Abstract #2446

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

Qiyuan Tian^1,2, Ziyu Li³, Qiuyun Fan^1,2, Chanon Ngamsombat¹, Yuxin Hu⁴, Congyu Liao^1,2, Fuyixue Wang^1,2, Kawin Setsompop^1,2, Jonathan R Polimeni^1,2, Berkin Bilgic^1,2, and Susie Y Huang^1,2

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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