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Abstract #2446

SRDTI: Deep learning-based super-resolution for diffusion tensor MRI

Qiyuan Tian1,2, Ziyu Li3, Qiuyun Fan1,2, Chanon Ngamsombat1, Yuxin Hu4, Congyu Liao1,2, Fuyixue Wang1,2, Kawin Setsompop1,2, Jonathan R Polimeni1,2, Berkin Bilgic1,2, and Susie Y Huang1,2
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States, 3Department of Biomedical Engineering, Tsinghua University, Beijing, China, 4Department of Electrical Engineering, Stanford University, Stanford, CA, United States

High-resolution diffusion tensor imaging (DTI) is beneficial for probing tissue microstructure in fine neuroanatomical structures, but long scan times and limited signal-to-noise ratio pose significant barriers to acquiring DTI at sub-millimeter resolution. To address this challenge, we propose a deep learning-based super-resolution method entitled “SRDTI” to synthesize high-resolution diffusion-weighted images (DWIs) from low-resolution DWIs. SRDTI employs a deep convolutional neural network (CNN), residual learning and multi-contrast imaging, and generates high-quality results with rich textural details and microstructural information, which are more similar to high-resolution ground truth than those from trilinear and cubic spline interpolation.

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