manufacturing
techniques. An example of a commercially available guide wire
product that includes both flow velocity and pressure measuring
elements is the ComboWire.RTM. XT pressure and flow guide wire
available from Volcano Corporation.
[0023] When the sensors 106 are active, a communications channel
110, such as an optical fiber, a conductor bundle, and/or a
wireless transceiver, present in the instrument 102 carries sensor
data to a patient interface monitor (PIM) 112 coupled to the
proximal end of the instrument 102. The PIM 112 is operable to
receive medical sensor data collected by the instrument 102 and the
corresponding sensors 106, and is operable to transmit the received
data to the processing system 114. In various embodiments, the PIM
112 transmits the medical sensor data over a USB connection, but in
other embodiments an Ethernet connection, a Thunderbolt connection,
a FireWire connection, or some other high-speed data bus connection
can be utilized. In other instances, the PIM 112 may be connected
to the processing system 114 via wireless connections using IEEE
802.11 Wi-Fi standards, Ultra Wide-Band (UWB) standards, wireless
FireWire, wireless USB, or another high-speed wireless networking
standard.
[0024] In addition to transmitting data between the instrument 102
and the processing system 114, the PIM 112 may perform processing
of the sensor data prior to transmitting the data. In examples of
such embodiments, the PIM 112 performs amplification, filtering,
time-stamping, identification, and/or aggregating of the data. The
PIM 112 may also transfer data from the processing system 114 to
the sensors 106 of the instrument 102. In an exemplary embodiment,
this feedback data include commands to enable and disable sensors
and/or to configure modes of operation for individual sensors. In
some embodiments, the PIM 112 also supplies power to drive the
operation of the sensors 106.
[0025] The processing system 114 to which the PIM 112 is coupled
governs sensor operation and data acquisition, processing,
interpretation, and display. In that regard, the processing system
114 receives sensor data from the sensors 106 of the instrument 102
via the PIM 112, processes the sensor data to render it suitable
for display, and presents the processed sensor data on a user
display or monitor 116.
[0026] In various embodiments, the diagnostic system 100 includes a
computing system comprising any combination of hardware and
software in order to acquire, process, and display medical data. In
embodiments in which the diagnostic system 100 includes a computer
workstation, the system includes a processor such as a
microcontroller or a dedicated central processing unit (CPU), a
non-transitory computer-readable storage medium such as a hard
drive, random access memory (RAM), read-only memory (e.g., CD-ROM,
DVD, etc.), a video controller such as a graphics processing unit
(GPU), and/or a network communication device such as an Ethernet
controller and/or wireless communication controller. The hardware
of the diagnostic system 100 may be programmed to execute steps
associated with the data acquisition and analysis described herein.
Accordingly, it is understood that any steps related to data
acquisition, data processing, instrument control, and/or other
processing or control aspects of the present disclosure may be
implemented by the diagnostic system 100 using corresponding
instructions stored on or in a non-transitory computer readable
medium accessible by the processing system. Further, it is
understood that the different processing and/or control aspects of
the present disclosure may be implemented separately or within
predefined groupings using a plurality of computing devices. The
present disclosure encompasses any divisions and/or combinations of
the processing and/or control aspects described below across
multiple computing devices.
[0027] Referring next to FIG. 2, an exemplary PIM 112 is shown in
more detail according to an embodiment of the present disclosure.
The PIM 112 offers an improved instrument interface that utilizes
high-sampling-rate analog-to-digital conversion to enable much of
the signal processing to be performed in the digital domain, rather
than making more extensive use of analog signal processing
electronics. For example, in some embodiments, analog components
are used to perform data processing steps including baseband
conversion, interpolation and low-pass filtering,
integrate-and-hold, and/or clutter filtering to remove low
frequency and stationary echoes. These components operate on an
analog representation of the sensor data (voltage waveforms).
Historically, analog signal processing has provided a relatively
compact and efficient method for handling high frequency signals,
but with limited flexibility for adopting to varying signal
processing tasks, while it was digital electronics was limited in
its ability to convert and process high frequency analog signals.
