mlsanityData to 14 Oct 2026

Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset

arXiv 2412.06666 ↗First posted 9 Dec 2024eess.IV cs.CV physics.med-phcited by 5

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Abstract (arXiv)

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 9 of its 100 references that are in the map. First posted on arXiv 9 Dec 2024.

Builds on

9 of 100 references.

References · 9

  1. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  2. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  3. 2015Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shiftscited by 13
  4. 2024Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learningcited by 11
  5. 2017Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codescited by 8
  6. 2018Complex diffusion-weighted image estimation via matrix recovery under general noise modelscited by 8
  7. 2024CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learningcited by 5
  8. 2024Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imagingcited by 1
  9. 2024Artificial Intelligence for Neuro MRI Acquisition: A Reviewcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

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  1. 2024X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models
  2. 2024Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
  3. 2024When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation
  4. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning
  5. 2026DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
  6. 2022Robust, fast and accurate mapping of diffusional mean kurtosis ↗
  7. 2025Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction
  8. 2024Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution