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Abstract #3988

A Learning-based Method for Quantifying the Fraction of Unsaturated Fatty Acid in Bone Marrow

Chaoxing Huang1, Ziqin Zhou1,2, Zijian Gao1, Vincent Wai Sun Wong3, Winnie Chiu Wing Chu1, and Weitian Chen1
1Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong, 2MR Research Collaboration, Siemens Healthineers, Kowloon, Hong Kong, 3Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong

Synopsis

Keywords: Analysis/Processing, MSK

Motivation: The level of unsaturated fatty acid in bone is an useful biomarker for assessing the health condition of bone.

Goal(s): Predict the fraction of unsaturated fatty acid from a reduced number of gradient echo image data and the uncertainty level simultaneously.

Approach: A Bayesian neural network is trained to refine the coarse parametric map fitted by a reduced number of images to the parametric map fitted by the full number of scans.

Results: Experiments on knee data show that the model can achieve a relative error of less than 5% and the uncertainty prediction reflects the confidence.

Impact: The feasibility of mapping the fraction of unsaturated fatty acid from a reduced number of imaging data in a learning-based way is validated.

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Keywords