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

arXiv: https://arxiv.org/abs/2102.09069 · HTML: https://mlsanity.com/paper/2102.09069/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [High-fidelity, high-isotropic resolution diffusion imaging through gSlider acquisition with B1+ & T1 corrections and integrated DeltaB0/Rx shim array](https://mlsanity.com/paper/1811.05473/) (2018)

## Built on by (most cited first, 4 of 4)

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [Enhance the Image: Super Resolution using Artificial Intelligence in MRI](https://mlsanity.com/paper/2406.13625/) (2024)
- [Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI](https://mlsanity.com/paper/2605.15895/) (2026)
- [dMRI-F: A Foundation Model for Generalizable Diffusion MRI Analysis from Sparse Acquisitions](https://mlsanity.com/paper/2610.17968/) (2026)

## Abstract (arXiv)

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.