GPU implementation of prior image constrained compressed sensing (PICCS)

The Prior Image Constrained Compressed Sensing (PICCS) algorithm (Med. Phys. 35, pg. 660, 2008) has been applied to several computed tomography applications with both standard CT systems and flat-panel based systems designed for guiding interventional procedures and radiation therapy treatment delivery. The PICCS algorithm typically utilizes a prior image which is reconstructed via the standard Filtered Backprojection (FBP) reconstruction algorithm. The algorithm then iteratively solves for the image volume that matches the measured data, while simultaneously assuring the image is similar to the prior image. The PICCS algorithm has demonstrated utility in several applications including: improved temporal resolution reconstruction, 4D respiratory phase specific reconstructions for radiation therapy, and cardiac reconstruction from data acquired on an interventional C-arm. One disadvantage of the PICCS algorithm, just as other iterative algorithms, is the long computation times typically associated with reconstruction. In order for an algorithm to gain clinical acceptance reconstruction must be achievable in minutes rather than hours. In this work the PICCS algorithm has been implemented on the GPU in order to significantly reduce the reconstruction time of the PICCS algorithm. The Compute Unified Device Architecture (CUDA) was used in this implementation.

[1]  R. Siddon.  Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[2]  Xinwei Xue,et al.  Acceleration of fluoro-CT reconstruction for a mobile C-arm on GPU and FPGA hardware: a simulation study , 2006, SPIE Medical Imaging.

[3]  Michael A. Speidel,et al.  MO‐D‐332‐05: Low Dose Myocardial CT Perfusion Measurements Using Prior Image Constrained Compressed Sensing (PICCS) , 2008 .

[4]  William H. Press,et al.  Numerical recipes in C , 2002 .

[5]  Jie Tang,et al.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.

[6]  Jie Tang,et al.  Tomosynthesis via total variation minimization reconstruction and prior image constrained compressed sensing (PICCS) on a C-arm system , 2008, SPIE Medical Imaging.

[7]  A. Kak,et al.  Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm , 1984, Ultrasonic imaging.

[8]  E. Sidky,et al.  Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT , 2009, 0904.4495.

[9]  Guang-Hong Chen,et al.  Temporal Resolution Improvement using PICCS , 2009 .

[10]  J. S. Kole,et al.  Evaluation of accelerated iterative X-ray CT image reconstruction using floating point graphics hardware , 2004 .

[11]  Jie Tang,et al.  High temporal resolution cardiac cone-beam CT using a slowly rotating C-arm gantry , 2009, Medical Imaging.

[12]  Guang-Hong Chen,et al.  Perfusion measurements by micro-CT using prior image constrained compressed sensing (PICCS): initial phantom results , 2010, Physics in medicine and biology.

[13]  Anne Rougee,et al.  Geometrical calibration for 3D x-ray imaging , 1993, Medical Imaging.

[14]  Ge Wang,et al.  X-ray micro-CT with a displaced detector array. , 2002, Medical physics.

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

[16]  S. Leng,et al.  High temporal resolution and streak-free four-dimensional cone-beam computed tomography , 2008, Physics in medicine and biology.

[17]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[18]  Markus Kowarschik,et al.  Comparison of High-Speed Ray Casting on GPU using CUDA and OpenGL , 2008 .

[19]  N. Navab,et al.  Enhanced 3-D-reconstruction algorithm for C-arm systems suitable for interventional procedures , 2000, IEEE Transactions on Medical Imaging.

[20]  D L Parker,et al.  Optimal short scan convolution reconstruction for fanbeam CT. , 1982, Medical physics.

[21]  Guang-Hong Chen,et al.  Limited view angle tomographic image reconstruction via total variation minimization , 2007, SPIE Medical Imaging.

[22]  Klaus Mueller,et al.  Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware , 2000, IEEE Transactions on Medical Imaging.

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

[24]  Ramesh R. Galigekere,et al.  Cone-beam reprojection using projection-matrices , 2003, IEEE Transactions on Medical Imaging.

[25]  Klaus Mueller,et al.  Why do commodity graphics hardware boards (GPUs) work so well for acceleration of computed tomography? , 2007, Electronic Imaging.

[26]  Klaus Mueller,et al.  IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY , 2007 .

[27]  Cyril Riddell,et al.  Design and development of C-arm based cone-beam CT for image-guided interventions: initial results , 2006, SPIE Medical Imaging.