# Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?

arXiv: https://arxiv.org/abs/2505.12978 · HTML: https://mlsanity.com/paper/2505.12978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 9 of 9)

- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models](https://mlsanity.com/paper/2302.03018/) (2023)
- [SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI](https://mlsanity.com/paper/2111.07220/) (2021)
- [Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering](https://mlsanity.com/paper/2111.09109/) (2021)
- [Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies](https://mlsanity.com/paper/2401.16564/) (2024)
- [Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study](https://mlsanity.com/paper/2007.00121/) (2020)
- [Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study](https://mlsanity.com/paper/2304.00996/) (2023)
- [High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging](https://mlsanity.com/paper/2310.20389/) (2023)

## Abstract (arXiv)

Diffusion MRI (dMRI) is essential for studying brain microstructure, but high-resolution imaging remains challenging due to the inherent trade-offs between acquisition time and signal-to-noise ratio (SNR). Conventional methods often optimize only the diffusion-weighted images (DWIs) without considering their relationship with the non-diffusion-weighted (b=0) reference images. However, calculating diffusion metrics, such as the apparent diffusion coefficient (ADC) and diffusion tensor with its derived metrics like fractional anisotropy (FA) and mean diffusivity (MD), relies on the ratio between each DWI and the b=0 image, which is crucial for clinical observation and diagnostics. In this study, we demonstrate that solely enhancing DWIs using a conventional pixel-wise mean squared error (MSE) loss is insufficient, as the error in ratio between generated DWIs and b=0 diverges. We propose a novel ratio loss, defined as the MSE loss between the predicted and ground-truth log of DWI/b=0 ratios. Our results show that incorporating the ratio loss significantly improves the convergence of this ratio error, achieving lower ratio MSE and slightly enhancing the peak signal-to-noise ratio (PSNR) of generated DWIs. This leads to improved dMRI super-resolution and better preservation of b=0 ratio-based features for the derivation of diffusion metrics.