High-sensitivity and real-time ultrasound blood flow imaging based on adaptive and localized spatiotemporal clutter filtering
View Patent ↗Described here are systems and methods for ultrasound clutter filtering to produce images of blood flow in a subject. The systems and methods described in the present disclosure may be advantageously applied to fast ultrasound imaging techniques, including ultrafast plane wave imaging techniques.
1 . A method for generating an image that depicts blood flow in a subject from ultrasound data acquired using an ultrasound imaging system, the method comprising:
(a) accessing ultrasound data acquired from a subject with the ultrasound imaging system;
(b) generating a plurality of weighted ultrasound data sets, each weighted ultrasound data set being generated by applying a different weighting function to the ultrasound data, wherein each different weighting function applies a different spatial weighting to the ultrasound data such that each weighted ultrasound data set comprises a spatial subset of the ultrasound data localized to a spatial region defined by the respective weighting function;
(c) estimating blood flow signal data for each weighted ultrasound data set by separately tissue clutter filtering each of the weighted ultrasound data sets; and
(d) generating a blood flow image from the estimated blood flow signal data by combining the blood flow signal data from the plurality of weighted ultrasound data sets, wherein the blood flow image depicts blood flow in the subject.
2 . The method of claim 1 , wherein generating the blood flow image comprises generating combined blood flow signal data by combining the blood flow signal data from the plurality of different weighted ultrasound data sets and generating the blood flow image from the combined blood flow signal data.
3 . The method of claim 2 , wherein generating the combined blood flow signal data comprises weighting each blood flow signal data by a weight before combining the blood flow signal data from the plurality of different weighted ultrasound data sets.
4 . The method of claim 3 , wherein each blood flow signal data is weighted by a different weight.
5 . The method of claim 3 , wherein the weight applied to a respective one of the blood flow signal data is dependent on at least one of an intensity of the respective one of the blood flow signal data, a power of the respective one of the blood flow signal data, or a velocity of the respective one of the blood flow signal data.
6 . The method of claim 1 , wherein generating the blood flow image comprises generating a different blood flow image from the blood flow signal data from each of the plurality of different weighted ultrasound data sets and combining the different blood flow images to generate the blood flow image.
7 . The method of claim 6 , wherein combining the different blood flow images to generate the blood flow image comprises weighting each different blood flow image by a weight before combining the different blood flow images to generate the blood flow image.
8 . The method of claim 7 , wherein each different blood flow image is weighted by a different weight.
9 . The method of claim 7 , wherein the weight applied to a respective one of the different blood flow images is dependent on at least one of an intensity of the respective one of the different blood flow images, a power of the respective one of the different blood flow images, or a velocity of the respective one of the different blood flow images.
10 . The method of claim 1 , wherein each different weighting function comprises one of a Gaussian window, a triangular window, a Welch window, a sine window, or a Hamming window.
11 . The method of claim 1 , wherein the tissue clutter filtering is based on an eigenvalue decomposition of each weighted ultrasound data set.
12 . The method of claim 1 , wherein the tissue clutter filtering is based on a singular value decomposition of each weighted ultrasound data set.