fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.
By Florian Knoll, Jure Zbontar, Anuroop Sriram, Matthew Muckley, M. Bruno, Aaron Defazio, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana and 13 more. Radiology: Artificial Intelligence, 2020
514 citations recorded by the source corpus.
A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.
Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/025e77d28d5554d2e49de25f047f1a93341d0766
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In Biomedical Engineering
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Solving Inverse Problems in Medical Imaging with Score-Based Generative Models (2021, methodology): “We simulate measurements by computing the k-space with Fast Fourier Transform, and follow [22, 23] to undersample the k-space with an equispaced Cartesian mask.”
- Federated learning for medical image analysis: A survey (2023, background): “4) Knee: fastMRI: The fastMRI [149], [158] 18 is a large-scale dataset for medical image reconstruction using machine learning approaches.”
- Robust Compressed Sensing MRI with Deep Generative Priors (2021, methodology): “Note that we use the true acceleration factor R, and this does not match the values in fastMRI [23] 1.”
- Transformers in Medical Image Analysis: A Review (2022, background): “[112] Brain IXI2; fastMRI [113] PSNR, SSIM Zero-shot MRI Reconstruction”
- Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021, background): “The first consists of the release of a large data set of raw k-space and DICOM images [25], [26].”