Systems and methods for microvessel ultrasound imaging
View Patent ↗Described here are systems and methods for generating microvessel images from image data acquired with an ultrasound system while analyzing the image data in realtime, or retrospectively, to generate a performance descriptor that can be used to assess data quality and/or motion correction quality; to adaptively suppress noise in the data; or both.
1. A method for generating an image that depicts microvessels in a subject using an ultrasound system, the steps of the method comprising:
(a) providing to a computer system, image data acquired from a subject with an ultrasound system, wherein the image data comprise image frames obtained at a plurality of different time points;
(b) generating reformatted data with the computer system by reformatting the image data as a Casorati matrix;
(c) generating motion matrix data with the computer system by computing a similarity metric of each column of the reformatted data with every other column of the reformatted data;
(d) performing motion correction on the acquired image data by analyzing the motion matrix data with the computer system and based on this analysis generating updated image data by:
(i) directing the ultrasound system to reject image data when analysis of the motion matrix data indicates translation motion occurred when the image data were acquired;
(ii) directing the computer system to process the image data to reduce motion corruption when analysis of the motion matrix data indicates periodic motion occurred when the image data were acquired; and
(e) generating an image that depicts microvessels in the subject by reconstructing the image from the updated image data using the computer system, wherein the image depicts fewer effects of motion based on the motion correction of the updated image data.
2. The method as recited in claim 1 , further comprising generating from the motion matrix data, a data quality metric indicative of a quantitative measure of image data quality and providing the data quality metric to a user.
3. The method as recited in claim 2 , wherein the data quality metric comprises a mean of the motion matrix data.
4. The method as recited in claim 2 , wherein the data quality metric comprises a median of the motion matrix data.
5. The method as recited in claim 2 , wherein providing the data quality metric to the user comprises generating a display that indicates the data quality metric.
6. The method as recited in claim 1 , wherein steps (b)-(d) are performed in real-time as the image data are being acquired with the ultrasound system.
7. The method as recited in claim 1 , wherein steps (b)-(d) are performed after the image data have been acquired with the ultrasound system.
8. The method as recited in claim 1 , further comprising generating from the motion matrix data, a motion correction quality metric indicative of a quantitative measure of motion correction quality and providing the motion correction quality metric to a user.
9. The method as recited in claim 8 , wherein the motion correction quality metric is based on a rank of the motion matrix data.
10. The method as recited in claim 1 , wherein the reformatted data comprise a Casorati matrix, wherein each column of the Casorati matrix corresponds to a vectorized image frame obtained from a different time point.
11. The method as recited in claim 1 , wherein the ultrasound system is directed to reacquire image data that are rejected when analysis of the motion matrix data indicates translation motion occurred when the image data were acquired.
12. The method as recited in claim 1 , wherein the similarity metric is a correlation coefficient.
13. The method as recited in claim 1 , wherein the similarity metric is a covariance metric.
14. The method as recited in claim 1 , wherein the similarity metric is at least one of an angle or a magnitude of a column of the Casorati matrix.
15. The method as recited in claim 1 , wherein the similarity metric is a distance metric.
16. The method as recited in claim 15 , wherein the distance metric is one of a Euclidian distance, a Manhattan distance, a Mahalanobis distance, or a Minkowski distance.
17. The method as recited in claim 1 , wherein analyzing the motion matrix comprises deciding frame-pairs in the image data and an optimal search window size for motion tracking within the image data.
18. The method as recited in claim 1 , wherein processing the image data to reduce motion corruption includes analyzing the motion matrix to identify a reference frame for motion correction and reducing motion corruption in the image data based in part on the identified reference frame.
19. The method as recited in claim 18 , wherein the reference frame is identified from the motion matrix as the image frame having a highest similarity metric with respect to other image frames in the image data.
20. The method as recited in claim 1 , wherein analyzing the motion matrix comprises identifying image frames that experienced out-of-plane motion while the image data were acquired, and wherein the updated image data are generated by rejecting those image frames identified as experiencing out-of-plane motion.
21. The method as recited in claim 20 , wherein identifying the image frames that experienced out-of-plane motion comprises identifying image frames from the motion matrix that are associated with low coherence.
22. The method as recited in claim 21 , further comprising generating a spatiotemporal coherence map from the motion matrix and identifying the image frames that experienced out-of-plane motion using the spatiotemporal coherence map.
23. The method as recited in claim 22 , wherein the updated image data are generated by rejecting only local spatial regions identified in the spatiotemporal coherence map as being associated with out-of-plane motion.
24. The method as recited in claim 1 , further comprising estimating background noise field data from the motion matrix data using the computer system, and suppressing noise in the image by normalizing the image using the background noise field data.
25. The method as recited in claim 24 , wherein the background noise field data are estimated by computing a synthetic noise image from the motion matrix data and estimating the background noise field data as a low-rank approximation of the synthetic noise image.
26. The method as recited in claim 25 , wherein the synthetic noise image is computed by:
generating a spatiotemporal correlation image from the motion matrix data, wherein pixel values in the spatiotemporal correlation image correspond to statistical measures of the motion matrix data;
thresholding the spatiotemporal correlation image to separate flow pixels associated with flow from noise pixels associated with noise; and
generating the synthetic noise image by replacing flow pixels in the spatiotemporal correlation image with noise pixels from the spatiotemporal correlation image.
27. The method as recited in claim 26 , wherein each pixel value in the spatiotemporal correlation image is computed by computing a mean of motion matrix data in a local kernel centered on that pixel.
28. The method as recited in claim 26 , wherein generating the synthetic noise image comprises replacing each flow pixel with a noise pixel randomly selected from a local neighborhood of the flow pixel.
29. The method as recited in claim 28 , wherein the local neighborhood comprises at least one of pixels across rows, columns, or frames.
30. The method as recited in claim 25 , wherein the low-rank approximation is based on a singular value decomposition.