Published September 12, 2022 | Version v1
Dataset Open

Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

  • 1. Siemens Healthineers International AG
  • 2. Lausanne University Hospital
  • 3. University of Lausanne
  • 4. Ecole Polytechnique Federale de Lausanne

Description

This repository contains datasets acquired for the MELBA paper (https://www.melba-journal.org/papers/2022:022.html) Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI. In particular, this repository contains fully sampled raw measurement data (k-space) from high resolution MRI scans of a phantom and a collection of fruits and vegetables. The prospectively accelerated data acquired for our paper can be found at http://mridata.org. The data is stored in ISMRMRD format in a .h5 file.

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md5:8af9f05ffbdea19340acaa562757ebef
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md5:803f99666557fad7d275186dec62ec3c
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