Abstract #2429

Automatic Quantification of Total Cerebral Blood Flow from Phase-Contrast MRI Using Deep Learning

Jinwon Kim¹, Hyebin Lee², Jinhee Jang², and Hyunyeol Lee¹

¹School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Korea, Republic of, ²Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Quantitative Imaging

In this work, we aim to develop a deep learning (DL)-based processing pipeline that enables rapid and correct segmentation of brain-feeding arteries in neck phase-contrast (PC) MR images, thereby achieving accurate quantification of total cerebral blood flow (tCBF) in an automated manner. To this end, we implemented a U-Net architecture where magnitude/phase-combined PC images are provided for network training. The results suggest that the present, automated method yields accurate measurements of tCBF in comparison to ground truth values obtained from manual vessel segmentation.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.