Automatic optimization for ultrasound medical imaging

• US 7,288,068 B2
• Filed: 12/15/2003
• Issued: 10/30/2007
• Est. Priority Date: 12/15/2003
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Simopoulos, Constantine, Steins, Robert W., Bakircioglu, Müge M.
• Primary Examiner(s): Jaworski; Francis J.
• Application Number:
  US10/737,204
• Publication Number:
  US 20050131300A1
• Time in Patent Office: 1,415 Days
• Field of Search: 600440-441, 600453-458, 128/916
• US Class Current: 600/455
• 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

Abstract

Methods and systems are provided for automatic optimization for ultrasound medical imaging. In one approach, velocity values are unwrapped to avoid aliasing artifacts. Multi-dimensional phase unwrapping is applied to the velocity data. The unwrapped velocity information is used to optimize one or both of the velocity scale (e.g., pulse repetition frequency) and the imaging frequency. For optimizing the scale setting, the distribution of unwrapped velocities from a systolic time period of the heart cycle are used to identify the pulse repetition frequency. For optimizing the imaging frequency, a correlation as a function of depth shows the penetration depth for a given imaging frequency. In a dependent or independent approach, one or more thresholds for velocity or energy in flow imaging are adaptively selected as a function of an amount of clutter. Velocity or other energy information in addition to the clutter information may be used for selecting the thresholds. In yet another dependent or independent approach, displacement of an imaging plane or other change is detected and used to trigger an automatic update of an imaging parameter for a same or different mode.

Claims

1. In a method for automatic optimization in color Doppler velocity imaging, an improvement comprising:

(a) applying multidimensional phase unwrapping to a set of velocity data representing a multidimensional region;

(b) setting a velocity scale, baseline, a parameter for auto-Doppler tracking, a persistence parameter, a spatial filter parameter, a threshold, a clutter filter parameter, an imaging frequency, or combinations thereof as a function of results of (a); and

(c) imaging as a function of the setting.

2. The method of claim 1 wherein (b) comprises:

(b1) determining a histogram of unwrapped velocities of the set of velocity data; and

(b2) selecting the velocity scale as a function of distribution of the histogram.

3. The method of claim 1 wherein (a) comprises determining a two dimensional phase unwrapping to each value of the set of velocity data, the multidimensional region being an area.

4. The method of claim 1 wherein (a) comprises determining a three dimensional phase unwrapping to each value of the set of velocity data, the multidimensional region being a volume.

5. The method of claim 1 further comprising:

(d) setting thresholds as a function of the results of (a) and a measure of clutter.

6. The method of claim 1 further comprising:

(d) identifying the set of velocity data prior to (a) as associated with a systole period of a heart cycle.

7. The method of claim 1 wherein (b) comprises setting the imaging frequency as a function of the results of (a), the imaging frequency being for flow imaging.

8. The method of claim 7 wherein (b) comprises:

(b1) determining a correlation as a function of depth between two frames of velocity data, one of the frames of velocity data being a function of the results of (a); and

(b2) reducing the imaging frequency in response to a decrease in the correlation at greater depths.

9. A system for automatic optimization in velocity imaging, the system having at least one processor operable to implement acts (a) and (b) of claim 1.

10. The method of claim 1 further comprising:

(d) detecting a displacement in an imaging region; and

(e) triggering (a) and (b) in response to (d).

11. In a method for automatic optimization in color Doppler velocity imaging, an improvement comprising:

(a) applying multidimensional phase unwrapping to a set of velocity data representing a multidimensional region wherein (a) comprises;

(a1) determining a multidimensional closed path with the gradient of the phase integrating to zero;

(a2) determining a phase for a plurality of locations along the multidimensional path; and

(b) adapting imaging as a function of the phase.

12. The method of claim 11 wherein (a1) comprises selecting phase residues along the multidimensional pat of opposite values.

13. In a method for automatic optimization in color Doppler velocity imaging, an improvement comprising:

(a) applying multidimensional phase unwrapping to a set of velocity data representing a multidimensional region;

(b) setting the velocity scale as a function of the results of (a);

(c) performing (b) as a function of the results and a user aliasing selection; and

(d) imaging as a function of the setting.

14. A method for automatic optimization of a threshold for color Doppler imaging, the method comprising:

(a) determining a clutter level as a function of energy input to and energy output from a clutter filter;

(b) selecting a threshold as a function of the clutter level; and

(c) imaging as a function of the threshold.

