# Generating fMRI volumes from T1-weighted volumes using 3D CycleGAN

arXiv: https://arxiv.org/abs/1907.08533 · HTML: https://mlsanity.com/paper/1907.08533/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

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

- [Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks](https://mlsanity.com/paper/2106.08557/) (2021)
- [Shape-consistent Generative Adversarial Networks for multi-modal Medical segmentation maps](https://mlsanity.com/paper/2201.09693/) (2022)
- [Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)](https://mlsanity.com/paper/2311.14148/) (2023)
- [Unified Multi-plane Autoregressive Diffusion for 3D Multi-contrast MRI Synthesis](https://mlsanity.com/paper/2610.09376/) (2026)

## Abstract (arXiv)

Registration between an fMRI volume and a T1-weighted volume is challenging, since fMRI volumes contain geometric distortions. Here we present preliminary results showing that 3D CycleGAN can be used to synthesize fMRI volumes from T1-weighted volumes, and vice versa, which can facilitate registration.