Method and apparatus to visualize the coronary arteries using ultrasound

• US 20070238999A1
• Filed: 09/14/2006
• Published: 10/11/2007
• Est. Priority Date: 02/06/2006
• Status: Active Grant

• Current Assignee: Maui Imaging, Inc.
• Original Assignee: Maui Imaging, Inc.
• Inventors: Specht, Donald F.
• Granted Patent: US 8,105,239 B2
• Time in Patent Office: Days
• US Class Current: 600/437
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• 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 8/08 Detecting organic movements or changes, e.g. tumours, cysts, swellings A61B8/02 - A61B8/06 take precedence
• CPC Class Codes: A61B 8/0883 for diagnosis of the heart
• CPC Class Codes: A61B 8/0891 for diagnosis of blood vessels
• CPC Class Codes: A61B 8/4254 using sensors mounted on the probe
• CPC Class Codes: A61B 8/483 involving the acquisition of a 3D volume of data
• CPC Class Codes: A61B 8/543 involving acquisition triggered by a physiological signal
• CPC Class Codes: G06T 2207/10136 3D ultrasound image
• CPC Class Codes: G06T 2207/20092 Interactive image processing based on input by user
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 2207/30101 Blood vessel; Artery; Vein; Vascular
• CPC Class Codes: G06T 7/0012 Biomedical image inspection

Abstract

A non-invasive screening technique for visualizing coronary arteries which overcomes the problems of visualizing the curved arteries by projecting the three dimensional volume of the arteries onto a two dimensional screen. Blood filled areas, and in particular, the coronary arteries and veins, are highlighted to contrast them with other nearby tissues using non-linear classification and segmentation techniques. Data is gathered as a sequence of 2D slices stored as a 3D volume. Software is employed to interpolate voxels intermediate to the slices. Wiener filtering or LMS spatial filtering can be implemented on each 2D scan to improve lateral resolution and reduce noise prior to the use of the scan data with the classification and segmentation algorithms. A traditional handheld ultrasound probe is employed to enable the technician to locate the area of interest, but a gyroscopic stabilizer is added to minimize unwanted variation on two axes of rotation while scanning through angles on the third axis of rotation.

Claims

1. A method of reconstructing a 3D image, comprising the steps of:

(a) scanning the object with a 2D scanner;

(b) recording a succession of scans at different positions and angles such as to record echos from a volume to be examined;

(c) recording the position of each scan plane;

(d) discriminating between tubular blood-filled areas and other tissues and anechoic areas by applying nonlinear image processing techniques; and

(e) displaying only the blood-filled areas in a three-dimensional projection with control of view angle and rotation.

2. The method of claim 1, wherein step (a) is performed using a handheld probe.

3. The method of claim 2, further including the step of using adding a gyroscope to the handheld probe to hold the position of the probe fixed in pitch and yaw such that it can be rotated with minimal changes in pitch and yaw.

4. The method of claim 3, wherein steps (b) and (c) are performed using a computer, and further including the step of adding a second gyroscope to the handheld probe to measure the rotation angle of the probe and send the measurements to the computer.

5. The method of claim 2, further including the step of adding a gyroscope to the handheld probe and using it to hold the position of the probe fixed in yaw and roll such that it can be rotated in pitch angle with minimal changes in yaw and roll.

6. The method of claim 5, wherein steps (b) and (c) are performed using a computer, and further including the step of adding a second gyroscope to the handheld probe to measure the rotation angle of the probe and send the measurements to the computer.

7. The method of claim 1, further including the step of mounting the scanner probe in a fixture above the patient so that all positions can be monitored and sent to the computer.

8. The method of claim 1, further including the steps of:

(f) monitoring the position of the scanner probe, and

(g) sending an electrocardiogram lead to the computer for EKG gating.

9. The method of claim 1, further including the step of using a Wiener Filter to minimize image blur and noise from other sources.

10. The method of claim 1, further including the step of minimizing image blur and noise from other sources by using an adaptive inverse filter.