15. The method of claim 14 wherein (a) comprises dividing or subtracting the energy input by the energy output.

16. The method of claim 14 wherein (b) comprises selecting an energy input threshold, an energy output threshold, a velocity threshold, or combinations thereof.

17. The method of claim 16 wherein (b) comprises selecting each of the energy input threshold, the energy output threshold and the velocity threshold as a function of the clutter level, energy output and velocity.

18. The method of claim 16 wherein (b) comprises selecting at least two of the thresholds from the group of:

the energy input threshold, the energy output threshold and the velocity threshold as a function of the clutter level.

19. The method of claim 16 wherein (b) comprises selecting the energy input or energy output thresholds.

20. The method of claim 16 further comprising:

(d) identifying the clutter level as high and a velocity as low; and

(e) selecting a clutter filter as a complex notch filter as a function of the identification of (d).

21. The method of claim 14 wherein (b) comprises selecting the threshold as a function of the clutter level, energy output and velocity.

22. The method of claim 21 further comprising:

(d) applying multidimensional phase unwrapping to a set of velocity data representing a multidimensional region;

wherein (b) comprises selecting as a function of the velocity, the velocity being from results of (d).

23. The method of claim 14 wherein (b) comprises selecting the threshold for a first region and an additional threshold of a same type for a second region different than the first region.

24. The method of claim 14 further comprising:

(d) applying multidimensional phase unwrapping to a set of velocity data representing a multidimensional region; and

(e) setting an imaging frequency as a function of results of (a).

25. A system for automatic optimization of thresholds for velocity imaging, the system comprising a processor operable to perform the acts (a) and (b) of claim 14.

26. The method of claim 14 further comprising:

(d) detecting a displacement in an imaging region; and

(e) triggering (a) and (b) in response to (d).

28. The method of claim 26 further comprising:

(d) identifying the two sets of velocity data as frames of data associated with a peak systole period.

27. A method for automatic optimization in velocity imaging, the method comprising:

(a) determining a correlation as a function of depth between two sets of velocity data;

(b) altering an imaging frequency as a function of the correlation; and

(c) imaging as a function of the altered imaging frequency.

29. The method of claim 27 wherein (b) comprises decreasing the imaging frequency where the correlation decreases for greater depths.

30. The method of claim 27 further comprising:

(d) detecting a displacement in an imaging region; and

(e) triggering (a) and (b) in response to (d).

31. A method for automatic optimization of an ultrasound imaging parameter, the method comprising:

(a) detecting a displacement associated with an imaging region;

(b) automatically updating an imaging parameter selected from the group of;

a flow imaging parameter, a velocity scale, a velocity threshold, an energy threshold, an imaging frequency, a beamforming parameter, a persistence value, spatial filter value and combinations thereof in response to (a); and

(c) imaging as a function of the updated imaging parameter.

32. The method of claim 31 wherein (b) comprises:

(b1) applying multidimensional phase unwrapping to a set of velocity data representing a multidimensional region; and

(b2) setting the velocity scale, or the imaging frequency as a function of results of (b1).

33. The method of claim 31 wherein (b) comprises:

(b1) determining a clutter level as a function of energy input to and energy output from a clutter filter; and

(b2) selecting the energy threshold, or the velocity threshold as a function of the clutter level.

34. The method of claim 31 wherein (a) comprises detecting elevation, azimuth, range displacement, or combinations thereof in an azimuth and range imaging plane and wherein (b) comprises adaptively updating the imaging parameter.

35. The method of claim 31 wherein (a) comprises detecting the displacement from B-mode data representing a sub-region of the imaging region.

36. The method of claim 35 wherein (b) comprises automatically updating the flow imaging parameter.

37. The method of claim 31 wherein (a) comprises:

(a1) calculating a similarity; and

(a2) comparing the similarity or a value responsive to the similarity to a threshold.

38. The method of claim 31 wherein (a) comprises:

(a1) identifying a feature; and

(a2) comparing the feature within a first image to the feature within a second image.

39. The method of claim 31 wherein (a) comprises detecting a repositioning of a spectral Doppler gate.

40. The method of claim 31 wherein (a) comprises detecting the displacement of an imaging plane from flow data.