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Abstract #3226

Magnetic Resonance Fingerprinting (MRF)-based Virtual Contrast-enhanced MRI Synthesis using Deep Transfer Learning

Yimin Ni1, Joseph H.C. Lai1, Chenyang Liu1, Wen Li1, Xiang Wang1, Peilin Wang1, Ge Ren1, Jing Cai1, and Tian Li1
1The Hong Kong Polytechnic University, Hong Kong, Hong Kong

Synopsis

Keywords: Other AI/ML, MR Fingerprinting/Synthetic MR

Motivation: The use of gadolinium-based contrast agents in MRI poses risks of renal damage and nephrogenic systemic fibrosis. Current virtual-contrast-enhanced deep-learning synthesis methods depend on qualitative and multiparametric MRI, leading to inter-facility variation and prolonged scanning time.

Goal(s): We aimed to develop an MRF-based DL model for virtual contrast-enhanced (VCE) MRI synthesis to improve consistency and efficiency.

Approach: To tackle the challenge of limited MRF training data, we proposed a deep transfer learning method to synthesize contrast-enhanced T1-weighted images from contrast-free MRF.

Results: The MRF-based model effectively reconstructed contrast and spatial features, achieving performance comparable to conventional T1w/T2w-based models while reducing acquisition time.

Impact: MRF-based VCE-MRI has the potential to enhance patient safety and streamline clinical workflows by eliminating the need for contrast agents. Offering comparable synthetic accuracy to conventional T1w/T2w-based models, the MRF-based approach features a more efficient single-sequence acquisition.

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Keywords