US 12,329,565 B2
Cardiac flow detection based on morphological modeling in medical diagnostic ultrasound imaging
Huseyin Tek, Princeton, NJ (US); Bogdan Georgescu, Princeton, NJ (US); Tommaso Mansi, Plainsboro, NJ (US); Frank Sauer, Princeton, NJ (US); Dorin Comaniciu, Princeton, NJ (US); Helene C. Houle, San Jose, CA (US); and Ingmar Voigt, Erlangen (DE)
Assigned to Siemens Medical Solutions USA, Inc., Malvern, PA (US)
Filed by Siemens Medical Solutions USA, Inc., Malvern, PA (US)
Filed on Nov. 22, 2021, as Appl. No. 17/531,980.
Application 17/531,980 is a division of application No. 15/797,161, filed on Oct. 30, 2017, abandoned.
Prior Publication US 2022/0079552 A1, Mar. 17, 2022
Int. Cl. A61B 8/00 (2006.01); A61B 8/06 (2006.01); A61B 8/08 (2006.01); G06T 7/00 (2017.01); G16H 50/20 (2018.01)
CPC A61B 8/065 (2013.01) [A61B 8/06 (2013.01); A61B 8/0883 (2013.01); A61B 8/0891 (2013.01); A61B 8/463 (2013.01); A61B 8/465 (2013.01); A61B 8/466 (2013.01); A61B 8/469 (2013.01); A61B 8/483 (2013.01); A61B 8/488 (2013.01); A61B 8/5207 (2013.01); A61B 8/5223 (2013.01); A61B 8/5292 (2013.01); G06T 7/0012 (2013.01); G16H 50/20 (2018.01); A61B 8/468 (2013.01); A61B 8/5246 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30104 (2013.01); G06V 2201/031 (2022.01)] 11 Claims
OG exemplary drawing
 
1. A method for detecting cardiac flow in echocardiography, the method comprising:
acquiring, by an ultrasound scanner, one or more volumetric ultrasound scans including B-mode data of a heart of a patient over time;
detecting, by an image processor, two or more heart valves over time from the B-mode data of the one or more volumetric ultrasound scans, wherein detecting comprises detecting with a machine-learnt classifier;
fitting a patient specific valve model to the detected two or more heart valves;
determining, by the image processor, a confidence value indicating an accuracy of the detecting of the mitral annulus and the left ventricle outflow tract in the fit patient specific valve model, wherein determining the confidence comprises determining the confidence with the machine-learnt classifier;
placing a first sampling plane for the mitral annulus and a second sampling plane for the left ventricle outflow tract based on the fit patient specific valve model;
tracking the first and second sampling planes over time based on optical flow, boundary detection, and motion prior;
calculating, by the image processor, a cardiac flow value from flow data of the volumetric ultrasound scans for the first and second sampling planes over time, the calculating of the cardiac flow value limited to avoid flow during a portion of a heart cycle; and
outputting an image of the cardiac flow value;
wherein the image indicates the confidence value for the first and second sampling planes.