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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