FastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning

Knoll F, Zbontar J, Sriram A, Muckley MJ, Bruno M, Defazio A, Parente M, Geras KJ, Katsnelson J, Chandarana H, Zhang Z, Drozdzalv M, Romero A, Rabbat M, Vincent P, Pinkerton J, Wang D, Yakubova N, Owens E, Zitnick CL, Recht MP, Sodickson DK, Lui YW (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Radiology: Artificial Intelligence Radiological Society of North America

Book Volume: 2

Article Number: e190007

Journal Issue: 1

DOI: 10.1148/ryai.2020190007

Abstract

A publicly available dataset containing k-space and image data of knee examinations for accelerated MR image reconstruction using machine learning is presented.

Authors with CRIS profile

Florian Knoll

Involved external institutions

New York University (NYU) US United States (USA) (US) Facebook, Inc. US United States (USA) (US) University of Florida US United States (USA) (US)

How to cite

APA:

Knoll, F., Zbontar, J., Sriram, A., Muckley, M.J., Bruno, M., Defazio, A.,... Lui, Y.W. (2020). FastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiology: Artificial Intelligence, 2(1). https://doi.org/10.1148/ryai.2020190007

MLA:

Knoll, Florian, et al. "FastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning." Radiology: Artificial Intelligence 2.1 (2020).

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