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arXiv · 2102.09069

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

Qiyuan Tian·Ziyu Li·Qiuyun Fan·Chanon Ngamsombat·Yuxin Hu·Congyu Liao·Fuyixue Wang·Kawin Setsompop·Jonathan R. Polimeni·Berkin Bilgic·Susie Y. Huang
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Abstract

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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Qiyuan Tian, Ziyu Li, Qiuyun Fan, Chanon Ngamsombat, Yuxin Hu, Congyu Liao, Fuyixue Wang, Kawin Setsompop, Jonathan R. Polimeni, Berkin Bilgic, Susie Y. Huang. 2021-02-17. SRDTI: Deep learning-based super-resolution for diffusion tensor MRI. https://arxiv.org/abs/2102.09069

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