Impatient MRI: Illinois Massively Parallel Acceleration Toolkit for image reconstruction with enhanced throughput in MRI

Much progress has been made in the design of efficient acquisition trajectories for high spatial and temporal resolution in magnetic resonance imaging (MRI). Additionally, significant developments in image reconstruction have enabled the reconstruction of reasonable images from massively undersampled or noisy data that is corrupted by a variety of physical effects, including magnetic field inhomogeneity. Translation of these techniques into clinical imaging has been impeded by the need for expertise and computational facilities to realize the potential of these methods. We present the Illinois Massively Parallel Acceleration Toolkit for Image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI), a reconstruction utility that enables advanced techniques within clinically relevant computation times by using the computational power available in low-cost graphics processing cards.

[1]  Mila Nikolova,et al.  Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery , 2005, SIAM J. Sci. Comput..

[2]  G H Glover,et al.  Simple analytic spiral K‐space algorithm , 1999, Magnetic resonance in medicine.

[3]  Zhi-Pei Liang,et al.  Anatomically constrained reconstruction from noisy data , 2008, Magnetic resonance in medicine.

[4]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[5]  Hugo R. Shi,et al.  Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity , 2005, IEEE Transactions on Signal Processing.

[6]  John D. Owens Parallel Processing for Imaging Applications , 2011 .

[7]  Jie Cheng,et al.  Programming Massively Parallel Processors. A Hands-on Approach , 2010, Scalable Comput. Pract. Exp..

[8]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[9]  Justin P. Haldar,et al.  Accelerating iterative field-compensated MR image reconstruction on GPUs , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[10]  Justin P. Haldar,et al.  Advanced MRI reconstruction toolbox with accelerating on GPU , 2011, Electronic Imaging.

[11]  Justin P. Haldar,et al.  Accelerating advanced MRI reconstructions on GPUs , 2008, J. Parallel Distributed Comput..

[12]  Jeffrey A. Fessler,et al.  Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities , 2003, IEEE Transactions on Medical Imaging.