System and method for adaptive clutter filtering in ultrasound color flow imaging

• US 20040199078A1
• Filed: 04/16/2004
• Published: 10/07/2004
• 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, Mo, Larry Y. L., McLaughlin, Glen W., Chou, Ching-Hua
• Granted Patent: US 6,997,876 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

A system and method for adaptive clutter filtering in 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 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.

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 the filtering is performed in real time while imaging.

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

8. The system of claim 7 in which the finite number of filters is two.

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

10. The system of claim 9 wherein a criterion for selecting the optimal filter further comprises if a filtered signal is less than a threshold.

11. The system of claim 9 wherein a criterion for selecting the optimal filter further comprises if a magnitude of a portion of a filtered signal that is within a predetermined frequency range is less than a threshold.

12. The system of claim 9 wherein a criterion for selecting the optimal filter further comprises if a total energy of the filtered signal is less than an energy threshold.

13. The system of claim 9 wherein the filtering is performed over each packet of flow data that corresponds to an acoustic point in a color flow region of interest.

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

15. The system of claim 9 further comprising a finite number of filters from which the optimal filter is selected.

16. The system of claim 15 in which the finite number of filters is two.

17. A method comprising:

iteratively selecting an optimal filter for filtering wherein the 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; and

filtering out clutter from ultrasound color flow data.

18. The method of claim 17 wherein the filtering comprises filtering an individual packet of the ultrasound color flow data that corresponds to an acoustic point in a region of interest.

19. The method of claim 17 wherein the filtering is performed in real time while collecting the ultrasound color flow data.

20. The method of claim 17 wherein the iteratively selecting further comprises determining if a filtered signal is less than a threshold.

21. The method of claim 17 wherein the iteratively selecting further comprises determining if a magnitude of a filtered signal is less than a than a frequency threshold.

22. The method of claim 17 wherein the iteratively selecting further comprises selecting a filter from a finite number of filters.

23. The method of claim 17 wherein the iteratively selecting further comprises selecting one of two filters.

24. The method of claim 17 wherein the iteratively selecting further comprises determining if a magnitude of a color flow signal in a preselected frequency range is less than a color flow signal threshold wherein a phase shift is less than a phase shift threshold.

25. A method comprising:

iteratively selecting an optimal filter for filtering wherein the iteratively selecting comprises determining if a magnitude of a color flow signal in a preselected frequency range is less than a color flow signal threshold, wherein the 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, and

filtering out clutter from ultrasound color flow data.

26. The method of claim 25 wherein the filtering comprises filtering an individual packet of the ultrasound color flow data that corresponds to an acoustic point in a region of interest.

27. The method of claim 25 wherein the filtering is performed in real time while collecting the ultrasound color flow data.

28. The method of claim 25 wherein the iteratively selecting further comprises determining if a filtered signal is less than a threshold.

29. The method of claim 25 wherein the iteratively selecting further comprises determining if a magnitude of frequency of a filtered signal is less than a frequency threshold.

30. The method of claim 25 wherein determining further comprises determining whether a phase shift is less than a phase shift threshold.

31. The method of claim 25 wherein the iteratively selecting further comprises selecting a filter from a finite number of filters.

32. The method of claim 25 wherein the iteratively selecting further comprises selecting one of two filters.