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arxiv: 2008.03410 · v2 · pith:EAZSMJBX · submitted 2020-08-08 · eess.IV

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

Chong Chen , Yingmin Liu , Philip Schniter , Matthew Tong , Karolina Zareba , Orlando Simonetti , Lee Potter , Rizwan Ahmad This is my paper

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keywords datamethodsacquisitionbeencardiovasculardatasetimagingk-space
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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.

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