Non-invasive turbulent blood flow imaging system

• US 6,278,890 B1
• Filed: 11/09/1998
• Issued: 08/21/2001
• Est. Priority Date: 11/09/1998
• Status: Expired due to Fees

• Current Assignee: MCG International Incorporated
• Original Assignee: MedAcoustics, Inc.
• Inventors: Stearns, Scott Donaldson, Ryden, Carl Ashley, Chassaing, Charles E., Van Horn, Mark Harold
• Primary Examiner(s): Winakur, Eric F.
• Assistant Examiner(s): CARTER, RYAN CHRISTOPHER
• Application Number:
  US09/188,510
• Time in Patent Office: 1,016 Days
• Field of Search: 600/407, 600/454-458, 600/481-485, 600/488, 600/500, 600/504
• US Class Current: 600/407
• CPC Class Codes: A61B 2034/2055 Optical tracking systems
• CPC Class Codes: A61B 2034/2063 Acoustic tracking systems, e.g. using ultrasound
• CPC Class Codes: A61B 2090/3983 Reference marker arrangements for use with image guided surgery
• CPC Class Codes: A61B 34/20 Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• CPC Class Codes: A61B 8/06 Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/4405 Device being mounted on a trolley
• CPC Class Codes: G01S 15/52 Discriminating between fixed and moving objects or between objects moving at different speeds
• CPC Class Codes: G01S 15/8906 Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
• CPC Class Codes: G01S 15/8979 Combined Doppler and pulse-echo imaging systems
• CPC Class Codes: G01S 15/899 Combination of imaging systems with ancillary equipment
• CPC Class Codes: G01S 15/8993 Three dimensional imaging systems
• CPC Class Codes: G01S 7/52036 using analysis of echo signal for target characterisation
• CPC Class Codes: G01S 7/5205 Means for monitoring or calibrating
• CPC Class Codes: G01S 7/52073 Production of cursor lines, markers or indicia by electronic means
• CPC Class Codes: G01S 7/52077 with means for elimination of unwanted signals, e.g. noise or interference

Abstract

A non-invasive methodology and instrumentation for the detection and localization of abnormal blood flow in a vessel of a patient is described. An array of sensors are positioned on an area of a patient'"'"'s body above a volume in which blood flow may be abnormal. Signals detected by the sensor array are processed to display an image which may indicate the presence or absence of abnormal blood flow.

Claims

1. A non-invasive, in vivo, method for detecting change in coronary artery stenosis which comprises the steps of:

(i) procuring a plurality of heart sound data sets which are indicative of a stenosis in a patient'"'"'s coronary artery

wherein each of said data sets is procured at a different time,

(ii) comparing at least two of the data sets to identify selected differences between said sets,

wherein a difference identified in said comparing step corresponds to a change in the coronary artery stenosis of the patient.

2. The claim 1 method wherein said heart sound data sets is comprised of sets of data generated by an array of acoustic sensors positioned on the chest of said patient.

3. The claim 1 method, wherein said heart sound data sets are sets of heart sound data generated by sensing shear waves.

4. The claim 1 method wherein said heart sound data sets exclude heart sound data generated by compression waves.

5. A method according to claim 1, wherein said procuring step comprises the steps of:

placing an array of sensors on an area of the body surface of a patient above a volume of said body which may include abnormal blood flow, wherein each of said sensors in said array includes means to receive sounds caused by blood flow in said volume of said patient'"'"'s body and to generate electrical signals from said sounds, and

combining said electrical signals generated by a plurality of said sensors in said array for each of said data sets obtained at different times,

wherein said comparing step includes the step of providing a display of the spatial distribution of phase coherence in said combined electrical signal, and wherein abnormal blood flow in said volume of said patient'"'"'s body is indicated by non-uniformity in said display of said spatial distribution of said phase coherence.

6. A method according to claim 1, wherein said procuring step comprises the steps of:

(i) positioning a plurality of sensors in an array on an area of a patient'"'"'s body above a volume of said patient'"'"'s body in which abnormal blood flow may be present; and

(ii) beamforming said plurality of sensors to provide a beamformer output; and

wherein said comparing step includes the step of processing said beamformer output to detect said abnormal blood flow, if present, in said vessel of said patient.

