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Abstract #2175

Deep Learning Enhanced T1 Mapping and Reconstruction Framework with Spatial-temporal and Physical Constraint

Yuze Li1, Huijun Chen1, Haikun Qi2, Zhangxuan Hu3, Zhensen Chen1, Runyu Yang1, Huiyu Qiao1, Jie Sun4, Tao Wang5, Xihai Zhao1, Hua Guo1, and Huijun Chen1
1Center for Biomedical Imaging Research, Medical School, Tsinghua University, Beijing, China, 2School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, 3GE Healthcare, Beijing, China, 4Vascular Imaging Lab and BioMolecular Imaging Center, Department of Radiology, University of Washington, Seattle, Seattle, WA, United States, 5Department of Neurology, Peking University Third Hospital, Beijing, China

A Deep learning enhAnced T1 parameter mappIng and recoNstruction framework using spatial-Temporal and phYsical constraint (DAINTY) was proposed. DAINTY explicitly imposed low rank and sparsity constraints on the multi-frame T1 weighted images to exploit the spatial-temporal correlation. A deep neural network was used to efficiently perform T1 mapping as well as denoise and reduce under-sampling artifacts. More importantly, smooth and accurate T1 maps generated from the neural network were transformed to T1 weighted images using the physical model, which the transformed T1 weighted images were also refined. Combining refined images and intermediate reconstructed images, the image quality was greatly improved. Results of simulation and in-vivo datasets showed DAINTY can achieve higher performance than compared methods.

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