Whole-Heart Single Breath-Hold Cardiac Cine: A Robust Motion-Compensated Compressed Sensing Reconstruction Method

In this paper we propose a methodology to achieve single breath-hold whole-heart cine MRI with a temporal resolution of \(\sim \)46 ms and a spatial resolution of 2 \(\times \) 2 mm\(^2\) out of a previously described method (JW-tTV) for single slice reconstruction. Its feasibility is tested by itself and in comparison with another state-of-the-art reconstruction method (MASTeR); both methods are adapted to Golden Radial k-space trajectories. From a formal viewpoint, we make use of a realistic numerical phantom to have a ground truth of deformation fields so that the methods performances against noise can be quantified and the sparsity regularization parameter involved in the reconstructions can be fixed according to the signal to noise ratio. Phantom results show that the adapted JW-tTV method is more robust against noise and provides more precise motion estimations and better reconstructions than MASTeR. Both methods are then applied to the reconstruction of 12–14 short axis slices covering the whole heart on eight volunteers. Finer details are better preserved with JW-tTV. Ventricle volumes and ejection fractions were computed from the volunteers data and preliminary results show agreement with conventional multiple breath-hold Cartesian acquisitions.

[1]  Marcos Martín-Fernández,et al.  Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath‐hold cardiac cine MRI , 2016, Magnetic resonance in medicine.

[2]  Marcos Martín-Fernández,et al.  Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI , 2017, Magnetic resonance in medicine.

[3]  W. Segars,et al.  MRXCAT: Realistic numerical phantoms for cardiovascular magnetic resonance , 2014, Journal of Cardiovascular Magnetic Resonance.

[4]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[5]  S. Dymarkowski,et al.  Clinical cardiac MRI , 2005 .

[6]  Mathews Jacob,et al.  Deformation Corrected Compressed Sensing (DC-CS): A Novel Framework for Accelerated Dynamic MRI , 2014, IEEE Transactions on Medical Imaging.

[7]  M. Salman Asif,et al.  Motion‐adaptive spatio‐temporal regularization for accelerated dynamic MRI , 2013, Magnetic resonance in medicine.

[8]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[9]  M. Stuber,et al.  Compressed sensing single-breath-hold CMR for fast quantification of LV function, volumes, and mass. , 2014, JACC. Cardiovascular imaging.

[10]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[11]  Li Feng,et al.  Highly accelerated real‐time cardiac cine MRI using k–t SPARSE‐SENSE , 2013, Magnetic resonance in medicine.

[12]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[13]  Peter Boesiger,et al.  Array compression for MRI with large coil arrays , 2007, Magnetic resonance in medicine.

[14]  Dwight G. Nishimura,et al.  Rapid gridding reconstruction with a minimal oversampling ratio , 2005, IEEE Transactions on Medical Imaging.

[15]  Olaf Dössel,et al.  An Optimal Radial Profile Order Based on the Golden Ratio for Time-Resolved MRI , 2007, IEEE Transactions on Medical Imaging.

[16]  Tim Nielsen,et al.  Parameter-Free Compressed Sensing Reconstruction using Statistical Non-Local Self-Similarity Filtering , 2012 .

[17]  Emmanuel J. Candès,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..