However, modern digital electronics can now accommodate high
frequency analog-to-digital conversion and signal processing tasks
in an even more compact and power-efficient format compared to
traditional analog electronics, while providing greatly enhanced
flexibility. While analog circuitry is often tuned to the specific
instrument 102 or family of instruments 102, digital signal
processing can typically provide a great degree of flexibility
through programmability of the digital signal processing element(s)
to adapt to a wide variety of sensor types. Accordingly in some
embodiments, digital signal processing provides the same or
improved signal processing capabilities and greater flexibility
compared to analog-intensive approaches, while offering a more
compact implementation and reduced power consumption. In some such
embodiments, these benefits can be realized in a smaller and less
expensive PIM 112.
[0028] As shown in FIG. 2, the PIM 112 includes a signal separator
202, one or more analog-to-digital converters (e.g., ADCs 204 and
206), a signal processor 208, and an interface subunit 210. In the
illustrated embodiment, the PIM 112 is configured to receive
pressure and flow-related data from the instrument 102. It is
understood that these data types are exemplary, and accordingly,
components may be added to or subtracted from the PIM 112 based on
the type of sensor data provided by the instrument 102.
[0029] Received sensor data may be first provided to the signal
separator 202 of the PIM 112. In embodiments in which the
instrument 102 supplies more than one type of data on the same set
of electrical conductors or other communication pathway, the signal
separator 202 discriminates between the data types to provide a
different type or modality of data at each output. For example, in
the illustrated embodiment, the signal separator 202 separates
pressure data reported by the instrument 102 from flow-related data
also reported by the instrument 102. In embodiments in which the
instrument 102 supplies only a single type of data, the signal
separator 202 may be omitted or disabled.
[0030] The mechanism by which the signal separator 202 operates
depends in part on the manner in which the data is transmitted over
the communications channel 110. In an embodiment, different types
of data are reported on different conductors, and the signal
separator 202 merely separates the conductors according to data
type. In an embodiment, data is time-division multiplexed, and the
signal separator 202 includes a time-division demultiplexer. In
some embodiments, different data types have different
characteristic frequencies, and the signal separator 202 includes a
number of low-pass, high-pass, and/or band-pass filters. For
example, in an embodiment, pressure data is reported as a DC and
low-frequency signal (e.g., approximately 100 Hz and below), while
the ultrasonic echo signal carrying the flow-related Doppler
ultrasound echo signal has a higher characteristic frequency (e.g.,
in the ultrasonic frequency range, typically 10 MHz and higher).
Accordingly, the signal separator 202 passes the composite signal
through a low-pass filter to extract the pressure-related data and
through a high-pass filter to extract the flow-related data. Of
course, these embodiments are merely exemplary, and other types of
signal separation are both contemplated and provided for.
[0031] After separation, one or more of the data signals may be
amplified. In the illustrated embodiment, the flow data is
amplified by the illustrated amplifier, while the pressure data is
not. However, it is understood that in other embodiments, all,
some, and/or none of the types of signal data are amplified prior
to analog-to-digital conversion. The amplified or unamplified data
is digitized using analog-to-digital converters such as ADCs 204
and 206. The ADCs 204 and 206 sample the analog signals at discrete
times and provide the sample values in a digital format. The
sampling rate used by the ADCs may be determined in part by the
type of data being sampled and the characteristic frequencies
thereof. For example, pressure data may vary relatively slowly, and
so, in an embodiment, a sampling rate of approximately 200 Hz is
provided to ADC 204. In some instances, the PIM 112 includes
components for processing the pressure data as described in U.S.
patent application Ser. No. 14/212,989, filed Mar. 14, 2014, now
published as U.S. Patent Application Publication No. U.S.
2014-0276143 A1 on Sep. 18, 2014, which is hereby incorporated by
reference in its entirety.
[0032] In contrast to the low characteristic frequency of pressure
data, flow velocity data produced by a Doppler ultrasound
transducer may have a much higher characteristic frequency.