11. The method of claim 1, wherein step (d) is accomplished using artificial neural networks.

12. The method of claim 11, wherein the inputs to the artificial neural networks are pixel values from a 2D scan.

13. The method of claim 11, wherein the inputs to the artificial neural networks are voxel values from a volume array interpolated from multiple 2D scans.

14. The method of claim 11, further including the step of enhancing segmentation of blood-filled areas by weighting the classification of adjacent pixels or voxels.

15. The method of claim 1, wherein step (d) is accomplished using Classification and Regression Trees.

16. The method of claim 15, further including the step of enhancing segmentation of blood-filled areas by weighting the classification of adjacent pixels or voxels.

17. The method of claim 1, wherein the tissue discriminating step is accomplished using Hidden Markov Models.

18. The method of claim 17, further including the step of enhancing segmentation of blood-filled areas by weighting the classification of adjacent pixels or voxels.

19. The method of claim 1, wherein the tissue discriminating step is accomplished using Fuzzy Logic.

20. The method of claim 19, further including the step of enhancing segmentation of blood-filled areas by weighting the classification of adjacent pixels or voxels.

21. The method of claim 1, wherein the tissue discriminating step is accomplished using Expert Systems.

22. The method of claim 21, further including the step of enhancing segmentation of blood-filled areas by weighting the classification of adjacent pixels or voxels.

23. The method of claim 1, wherein step (e) includes projecting on a screen a 2D image of only the identified tubular blood-filled areas.

24. The method of claim 23, further including the step of controlling the angle of view of the projection by using a computer input device.

25. The method of claim 1, wherein steps (b), (c) and (d) are performed using a computer, and wherein said displaying step projects on a screen a 2D image of the identified tubular blood-filled tissues highlighted in intensity or color superimposed on echos showing the tissues of the heart.

26. The method of claim 25, further including the step of controlling the angle of view of the projection by using a pointing device or other input to the computer.

27. A method of displaying a 3D image, comprising the steps of:

(a) scanning the object with a 3D scanner;

(b) minimizing image blur, speckle, and other noise sources by applying at least one three-dimensional deconvolution technique;

(c) applying an artificial neural network to distinguish blood filled areas from other tissue; and

(d) visually displaying the blood-filled areas in a three-dimensional projection with control of view angle and rotation.

28. The method of claim 27, wherein step (d) involves visually displaying only the blood-filled areas.

29. The method of claim 27, wherein step (b) includes applying a linear filtering technique selected from the group consisting of LMS filtering and Wiener filtering.

30. A system for rendering a projection of images of the coronary arteries in three dimensions, comprising:

at least one computer;

at least one video display device;

at least one computer input device;

a 2D ultrasound scanner in communication with one of said computers, said scanner capable of scanning a plurality of closely spaced 2D slices capturing the structural features of the coronary arteries and other portions of the heart and tissue structures proximate the heart, said scanner including a phased array probe and a position sensor;

an electronic storage medium on one or more of said computers for storing said plurality of closely spaced 2D slices on digital or other electronic storage medium;

position recordation means to record the position and angle of each 2D slice relative to the other 2D slices

3D voxel array production software stored and run on one or more of said computers for filling the voxels of a 3D array interpolating from the processed closely spaced 2D slices to create a 3D volume;

image segmentation software stored and run on one or more of said computers for classifying each of said voxels in said 3D volume as tubular blood-filled or not, thereby distinguishing blood-filled areas from other areas of tissue; and

image projection software stored and run on one or more of said computers for projecting an image of said 3D volume from varying view angles controlled by one of said computer input devices.

31. The system of claim 30, further including image processing software stored and run on one or more of said computers for removing aperture blur and speckle noise from each stored 2D image.

32. The system of claim 30, wherein said phased array probe includes transducers, and wherein said system further includes position fixation means for keeping said transducers of said sensor probe in the same position while said probe is being rotated during scanning.

33. The system of claim 32, wherein said position fixation means comprises software to continuously measure and compensate for the spatial position (x, y, and z) and the angular position (pitch, yaw, and roll) of the transducers of said probe sensor.

34. The system of claim 32, wherein said position fixation means comprises a fixture mounted above the patient being scanned, whereby said fixture assures that the degrees of freedom are fixed during the collection of data, except either roll or pitch, which are measured.

