# Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information

arXiv: https://arxiv.org/abs/2503.03329 · HTML: https://mlsanity.com/paper/2503.03329/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 5 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography](https://mlsanity.com/paper/1812.05129/) (2018)
- [Generative Sampling in Bundle Tractography using Autoencoders (GESTA)](https://mlsanity.com/paper/2204.10891/) (2022)
- [TractOracle: towards an anatomically-informed reward function for RL-based tractography](https://mlsanity.com/paper/2403.17845/) (2024)
- [Anatomically Constrained Tractography of the Fetal Brain](https://mlsanity.com/paper/2403.02444/) (2024)

## Built on by (most cited first, 2 of 2)

- [DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling](https://mlsanity.com/paper/2508.04568/) (2025)
- [A foundation for systematic analysis of transformers and RNNs for tractography](https://mlsanity.com/paper/2610.01894/) (2026)

## Abstract (arXiv)

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods.