Abstract #1604

Self-supervised Learning Based Liver Multi-parametric Mapping in a Single Breath-hold

Chaoxing Huang^1,2, Yurui Qian¹, Jian Hou¹, Baiyan Jiang^1,3, Queenie Chan⁴, Vincent Wai-Sun Wong⁵, Winnie Chiu-Wing Chu^1,2, and Weitian Chen^1,2

¹Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong, ²CUHK Lab of AI in Radiology (CLAIR), Shatin, Hong Kong, ³Illuminatio Medical Technology Limited, Hong Kong SAR, China, ⁴Philips Healthcare, Hong Kong SAR, China, ⁵Department of Medicine and and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong

Synopsis

Keywords: Quantitative Imaging, LiverA self-supervised learning based multiparametric mapping method is proposed to map T1ρ and T2 simultaneously, by utilising the relaxation constraint in the learning process. The method was examined on a dataset of 52 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional fitting method, with reduced number of images, and reduced scan time.

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.