35. The system of claim 34, further including software including correlation algorithms to compensate for motions of the heart.

36. The system of claim 32, wherein said phased array probe is a handheld probe having a handle, and said position fixation means comprises a first gyroscope mechanically attached to said phased array probe such that it is aligned with the centerline of the sector scan so as to be perpendicular with the surface of a patient'"'"'s skin and aimed at tissue to be imaged, and a second gyroscope mechanically attached to said phased array probe and aligned perpendicular to said first gyroscope and includes a sensor to measure the relative angle between it and said handle of said handheld probe.

37. The system of claim 36, wherein said first gyroscope is motorized and has sufficient mass and rotational speed to stabilize said handheld probe in pitch and yaw such that during data collection unwanted changes in pitch and yaw are minimized over a sequence of roll angles.

38. The system of claim 32, wherein said phased array probe is a handheld probe having a handle, and said position fixation means comprises a first gyroscope mechanically attached to said phased array probe such that it is aligned parallel to the line of transducer elements in said probe, and a second gyroscope mechanically attached to said phased array probe and aligned perpendicular to said first gyroscope and includes a sensor to measure the relative angle between it and said handle of said handheld probe.

39. The system of claim 38, wherein said phased array probe has a curved array, and said first gyroscope is aligned with the closest mean-squared error fit line of said curved array.

40. The system of claim 39, wherein said first gyroscope is motorized and has sufficient mass and rotational speed to stabilize said handheld probe in yaw and roll.

41. The system of claim 32, wherein said position recordation means comprises a fixture positioned rigidly above the patient'"'"'s chest.

42. The system of claim 30, wherein said phased array probe has a two-dimensional array of transducers and means for adjusting the roll or pitch angles of said transducers electronically.

43. The system of claim 30, wherein one of said computer input devices is a pointing device.

44. The system of claim 30, wherein said position recordation means allows the sonographer to scan a patient'"'"'s chest manually and includes software stored and run on one of said computers for discarding or averaging redundant images, and a sensor for recording relative instantaneous positions of said scanner probe.

45. The system of claim 30, wherein said image segmentation software comprises a neural network.

46. The system of claim 45, wherein said neural network is a multi-layer perceptron.

47. The system of claim 45, wherein said neural network is selected from the group consisting of a probabilistic neural network and a general regression neural network.

48. The system of claim 30, wherein said image segmentation software includes a classification and regression tree.

49. The system of claim 30, wherein said image segmentation software includes a hidden Markov model.

50. The system of claim 30, wherein said image segmentation software uses fuzzy logic.

51. The system of claim 30, wherein said image segmentation software includes a general regression neural network.

52. The system of claim 30, further including synchronization means for capturing said closely spaced 2D images in one phase of the cardiac cycle over the course of several cycles.

53. The system of claim 52, wherein said synchronization means is standard EKG gating.

54. The system of claim 52, wherein said synchronization means is image-based synchronization.

55. The system of claim 30, further including image segmentation enhancement software.

56. The system of claim 55, wherein said image segmentation enhancement software uses the influence of neighbor pixels or voxels.

57. The system of claim 55, wherein said image segmentation enhancement software uses only neighboring pixels in each 2D scan.

58. The system of claim 55, wherein said image segmentation enhancement software uses neighboring voxels in said 3D volume.

59. The system of claim 30, further including 3D image rotation means, whereby the point of view of the 3D image is rotated about any axis such that one group of coronary arteries at a time can be observed without being obscured by the other coronary arteries or non-suppressed portions of the main chambers of the heart and other artifacts.

60. The system of claim 30, further including probe location compensation software stored and run on one or more of said computers for compensating for small variations in the location of the probe from one scan to the next.

61. The system of claim 60, wherein said probe location compensation software uses correlation techniques.

62. The system of claim 30, wherein said video display device has several display modes, including 2D sector, M mode, doppler, and 3D.

63. The system of claim 30, further including an electrocardiogram sensor having an electrocardiogram sensor input in communication with one of said computers.