Ultrasound transducers operate by emitting ultrasound waves
centered on a nominal center frequency, and receiving the echo
signals from surrounding tissues such as the vessel wall 104 and
the moving blood within the vessel. Accordingly, in some
embodiments, the relevant flow data (Doppler-shifted ultrasound
echo signal) falls within a relatively narrow bandwidth centered
around the nominal center frequency. Various exemplary ultrasound
transducers used for intravascular Doppler flow measurement have
nominal center frequencies between approximately 10 MHz and
approximately 15 MHz. Other exemplary ultrasound transducers have
nominal center frequencies of approximately 20 MHz or approximately
40 MHz. Due to its relatively high center frequency compared to its
typically much narrower bandwidth, Doppler ultrasound echo signals
are often converted to a baseband format prior to being digitized,
in order to reduce the required ADC sampling rate. Baseband
conversion mixes a narrow bandwidth, high-frequency signal with
sine and cosine reference waveforms to produce a pair of
low-frequency in-phase and quadrature signals, centered on zero
frequency but covering the same bandwidth as the original echo
signal. The resulting lower frequency signals can be faithfully
represented by samples digitized at a much lower rate compared to
that required to directly sample the original echo signal.
[0033] However, as will be discussed in further detail below, there
are advantages to directly digitizing the Doppler-shifted
ultrasound echo signal at a higher sampling rate, rather than using
analog baseband conversion to reduce the sample rate requirement.
In particular, analog baseband conversion introduces nonlinear
signal distortion and mismatch between in-phase and quadrature
channels, creating artifacts in the subsequent Doppler analysis,
while direct digital sampling may utilize a single ADC to capture
both in-phase and quadrature samples, thereby ensuring perfect
matching to eliminate channel matching artifacts. In addition,
analog baseband conversion circuitry requires careful tuning of the
components to ensure optimum performance, and generally provides
limited flexibility in adapting to different operating frequencies
to support multiple device types. In contrast, direct digital
sampling reduces or eliminates the need for tuning of the
components, with any required calibration or tuning implemented in
the digital domain through compensation coefficients stored in
nonvolatile memory. Furthermore the direct digital sampling
approach offers a great deal of flexibility to accommodate
different devices and signal processing algorithms by loading new
firmware to control the sampling frequency and other signal
processing parameters.
[0034] One particular variation of direct digital sampling is the
digital quadrature sampling approach as described in greater detail
below. According to this approach, in various embodiments, the flow
ADC 206 digitizes the Doppler-shifted ultrasound echo signals at a
sampling rate determined by the equation:
Sampling Rate=4/(2N+1).times.Nominal Center Frequency
where N is an integer greater than or equal to 0. In an exemplary
embodiment, where the ultrasound transducer of the instrument 102
has a nominal center frequency of 12 MHz, N is selected to be 0
such that the flow data ADC 206 samples the Doppler-shifted
ultrasound echo signal at 4 times the nominal center frequency
(i.e., 48 MHz).
[0035] As an alternative to direct quadrature sampling, a
high-speed ADC 206 may be used to sample the Doppler-shifted
ultrasound echo signal at an arbitrary frequency greater than the
Nyquist rate (i.e., greater than twice the highest frequency
component in the echo signal). High-speed ADCs support very high
sampling rates (in the GHz range and above). Thus, in some
embodiments, ADC 206 is used to sample the flow data at a rate
significantly greater than the Nyquist rate (e.g., 10 times the
Nyquist rate or greater). In further exemplary embodiments, the
flow data ADC 206 samples at pseudo-random intervals.
[0036] The digitized sensor data is provided to a signal processor
208, which may include an FPGA (field programmable gate array), an
ASIC (application-specific integrated circuit), a programmable
microcontroller, a microprocessor, and/or any other processing
resource. In an embodiment, the signal processor 208 may perform
baseband conversion, filtering, interpolation, noise reduction,
integrate-and-hold, and/or other signal processing tasks on
digitized Doppler-shifted ultrasound echo data before providing
this processed, flow-related data to other components of the
diagnostic system 100 such as the processing system 114 via the
interface subunit 210.
