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

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.

[1]  Brendt Wohlberg,et al.  Plug-and-Play priors for model based reconstruction , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[2]  Steen Moeller,et al.  Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues , 2020, IEEE Signal Processing Magazine.

[3]  Karl Kunisch,et al.  A Bilevel Optimization Approach for Parameter Learning in Variational Models , 2013, SIAM J. Imaging Sci..

[4]  Michael Elad,et al.  Calibrationless parallel imaging reconstruction based on structured low‐rank matrix completion , 2013, Magnetic resonance in medicine.

[5]  Steen Moeller,et al.  Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging , 2018, Magnetic resonance in medicine.

[6]  M. Sekiya,et al.  Measurement of Cardiac Chamber Volumes by Cine Magnetic Resonance Imaging , 1993, Angiology.

[7]  Kaushik Mitra,et al.  Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior , 2018, IEEE Transactions on Computational Imaging.

[8]  Michael Elad,et al.  ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA , 2014, Magnetic resonance in medicine.

[9]  C. Dumoulin,et al.  Magnetic resonance angiography. , 1986, Radiology.

[10]  Dudley J Pennell,et al.  The histologic basis of late gadolinium enhancement cardiovascular magnetic resonance in hypertrophic cardiomyopathy. , 2004, Journal of the American College of Cardiology.

[11]  Leon Axel,et al.  On compressed sensing in parallel MRI of cardiac perfusion using temporal wavelet and TV regularization , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Thomas Pock,et al.  Learning a variational network for reconstruction of accelerated MRI data , 2017, Magnetic resonance in medicine.

[13]  H. Wen,et al.  DENSE: displacement encoding with stimulated echoes in cardiac functional MRI. , 1999, Journal of magnetic resonance.

[14]  Jeffrey A. Fessler,et al.  Optimization methods for MR image reconstruction , 2019, 1903.03510.

[15]  Bruce R. Rosen,et al.  Image reconstruction by domain-transform manifold learning , 2017, Nature.

[16]  Carola-Bibiane Schönlieb,et al.  Adversarial Regularizers in Inverse Problems , 2018, NeurIPS.

[17]  Frank Ong,et al.  ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging , 2017, Scientific Reports.

[18]  Mathews Jacob,et al.  Model based image reconstruction using deep learned priors (MODL) , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[19]  H. Hricak,et al.  Magnetic resonance imaging with respiratory gating: techniques and advantages. , 1984, AJR. American journal of roentgenology.

[20]  Vivek Muthurangu,et al.  Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease , 2018, Magnetic resonance in medicine.

[21]  James Demmel,et al.  Fast $\ell_1$ -SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime , 2012, IEEE Transactions on Medical Imaging.

[22]  Leslie Ying,et al.  Joint image reconstruction and sensitivity estimation in SENSE (JSENSE) , 2007, Magnetic resonance in medicine.

[23]  Daniel K Sodickson,et al.  Assessment of the generalization of learned image reconstruction and the potential for transfer learning , 2019, Magnetic resonance in medicine.

[24]  Charles A. Bouman,et al.  Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery , 2019, IEEE Signal Processing Magazine.

[25]  R. Edelman,et al.  First-pass cardiac perfusion: evaluation with ultrafast MR imaging. , 1990, Radiology.