Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset
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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
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
- 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
- 2015Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shiftscited by 13
- 2024Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learningcited by 11
- 2017Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codescited by 8
- 2018Complex diffusion-weighted image estimation via matrix recovery under general noise modelscited by 8
- 2024CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learningcited by 5
- 2024Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imagingcited by 1
- 2024Artificial Intelligence for Neuro MRI Acquisition: A Reviewcited by 1
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Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translationcited by 8
- 2025Automated Mapping the Pathways of Cranial Nerve II, III, V, and VII/VIII: A Multi-Parametric Multi-Stage Diffusion Tractography Atlascited by 1
- 20253D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imagingcited by 0
- 2026Any-to-All MRI Synthesis: A Unified Foundation Model for Nasopharyngeal Carcinoma and Its Downstream Applicationscited by 0
- 2026CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellationcited by 0
- 2026CNsEMD: An Expert-Annotated Multi-Field-Strength MRI Dataset and a Hyperspherical Manifold Network for Multimodal Cranial Nerve Parcellation7 Sep 2026
- 2026Any-to-All MRI Synthesis: A Unified Foundation Model for Nasopharyngeal Carcinoma and Its Downstream Applications9 Feb 2026
- 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation7 Aug 2025
- 2025Automated Mapping the Pathways of Cranial Nerve II, III, V, and VII/VIII: A Multi-Parametric Multi-Stage Diffusion Tractography Atlas31 Jul 2025
- 20253D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging25 Feb 2025
Similar, not cited
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