# Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

arXiv: https://arxiv.org/abs/1810.02683 · HTML: https://mlsanity.com/paper/1810.02683/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [Multi-modal learning for predicting the genotype of glioma](https://mlsanity.com/paper/2203.10852/) (2022)
- [Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI](https://mlsanity.com/paper/2106.13188/) (2021)
- [Generative Aging of Brain Images with Diffeomorphic Registration](https://mlsanity.com/paper/2205.15607/) (2022)
- [Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation](https://mlsanity.com/paper/2305.14589/) (2023)
- [Diffusion Bridge Models for 3D Medical Image Translation](https://mlsanity.com/paper/2504.15267/) (2025)
- [Predicting conversion of mild cognitive impairment to Alzheimer's disease](https://mlsanity.com/paper/2203.04725/) (2022)
- [Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis](https://mlsanity.com/paper/2505.09323/) (2025)
- [SRUG: A Fusion-Driven Generator Network for Medical Image Translation](https://mlsanity.com/paper/2601.04785/) (2026)
- [Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging](https://mlsanity.com/paper/2108.04135/) (2021)
- [Macro2Micro: A Rapid and Precise Cross-modal Magnetic Resonance Imaging Synthesis using Multi-scale Structural Brain Similarity](https://mlsanity.com/paper/2412.11277/) (2024)
- [Revolutionizing Brain Tumor Imaging: Generating Synthetic 3D FA Maps from T1-Weighted MRI using CycleGAN Models](https://mlsanity.com/paper/2505.03662/) (2025)
- [Multi-modal MRI-Based Alzheimer's Disease Diagnosis with Transformer-based Image Synthesis and Transfer Learning](https://mlsanity.com/paper/2601.11614/) (2026)

## Abstract (arXiv)

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.