Method and apparatus for adaptive clutter filtering in ultrasound color flow imaging

• US 20020169378A1
• Filed: 06/11/2002
• Published: 11/14/2002
• Est. Priority Date: 08/20/1999
• Status: Active Grant

• Current Assignee: Shenzhen Mindray Bio-Medical Electronics Co., Ltd. (Excelsior Union Ltd.)
• Original Assignee: Zonare Medical Systems Incorporated (Excelsior Union Ltd.)
• Inventors: Ji, Ting-Lan, McLaughlin, Glen W., Chou, Ching-Hua, Mo, Larry Y.L.
• Granted Patent: US 6,733,455 B2
• Time in Patent Office: Days
• US Class Current: 600/437
• CPC Class Codes: A61B 8/00 Diagnosis using ultrasonic, sonic or infrasonic waves imaging of objects using sonar G01S15/00
• 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 8/0833 involving detecting or locating foreign bodies or organic structures
• CPC Class Codes: A61B 8/0866 involving foetal diagnosis; pre-natal or peri-natal diagnosis of the baby
• CPC Class Codes: A61B 8/13 Tomography A61B8/10, A61B8/12 take precedence; tomography for radiation diagnosis A61B6/02
• CPC Class Codes: A61B 8/4433 involving a docking unit
• CPC Class Codes: A61B 8/4438 Means for identifying the diagnostic device, e.g. barcodes
• CPC Class Codes: A61B 8/4455 Features of the external shape of the probe, e.g. ergonomic aspects
• CPC Class Codes: A61B 8/462 characterised by constructional features of the display
• CPC Class Codes: A61B 8/467 characterised by special input means
• CPC Class Codes: A61B 8/488 involving Doppler signals
• CPC Class Codes: G01S 15/8981 Discriminating between fixed and moving objects or between objects moving at different speeds, e.g. wall clutter filter
• CPC Class Codes: G01S 7/52084 related to particular user interfaces special user input means for ultrasonic medical diagnostic devices A61B8/467

Abstract

An adaptive clutter filtering for ultrasound color flow imaging is provided including an iterative algorithm that is used to select the best clutter filter for each packet of color flow data. If significant clutter motion is present, a high pass filter cutoff frequency is automatically set to suppress the clutter and associated flash artifacts. The cutoff frequency is chosen according to the frequency of the clutter—the lower the clutter frequency, the lower the cutoff frequency can be. If clutter frequencies are low, lower filter cutoffs allow for maximum low flow detection. In this manner, the filter cutoff frequency can be optimized based on the data for each pixel in the color flow image.

Claims

1. A system comprising a processor that is configured to iteratively select an optimal filter for filtering out clutter from ultrasound color flow imaging data.

2. The system of claim 1 wherein the filtering is performed over each packet of flow data that corresponds to an acoustic point in a color flow region of interest.

3. The system of claim 1 wherein a criterion for selecting the optimal filter is if a filtered signal is less than a threshold.

4. The system of claim 1 wherein a criterion for selecting the optimal filter is if a magnitude of a portion of a filtered signal that is within a predetermined frequency range is less than a threshold.

5. The system of claim 1 wherein a criterion for selecting the optimal filter is if a total energy of the filtered signal is less than an energy threshold.

6. The system of claim 1 wherein a criterion for selecting the optimal filter is if a mean frequency of a filtered signal data is less than a clutter frequency threshold.

7. The system of claim 6 wherein if the mean frequency is less than the clutter frequency threshold is determined by whether an absolute value of an imaginary part of a first order autocorrelation of a color flow signal is less than a constant times a real part of the autocorrelation, where the constant is determined by the clutter frequency threshold.

8. The system of claim 1 wherein a determination of whether a filter is optimal is made by comparing an index to a threshold for that index, the index being computed using a mathematical formula including a mean frequency and a magnitude of a filtered signal.

9. The system of claim 1, wherein the filtering is performed in real time while imaging.

10. The system of claim 1 further comprising a finite number of filters from which the optimal filter is selected.

11. The system of claim 10 in which the finite number of filters is two.

14. The method of claim 13 wherein the filtering includes filtering an individual packet of the ultrasound color flow data that corresponds to an acoustic point in a region of interest.

15. The method of claim 13 wherein the filtering is performed in real time while collecting the ultrasound color flow data.

16. The method of claim 13 wherein iteratively selecting includes determining if a filtered signal is less than a threshold.

17. The method of claim 13 wherein iteratively selecting includes determining if a magnitude of frequency of a filtered signal is less than a frequency threshold.

18. The method of claim 13 wherein iteratively selecting includes determining if a magnitude of a color flow signal in a preselected frequency range is less than a color flow signal threshold.

19. The method of claim 18 wherein determining includes determining whether an absolute value of an imaginary part of a first order autocorrelation of the color flow signal is less than a constant times a real part of the autocorrelation, where the constant is determined by a frequency threshold.

20. The method of claim 18 wherein determining includes determining whether a phase shift is less than a phase shift threshold.

21. The method of claim 13 wherein iteratively selecting comprises:

computing an index using a mathematical formula including a mean frequency and a magnitude of a filtered signal; and

determining whether a filter is optimal by comparing the index to a threshold.

