Acquiring perfusion maps from contrast-enhanced MRA using deep learning approaches
Muhammad Asaduddin1, Hong Gee Roh2, Hyun Jeong Kim3, Eung Yeop Kim4, and Sung-Hong Park1
1Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Department of Radiology, Konkuk University Medical Center, Seoul, Korea, Republic of, 3Department of Radiology, Daejeon St. Mary's Hospital, The catholic University of Korea, Daejeon, Korea, Republic of, 4Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea, Republic of
Perfusion maps and dynamic angiograms are both important for stroke/tumor treatment but commonly acquired in separate scans and thus may require additional injection of contrast agent for best results. In this work, we present a deep learning method to acquire perfusion maps from contrast-enhanced MRA data. Our results showed that an architecture of multiple decoders and an enhanced encoder produced perfusion maps that were visually and quantitatively similar to the standard DSC MRI-based perfusion maps. This approach enables us to acquire accurate perfusion maps and angiogram using a single contrast agent injection, reducing costs and risks while improving patient comfort.
This abstract and the presentation materials are available to members only;
a login is required.