7. A method according to claim 6, wherein said beamformer output is provided by a beam steering angle of from 30 to 30°

as a function of the number of sensors of when the source of the formed beam is at infinity.

8. The method of claim 6, wherein an adjustable gain/resolution null space beamformer is used to provide said beamformer output.

9. A method according to claim 6, wherein the plurality of sensors comprises sensors having a stretched piezolelectric film transducer.

10. A method according to claim 1, wherein said comparing step detects abnormal blood flow in a vessel of a patient wherein the abnormal blood flow is caused by a partial occlusion of the vessel, wherein the abnormal blood flow produces sounds detectable at the surface of said patient'"'"'s body, and wherein said procuring step comprises the steps of:

(i) positioning a plurality of sensors in an array on an area of the body surface of the patient above a vessel which may include the partial occlusion,

(ii) detecting sounds from the sensors caused by abnormal blood flow if present in the vessel of the patient;

(iii) generating electrical signals responsive to said detecting step the electrical signals which includes a compression wave component and a shear wave component;

(iv) locating each of said sensors on the surface of the body of said patient; and

(v) isolating said shear wave component, if present therein, from said plurality of electrical signals transmitted by said plurality of sensors; and

wherein said comparing step comprises processing said isolated shear wave component to detect if there is an occlusion present in the vessel of the patient.

11. The method of claim 10, further comprising the step of providing a display indicative of the occlusion if present in the vessel of the patient.

12. The method claim 10, wherein said shear wave component is isolated in step (v) by blocking the compression wave component in said signals transmitted by said sensors.

13. The method of claim 10, wherein said locating step is performed such the physical location on the subject is identified by photogrammetry.

14. The method of claim 10, wherein said compression wave component is blocked by velocity filtering or by steering a null at the source of said compression wave.

15. A method for non-invasively detecting a change in coronary artery stenosis, comprising the steps of:

positioning a first sensor array on the skin of a patient;

obtaining a first set of acoustic data from the sensor array at a first time;

removing the sensor array from the skin of the patient;

positioning a second sensor array on the skin of a patient;

obtaining a second set of acoustic data from the sensor array at a second time, the second time being temporally separate from the first time;

comparing the first and second acoustic data sets; and

determining a change in coronary artery stenosis in the patient based on said comparing step.

16. A method according to claim 15, wherein said comparing step includes identifying high and low energy features associated with the first and second data sets.

17. A method according to claim 15, further comprising the step of obtaining an ECG of the patient, and wherein said comparing step considers the ECG of the patient.

18. A method according to claim 17, wherein said comparing step comprises the step of parsing the first and second acoustic data sets to synchronize the data to the heartbeats of a patient.

19. A method according to claim 18, wherein said comparing step further comprises the steps of:

averaging the parsed data across a plurality of heartbeats of the patient; and

beamforming the averaged data into the time domain.

20. A method according to claim 19, wherein said comparing step comprises the step of extracting selected energy features at two frequencies associated with the first and second data sets, and wherein the extracted energy features of each of the first and second data sets are used in said determining step.

21. A method according to claim 20, wherein said selected energy features include a first energy feature at a first frequency which is between 200-600 Hz and a second energy feature at a second frequency higher than the first frequency, the second frequency being between 600-1800 Hz.

22. A method according to claim 15, wherein the first data set is obtained before and the second data set obtained after an interventional treatment has been performed on the patient.

23. A method according to claim 22, wherein said determining step estimates the change in the size of a stenotic coronary lesion.

24. A method according to claim 15, wherein the acoustic data includes data which corresponds to shear wave data acquired during the second quarter of the diastolic period of a patient.

25. A method according to claim 15, further comprising the step of acquiring ambient noise information which may impact the reliability of the sensed acoustic data.

26. A method according to claim 20, wherein said comparing step further comprises the step of classifying the extracted energy features per beat into a first subclass associated with pre-intervention data and a second subclass associated with post intervention data.

27. A method according to claim 26, further comprising the step of averaging the first and second data subclasses from said classifying step across beats.

28. A method according to claim 27, further comprising the step of plotting the classified subclass data associated with the pre- and post-intervention at the two selected frequencies.

29. A method according to claim 28, wherein said comparing step considers the number of data points at each of the two frequencies for the pre- and post-intervention subclass data in said plotting step.

30. A method according to claim 15, further comprising the step of generating a time series of acoustic images, wherein one said acoustic image is from the first acoustic data set at the first time and another of said acoustic image is from the second acoustic data at the second time.

31. A method according to claim 30, wherein the acoustic images are three-dimensional.

32. A method according to claim 15, wherein the first and second acoustic data is multi-channel data obtained for each heartbeat.

33. A method according to claim 15, wherein the obtaining steps include obtaining multi-channel acoustic data for a plurality of heartbeats, said method further comprising the step of screening each of the first and second acoustic data sets obtained for each heartbeat to select which to include in said comparing step.

34. A method according to claim 33, wherein all channel data corresponding to a particular heartbeat is retained together or rejected together.

35. A method for identifying post-intervention changes in coronary heart stenosis which comprises:

(i) providing energy feature data indicative of coronary artery stenosis extracted from heartbeats of a patient before intervention;

(ii) providing energy feature data indicative of coronary artery stenosis extracted from heartbeats of a patient after intervention;

(iii) pooling all of said feature data from all pre-intervention and all post-intervention data from said heart beats of said patient;

(iv) classifying said feature data pooled in step (iii) wherein a classifier output is produced for each pre-intervention beat and each post-intervention beat of said patient'"'"'s heart; and

(v) separately averaging said pre-intervention and post-intervention classifier output data produced in step

(iv) across said beats; and

(vi) determining the difference between the average of the pre-intervention and the average of the post-intervention classifier outputs obtained in step (v);

wherein a risk of coronary heart disease may be indicated when said difference in said average output from said post-intervention classifier outputs is equal to or greater than the average of said post-intervention classifier outputs.

36. A non-invasive, in vivo method for detecting coronary artery stenosis change over time which comprises:

(i) providing an array of acoustic sensors positioned on a patient'"'"'s chest

wherein said sensors detect S₁, S₂ and quiet interval sounds produced by said patient'"'"'s heart and convert said sounds to an electrical signal and

wherein said electrical signal includes a heart sound component and a noise component;

(ii) amplifying particular signal to noise ratio from said electrical signal;

(iii) subjecting said amplified electrical signal to signal processing to provide a processed signal having an increased signal to noise ratio;

(iv) isolating the portion of said processed signal of step (iii) which corresponds to the quiet interval in said patient'"'"'s heart sounds detected by said sensors in step (i);

(v) subjecting quiet interval signals isolated in step (iv) to visual screening wherein portions of said quiet interval signals attributable to breath sounds or transient noise are rejected and other portions of said signal are accepted;

(vi) subjecting said quiet interval signals isolated in step (iv) to aural screening wherein a portion of said signal is bandpass filtered from 500 Hz to 1800 Hz wherein regions with noise interference indicative of breathing sounds or cable noises or room noises or speech or bowel sounds are rejected;

(vii) identifying and correlating portions of the patient'"'"'s heart sound signal residual after step (vi) with first, second and third beats of said patient'"'"'s heart with corresponding first, second and third signal channels;

(viii) providing a time alignment of said first, second and third channels;

(ix) assigning optimal weights to each of said time aligned signals;

(x) computing two energy-based features from said time aligned and optimally weighted signals

wherein a first energy based feature is computed at a low frequency from 200 Hz to 600 Hz and

wherein a second energy based feature is calculated at a frequency of 600 Hz to 1800 Hz.

37. A method for acquiring and preprocessing data in preparation for signal analysis to detect changes in coronary artery stenosis which comprises:

(i) providing a fixed array of sensors conformable to the chest of a patient;

wherein said sensors detect cardiac sounds in said chest of said patient and convert said blood flow sounds to electrical signals;

(ii) placing said array on the chest of said patient in an area above said patient'"'"'s heart, wherein the patient'"'"'s heart beat generates S₁, S₂, S₃, S₄ and other cardiac sounds;

(iii) converting said cardiac sounds into engineering units of acceleration wherein said units of accelerator constitute said signals to be analyzed;

(iv) segmenting said signals into acceleration units identifying individual beats of said patient'"'"'s heart;

(v) providing a real time window between said S₂ and S₁ sounds of each of said individual beats of said patient'"'"'s heart; and

(vi) subjecting said S₁ and S_S sounds to quality inspection of said sounds.