TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks

• URL: http://arxiv.org/abs/2509.16429v1
• Date: Fri, 19 Sep 2025 21:10:13 GMT
• Title: TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks
• Authors: Itzik Waizman, Yakov Gusakov, Itay Benou, Tammy Riklin Raviv,
• Abstract summary: White matter tractography is an advanced technique that reconstructs the 3D white matter pathways of the brain from diffusion MRI data.<n>We propose a novel tractography method that leverages Transformers to model the sequential nature of white matter streamlines.<n>We present our results on the TractoInferno dataset, demonstrating strong generalization to real-world data.
• Score: 8.003036966340273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: White matter tractography is an advanced neuroimaging technique that reconstructs the 3D white matter pathways of the brain from diffusion MRI data. It can be framed as a pathfinding problem aiming to infer neural fiber trajectories from noisy and ambiguous measurements, facing challenges such as crossing, merging, and fanning white-matter configurations. In this paper, we propose a novel tractography method that leverages Transformers to model the sequential nature of white matter streamlines, enabling the prediction of fiber directions by integrating both the trajectory context and current diffusion MRI measurements. To incorporate spatial information, we utilize CNNs that extract microstructural features from local neighborhoods around each voxel. By combining these complementary sources of information, our approach improves the precision and completeness of neural pathway mapping compared to traditional tractography models. We evaluate our method with the Tractometer toolkit, achieving competitive performance against state-of-the-art approaches, and present qualitative results on the TractoInferno dataset, demonstrating strong generalization to real-world data.

Related papers

• GenTract: Generative Global Tractography [8.597313702333148]
  GenTract is the first generative model for global tractography.<n>It produces tractograms with high precision on research-grade data.
  arXiv  Detail & Related papers  (2025-11-17T09:43:57Z)
• Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information [6.669798532124614]
  Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain.<n>It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders.<n>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.
  arXiv  Detail & Related papers  (2025-03-05T10:02:35Z)
• Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding [9.606654786275902]
  In this paper, we propose a novel method called Polyhedra Transformer (PE-Transformer) for dMRI, designed specifically to handle spherical signals.<n>Our approach involves projecting an icosahedral unit sphere to resample signals from predetermined directions. These resampled signals are then transformed into embeddings, which are processed by a transformer encoder that incorporates orientational information reflective of the icosahedral structure.
  arXiv  Detail & Related papers  (2025-01-23T03:32:52Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Learning Spatially-Continuous Fiber Orientation Functions [1.4504054468850665]
  We propose FENRI, a novel method that learns spatially-continuous fiber orientation density functions from low-resolution diffusion-weighted images. We demonstrate that FENRI accurately predicts high-resolution fiber orientations from realistic low-quality data.
  arXiv  Detail & Related papers  (2023-12-10T01:28:47Z)
• TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation [63.842881844791094]
  Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation. We propose TractCloud, a registration-free framework that performs whole-brain tractography parcellation directly in individual subject space.
  arXiv  Detail & Related papers  (2023-07-18T06:35:12Z)
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
  We propose a domain transfer approach based on conditional invertible neural networks (cINNs) Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood. Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
  arXiv  Detail & Related papers  (2023-03-17T18:00:27Z)
• Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions [68.41088365582831]
  White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. Most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient parcellation of 198 SWM clusters from whole-brain tractography.
  arXiv  Detail & Related papers  (2022-07-18T23:07:53Z)
• Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction [110.61383502442598]
  We introduce a novel neural network framework termed Cross-Modal Message Propagation Network (CMMPNet) CMMPNet is composed of two deep Auto-Encoders for modality-specific representation learning and a tailor-designed Dual Enhancement Module for cross-modal representation refinement. Experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction.
  arXiv  Detail & Related papers  (2021-11-30T04:30:10Z)
• A computational geometry approach for modeling neuronal fiber pathways [10.741721423684305]
  Tractography constructs complex neuronal fibers in three dimensions that exhibit the geometry of white matter pathways in the brain. We develop a computational geometry-based tractography representation that aims to simplify the connectivity of white matter fibers. Using diffusion MRI data from Alzheimer's patient study, we extract tractography features from our model for distinguishing the Alzheimer's subject from the normal control.
  arXiv  Detail & Related papers  (2021-08-02T21:16:29Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.