[0037] Referring now to FIG. 3, an exemplary signal processing
resource 208 is illustrated in more detail. The illustrated logical
blocks 302-316 represent hardware, firmware, software, and/or
combinations thereof configured to perform various signal
processing tasks on the sensor data. In the illustrated embodiment,
the sensor data includes Doppler-shifted ultrasound echo signals
representing blood flow data, and the signal processing blocks
include mixer blocks 302 and 306 and low-pass filter blocks 304 and
308 that perform a baseband conversion of the digitized data.
Similarly to baseband conversion in the analog domain, the digital
baseband conversion blocks produce a set of low frequency baseband
signals that represent the high frequency data. The underlying
principle is that a time varying signal S(t) can be expressed as
follows:
S(t)=I(t) cos (2.pi.f.sub.ct)-Q(t) sin (2.pi.f.sub.ct)
The two constituent signals, I(t) and Q(t), are referred to as the
in-phase and quadrature components of S(t). This representation is
useful because I(t) and Q(t) are frequency shifted downwards by
f.sub.c, referred to as the carrier frequency, and if the time
varying signal has a narrow bandwidth around the carrier frequency,
then the constituent signals are similarly narrow in bandwidth
around zero frequency. In this case, the time varying signal is
fully represented by the two constituent signals combined with
knowledge of the chosen carrier frequency, while in many cases the
low frequency constituent signals I/(t) and Q(t) are easier to
manipulate than their high-frequency counterpart S(t) in the
subsequent signal processing steps. In many embodiments, f.sub.c is
selected to be equal to the nominal center frequency of the
ultrasound transducer used to obtain the Doppler flow data. To
obtain I(t), the digitized sensor data is mixed (multiplied) with a
cosine reference waveform cos (2.pi.f.sub.ct) in the in-phase mixer
block 302 and then low-pass filtered by filter block 304 to remove
the unwanted high frequency component. To obtain Q(t), the
digitized sensor data is mixed with a sine reference waveform sin
(2.pi.f.sub.ct) in the quadrature mixer block 306 and low-pass
filtered by filter block 308 to remove the unwanted high frequency
component.
[0038] The in-phase mixer block 302 of the signal processor 208 may
be implemented with a multiplier supplied with samples of the
reference waveform cos (2.pi.f.sub.ct) as coefficients, and the
quadrature mixer block 306 may be implemented with a multiplier
supplied with samples of the reference waveform sin (2.pi.f.sub.ct)
as coefficients. In embodiments in which the sampling rate conforms
to the equation:
Sampling Rate=4/(2N+1).times.Nominal Center Frequency
these coefficients are trivial. For example, when N=0 and f.sub.c
is selected to be the nominal center frequency of the ultrasound
transducer, the digitized samples of the reference waveform cos
(2.pi.f.sub.ct) are [1.0, -1.0, 1.0, -1.0, . . . ] and the
digitized samples of the reference waveform sin (2.pi.f.sub.ct) are
[0.1, 0, -1.0, 1.0, -1, . . . ]. With these simple coefficients,
the normally complex digital multiplication blocks generally needed
for quadrature mixing can be reduced to simple digital logic.
Furthermore, the ADC samples that contribute to the in-phase
component are the odd samples only, while the samples that
contribute to the quadrature component are the even samples only.
This separation between in-phase and quadrature components,
facilitated by digital quadrature sampling, simplifies the
subsequent signal processing operations by cutting in half the
number of samples that contribute to the various intermediate
results (such as the low-pass filter or the integrate-and-hold
outputs).
[0039] In alternative embodiments using high frequency
analog-to-digital conversion at an arbitrary rate (as opposed to
direct digital quadrature sampling), the reference waveform
coefficients are not so simple and furthermore each sample may
contribute to both the in-phase and quadrature components. With
these nontrivial coefficients, a pair of high-speed digital
multipliers is needed to implement the mixer blocks 302 and 306,
and each of these mixer blocks would need to process both even and
odd samples. But even with this added complexity, the required
high-speed multipliers can easily be incorporated into an
FPGA-based (or other) implementation.
[0040] As further illustrated in FIG. 3, the in-phase and
quadrature outputs from the mixer blocks 302 and 306 are
subsequently processed by interpolation and low-pass filter blocks
304 and 308. Interpolation provides in-phase and quadrature samples
that correspond to the same instant in time, and it is particularly
advantageous with respect to direct quadrature sampling, since that
sampling process naturally provides in-phase and quadrature samples
that are offset from each other by a time shift equal to the
sampling period of the direct quadrature sampling. In general,
interpolation is implemented by a band-pass filter, however in the
case of baseband signals centered on zero frequency, interpolation
is implemented with a simple low-pass filter. It is convenient to
implement interpolation with a finite impulse response (FIR)
filter, in which case the interpolation filter coefficients can be
combined (by convolution) with the low-pass filter coefficients
such that both operations can be performed together in a single
step.
[0041] Once the in-phase and quadrature components, I(t) and Q(t),
are determined, the signal processor 208 may perform other signal
processing tasks. In an embodiment, the signal processor 208
performs an integrate-and-hold process to implement range
selectivity, define a range gate depth, limit the bandwidth, and
improve the signal-to-noise ratio. The in-phase and quadrature
outputs from the interpolation and low-pass filter blocks 304 and
308 are processed by integrate-and-hold blocks 310 and 312 to
produce the range-gated baseband Doppler signals. The
integrate-and-hold step is performed in the digital domain by
simply accumulating (summing) the chosen number of successive
samples (corresponding to the range gate width) after the desired
delay from the initial transmit pulse (corresponding to the range
gate depth). The integration operation produces a low-pass
filtering effect with a cutoff frequency nominally equal to the
inverse of the integration time, while the signal-to-noise ratio is
improved by averaging as the square root of the number of samples
accumulated by the integrator.
[0042] Each of the three types of signal processing blocks
described in the foregoing paragraphs (mixer, interpolation/filter,
and integrate-and-hold) produces an output which is a linear
combination of the input samples to that block. By the principles
of linearity, a linear combination of linear combinations is itself
a linear combination of the original inputs, and accordingly, all
three of these processing steps can be combined into a single
mathematical operation which simply provides a weighted sum (i.e.,
a linear combination) of the input samples from the ADC. In this
case, the weighting coefficients for the composite operation
incorporate the coefficients needed for baseband mixing,
interpolation, low-pass filtering, and integration, and a single
multiplier/accumulator element can provide the mixer,
interpolation/low-pass filter, and integrate-and-hold functions in
blocks 302, 304, and 310 for the in-phase channel, or in blocks
306, 308, and 312 for the quadrature channel.
[0043] In an embodiment, the signal processor 208 performs clutter
filtering in blocks 314 and 316 to remove the ultrasound echo
contributions due to stationary and slow moving tissues. In some
embodiments, clutter filtering follows baseband conversion in the
mixer and low-pass filter blocks, and range gate selection in the
integrate-and-hold block to remove the stationary and low frequency
components present in the range-gated baseband Doppler signals.
Clutter filtering can be implemented after FFT (Fast Fourier
Transform) processing transforms the Doppler signals to the
frequency domain, by simply blanking the low frequency bins of the
spectrum. Alternatively, clutter filtering can be performed in a
time domain operation prior to the FFT. The time domain clutter
filter prior to FFT is advantageous in terms of reducing the
dynamic range within the FFT computations required to preserve the
low level flow blood flow components of the Doppler spectrum in the
presence of large, low-frequency clutter components. The time
domain clutter filter can be implemented with a relatively simple
algorithm consisting of a boxcar average (accumulator) and
subtraction, or it can use a more elaborate high-pass filter
implemented with an IIR (infinite impulse response) or FIR (finite
impulse response) architecture.
[0044] In various embodiments, the signal processor 208 performs
additional signal processing such as fast Fourier transform (FFT)
on the in-phase and quadrature components and/or instantaneous peak
velocity (IPV) processing on the spectral output from the FFT.
Subsequently, the signal processor 208 provides the processed
in-phase and quadrature components to the interface subunit 210 for
delivery to other components of the diagnostic system 100 such as
the processing system 114.
[0045] Referring now to FIG. 4, a method 400 of processing sensor
data using the diagnostic system 100 of FIGS. 1-3 is illustrated
according to an embodiment of the present disclosure. It is
understood that additional steps can be provided before, during,
and after the steps of method 400, and that some of the steps
described can be replaced or eliminated for other embodiments of
the method.
