ArticleScientific data2023
M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- MRpro: Open Framework for Model-Based, Learned, and Quantitative MR Imaging-Application to Low-Field MRI.NMR in biomedicine · 2026Article
- Advancing the Volumetric Analysis of Ultra-Low-Field Brain MRI Using Image-to-Image Translation.Magnetic resonance in medicine · 2026Article
- Article
- Noise-Level Adaptive Diffusion Priors for Zero-Shot Low-Field MRI Quality Enhancement.NMR in biomedicine · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Hybrid learning: a combination of self-supervised and supervised learning for joint MRI reconstruction and denoising in low-field MRI.Physics in medicine and biology · 2026Article
- Accelerating MRI With Longitudinally-Informed Latent Posterior Sampling.Magnetic resonance in medicine · 2026Article
- Vis-OCT Explorer: an open-source software for visible-light optical coherence tomography data processing.Biomedical optics express · 2026Article
- Real-world unified denoising for multi-organ fast MRI: a large-scale prospective validation.NPJ digital medicine · 2026Article
- Subject grounding to reduce electromagnetic interference for MRI scanners operating in unshielded environments.Magnetic resonance in medicine · 2026Article
- The development and application of magnetic surgery in clinical treatment.Biomedical engineering online · 2026Review
- WCCAN: Windowed Cross-contrast Attention Network for Multi-contrast Brain MR Image Super-resolution.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- A Trust-Guided Approach to MR Image Reconstruction With Side Information.IEEE transactions on medical imaging · 2026Article
- Multi-contrast low-field MRI acceleration with k-space progressive learning and image-space hybrid attention fusion.Medical image analysis · 2026Article
- Robust multi-coil MRI reconstruction via self-supervised denoising.Magnetic resonance in medicine · 2025Article
- UltimateSynth: MRI Physics for Pan-Contrast AI.bioRxiv : the preprint server for biology · 2025Article
- MRI at low field: A review of software solutions for improving SNR.NMR in biomedicine · 2025Review
- Denoising very low-field magnetic resonance images using native noise modeling.Frontiers in neuroimaging · 2025Article
- MRI denoising with a non-blind deep complex-valued convolutional neural network.NMR in biomedicine · 2025Article
- Facilitators and barriers in managing older chronic heart failure patients in community health care centers: a qualitative study of medical personnel's perspectives using the socio-ecological model.Frontiers in health services · 2025Article
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of ~1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.
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