公开(公告)号：US20240046464A1

公开(公告)日：2024-02-08

申请号：US18277500

申请日：2022-02-02

申请人： Frederick H. EPSTEIN ,  Changyu SUN ,  Sona QADIMI ,  Yu WANG ,  UNIVERSITY OF VIRGINIA PATENT FOUNDATION

发明人： Frederick H. EPSTEIN ,  Changyu SUN ,  Sona QADIMI ,  Yu WANG

IPC分类号： G06T7/00 ,  G06T3/40 ,  G06T7/246 ,  G16H50/20

CPC分类号： G06T7/0012 ,  G06T3/40 ,  G06T7/246 ,  G16H50/20 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20084 ,  G06T2207/30048

摘要： An exemplary method and system are disclosed that employ DENSE deep learning neural-network(s) trained with displacement-encoded imaging data (i.e., DENSE data) to estimate intramyocardial motion from cine MRI images and other cardiac medical imaging modalities, including standard cardiac computer tomography (CT) images, magnetic resonance imaging (MRI) images, echocardiogram images, heart ultrasound images, among other medical imaging modalities described herein. The DENSE deep learning neural-network(s) can be configured (trained) using (i) contour motion data from displacement-encoded imaging magnitude data as inputs to the neural network and (ii) displacement maps derived from displacement-encoded imaging phase images for comparison to the outputs of the neural network for neural network adjustments during the training.