22. The method of claim 13 wherein iteratively selecting includes selecting a filter from a finite number of filters.

23. The method of claim 13 wherein iteratively selecting includes selecting one of two filters.

25. The adaptive clutter filtering method of claim 24 wherein the optimal set of HPF parameters is selected from a group of sets of HPF parameters ordered in a sequence according to a measure of an amount of clutter rejected.

26. The adaptive clutter filtering method of claim 25, wherein the iterative loop sequentially searches the group of sets of HPF parameters for the optimal set of HPF parameters according to the measure, and exits when either a clutter status change occurs, or when a last set of HPF parameters in the sequence has been reached.

27. The adaptive clutter filtering method of claim 26, wherein the iterative loop performs a binary search for the optimal set of HPF parameters from the group of sets of HPF parameters, starting from a middle of the sequence, and stopping when either a clutter status change occurs or when an extreme set of HPF parameters is reached.

28. The adaptive clutter filtering method of claim 26, wherein selecting a set of HPF parameters corresponds to changing a passband frequency cutoff value and sharpness of a frequency transition band.

29. The adaptive clutter filtering method of claim 26, wherein computing the magnitude related index includes computing a total power of the filtered I/Q data.

30. The adaptive clutter filtering method of claim 26, wherein computing the magnitude related index includes computing a sum of absolute values of the In phase component and of the Quadrature component of the filtered I/Q data.

31. The adaptive clutter filtering method of claim 26, wherein computing the phase change related index includes computing a real part and an imaginary part of an autocorrelation of the filtered I/Q data.

32. The adaptive clutter filtering method of claim 26, wherein the magnitude threshold test includes comparing the magnitude related index to a predefined clutter magnitude threshold that is a function of an iteration number.

33. The adaptive clutter filtering method of claim 26, wherein the magnitude threshold test includes comparing the magnitude related index to a predefined clutter magnitude threshold that is a function of a currently selected set of HPF parameters.

34. The adaptive clutter filtering method of claim 26, wherein the testing includes comparing |y| with C*x, where x and y denote the real and imaginary parts of an autocorrelation of the In phase and Quadrature components of the filtered I/Q data, and C is a predefined constant that corresponds to the largest possible phase change per pulse repetition period for clutter.

35. The adaptive clutter filtering method of claim 26, further comprising:

processing a predetermined percentage of pixels in a color flow region of interest;

after the processing, adjusting the initial set of HPF parameters based on statistics of previously selected sets of HPF parameters; and

processing pixels of the color flow region of interest not previously processed based on the adjusting.

36. The adaptive clutter filtering method of claim 26, wherein a maximum number of iterations allowed for selecting a set of HPF parameters is larger in a predefined central portion of a color flow region of interest, and smaller in other portions.

12. A system comprising:

a processor configured to iteratively select an optimal filter from an ordered set of filters for filtering out clutter from ultrasound image data while collecting ultrasound image data, using as criteria for selecting the optimal filter

whether a filtered signal is less than a signal threshold, and

whether a magnitude of frequency of the filtered signal is less than a frequency threshold, which is determined by whether a phase shift is less than a phase shift threshold, which in turn is determined by whether a real part of a first order autocorrelation of the filtered signal is greater than a constant times an imaginary part of the autocorrelation, where the constant is determined by the frequency threshold.

13. A method comprising:

iteratively selecting an optimal filter for filtering, and

filtering out clutter from ultrasound color flow data.

24. An adaptive clutter filtering method for color flow imaging that selects an optimal set of High Pass Filter (HPF) parameters based on an In phase component and Quadrature component of a data packet (I/Q data) for each pixel location in an image, the adaptive clutter filtering method having an iterative loop comprising:

applying an initial set of HPF parameters to the I/Q data to produce filtered I/Q data;

computing a magnitude related index and a phase change related index of the filtered I/Q data;

testing the magnitude related index and phase change related index using corresponding threshold tests, based on predefined threshold constants, to determine if clutter is present in the filtered I/Q data; and

deciding whether to

repeat the applying, the computing, the testing and the deciding using another set of HPF parameters, or

exit the iterative loop and compute flow related parameters for color flow image formation.

37. A method comprising:

collecting ultrasound data;

iteratively selecting an optimal filter from an ordered set of filters for filtering out clutter from an ultrasound image signal while collecting the ultrasound data, including

filtering the ultrasound image signal to produce a filtered ultrasound image signal, and

determining

whether the filtered ultrasound image signal is less than a signal threshold, and

whether a magnitude of frequency is less than a frequency threshold, by determining whether a phase shift is less than a phase shift threshold, which in turn is determined by whether a real part of a first order autocorrelation of the filtered ultrasound signal is greater than a constant times an imaginary part of the autocorrelation, where the constant is determined by the frequency threshold.

38. A system comprising:

means for iteratively selecting an optimal filter from an ordered set of filters for filtering out clutter from ultrasound image signal while collecting the ultrasound data, including

means for performing an initial filtering of the ultrasound image signal to produce a filtered ultrasound image signal, and

means for determining

whether the ultrasound image signal is less than a signal threshold, and

whether a magnitude of frequency is less than a frequency threshold, having means for determining whether a phase shift is less than a phase shift threshold having means for determining whether a real part of a first order autocorrelation of the ultrasound image signal is greater than a constant times an imaginary part of the autocorrelation, where the constant is determined by the frequency threshold.