# Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning

arXiv: https://arxiv.org/abs/2206.00305 · HTML: https://mlsanity.com/paper/2206.00305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning](https://mlsanity.com/paper/2011.01355/) (2020)
- [SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI](https://mlsanity.com/paper/2111.07220/) (2021)
- [Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI](https://mlsanity.com/paper/1905.04176/) (2019)

## Built on by (most cited first, 1 of 1)

- [MBD: Multi b-value Denoising of Diffusion Magnetic Resonance Images](https://mlsanity.com/paper/2410.16898/) (2024)

## Abstract (arXiv)

In this paper, we propose a method for denoising diffusion-weighted images (DWI) of the brain using a convolutional neural network trained on realistic, synthetic MR data. We compare our results to averaging of repeated scans, a widespread method used in clinics to improve signal-to-noise ratio of MR images. To obtain training data for transfer learning, we model, in a data-driven fashion, the effects of echo-planar imaging (EPI): Nyquist ghosting and ramp sampling. We introduce these effects to the digital phantom of brain anatomy (BrainWeb). Instead of simulating pseudo-random noise with a defined probability distribution, we perform noise scans with a brain-DWI-designed protocol to obtain realistic noise maps. We combine them with the simulated, noise-free EPI images. We also measure the Point Spread Function in a DW image of an AJR-approved geometrical phantom and inter-scan movement in a brain scan of a healthy volunteer. Their influence on image denoising and averaging of repeated images is investigated at different signal-to-noise ratio levels. Denoising performance is evaluated quantitatively using the simulated EPI images and qualitatively in real EPI DWI of the brain. We show that the application of our method allows for a significant reduction in scan time by lowering the number of repeated scans. Visual comparisons made in the acquired brain images indicate that the denoised single-repetition images are less noisy than multi-repetition averaged images. We also analyse the convolutional neural network denoiser and point out the challenges accompanying this denoising method.