Abstract #3563

TransUnet Based Deep Learning with Tissue-weighted Loss Function for Accelerated Magnetic Resonance Fingerprint Reconstruction

Jintao Wei^1,2, Zihan Zhou^3,4, Bo Dong^1,2, Paween Wongkornchaovalit^1,2, HuiHui Ye⁵, and Hongjian He^1,6,7

¹Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, China, ²College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, ³Department of Radiology, Stanford university, Stanford, CA, United States, ⁴Graduate School of Education, Stanford university, Stanford, CA, United States, ⁵State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, ⁶School of Physics, Zhejiang University, Hangzhou, China, ⁷State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China

Synopsis

Keywords: MR Fingerprinting, Image Reconstruction, transformer, MRF, reconstruction, reproducibility

Motivation: MRF data with short time frames make dictionary matching challenging with large quantitative errors.

Goal(s): We proposed a deep learning method by taking account in global Information and tissue-specific relaxometry, for better quantitative results using shorter frames than dictionary matching with good reproducibility.

Approach: We proposed to combine Unet with an embedded transformer and a tissue-weighted loss function. We compared our approach to dictionary matching and examined various levels of reduced scan time.

Results: The proposed method has less error with shorter frame data comparing dictionary matching. This is more effective for the gray matter and for scan times shorter than 300 frames.

Impact: The proposed method improves the problem of poor quality of reconstructed images for dictionary matching in shorter frames, in addition this method has good repeatability and provides a powerful reconstruction algorithm for MRF sequence acceleration.

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