[0046] Referring to block 402, medical sensor data is obtained. In
some embodiments, an intravascular device such as the
aforementioned instrument 102 is advanced into a vessel 104.
Sensors 106 disposed on the instrument 102 are activated and used
to obtain the medical sensor data. Accordingly, the medical sensor
data includes one or more data modalities such as flow (velocity),
flow (volume), pressure, images, temperature, and/or combinations
thereof. In one such embodiment, the medical sensor data includes
both Doppler ultrasound blood flow sensor data and pressure sensor
data. The obtained medical sensor data is provided to a PIM 112 via
a communications channel 110 as shown in block 404.
[0047] Referring to block 406, in an embodiment, a signal separator
202 within the PIM 112 separates the medical sensor data according
to the respective modalities. For example, the signal separator 202
may separate Doppler ultrasound blood flow sensor data from
pressure sensor data according to their different characteristic
frequencies.
[0048] Referring to block 408, one or more ADCs of the PIM 112
digitize the medical sensor data thereby converting it from an
analog format to a digital format. The ADCs sample the analog
signal at discrete times and provide the sampled values in a
digital format. In an exemplary embodiment of Doppler ultrasound
based blood flow sensor data, it may be advantageous to perform
digital quadrature sampling at an ADC sampling rate specified by
the equation:
Sampling Rate=4/(2N+1).times.Nominal Center Frequency
where N is an integer greater than or equal to 0. In some
embodiments, N is selected to be 0 to provide a sampling rate 4
times the nominal center frequency. This sampling rate provides
digital quadrature sampling to simplify the subsequent signal
processing, and it is far higher than the Nyquist rate minimally
required to faithfully capture the full bandwidth of the signal.
The over-sampled data can provide increased signal-to-noise ratio
with respect to ADC quantization noise through sample averaging. In
other embodiments, an ADC samples the Doppler ultrasound based
blood flow sensor data at a rate significantly greater than the
Nyquist rate (e.g., 10 times the Nyquist rate) and/or at a
pseudo-random sampling intervals.
[0049] Referring to block 410, a signal processor 208 of the PIM
112 performs a baseband conversion on the digitized sensor data.
The baseband conversion produces a set of low frequency signals
that represent the high frequency, narrow bandwidth sensor data.
The digitized sensor data is mixed (multiplied) with a cosine
reference waveform cos (2.pi.f.sub.ct) and low-pass filtered to
obtain an in-phase component. The digitized sensor data is mixed
(multiplied) with a sine reference waveform sin (2.pi.f.sub.ct) and
low-pass filtered to obtain a quadrature component. In various
embodiments, the baseband conversion is performed by a single
weighted accumulator, while in other embodiments the baseband
conversion is performed by a pair of weighted accumulators, one for
the in-phase component and the other for the quadrature
component.
[0050] Referring to block 412, the signal processor 208 of the PIM
112 performs additional processing on the baseband converted sensor
data. This may include an integrate-and-hold process, clutter
filtering, FFT, IPV, and/or other suitable processing steps.
Referring to block 414, the processed sensor data is provided to
other components of the diagnostic system 100 such as the
processing system 114 via an interface subunit 210 of the PIM. The
processed sensor data may then be used for any suitable purpose
including diagnostic assessment of the vessel 104. In various
embodiments, the integrate-and-hold process is performed using the
same weighted accumulator(s) used to perform the baseband
conversion by mixing and filtering.
[0051] Persons skilled in the art will also recognize that the
apparatus, systems, and methods described above can be modified in
various ways. Accordingly, persons of ordinary skill in the art
will appreciate that the embodiments encompassed by the present
disclosure are not limited to the particular exemplary embodiments
described above. In that regard, although illustrative embodiments
have been shown and described, a wide range of modification,
change, and substitution is contemplated in the foregoing
disclosure. It is understood that such variations may be made to
the foregoing without departing from the scope of the present
disclosure. Accordingly, it is appropriate that the appended claims
be construed broadly and in a manner consistent with the present
disclosure.
* * * * *