OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging

TL;DR

This paper introduces OCMR, a comprehensive open-access dataset of multi-coil k-space cardiac MRI data, to facilitate the development and evaluation of accelerated image reconstruction methods in cardiovascular MRI.

Contribution

The paper provides the first large-scale, publicly available multi-coil k-space dataset for cardiac MRI, enabling objective comparison of reconstruction techniques.

Findings

Dataset includes 53 fully sampled and 212 undersampled series.

Facilitates benchmarking of accelerated MRI reconstruction methods.

Supports clinical translation of new imaging algorithms.

Abstract

Cardiovascular MRI (CMR) is a non-invasive imaging modality that provides excellent soft-tissue contrast without the use of ionizing radiation. Physiological motions and limited speed of MRI data acquisition necessitate development of accelerated methods, which typically rely on undersampling. Recovering diagnostic quality CMR images from highly undersampled data has been an active area of research. Recently, several data acquisition and processing methods have been proposed to accelerate CMR. The availability of data to objectively evaluate and compare different reconstruction methods could expedite innovation and promote clinical translation of these methods. In this work, we introduce an open-access dataset, called OCMR, that provides multi-coil k-space data from 53 fully sampled and 212 prospectively undersampled cardiac cine series.