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/content/aapm/journal/medphys/38/5/10.1118/1.3578605
1.
1. J. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Krüger, A. E. Lefohn, and T. J. Purcell, “A survey of general-purpose computation on graphics hardware,” Comput. Graph. Forum 26(1), 2151 (2008).
2.
2. J. Owens, M. Houston, D. Luebke, S. Green, J. Stone, and J. Phillips, “GPU computing,” Proc. IEEE 96(5), 879899 (2008).
http://dx.doi.org/10.1109/JPROC.2008.917757
3.
3. M. Garland, S. Le Grand, J. Nickolls, J. Anderson, J. Hardwick, S. Morton, E. Phillips, Y. Zhang, and V. Volkov, “Parallel computing experiences with CUDA,” IEEE MICRO 28(4), 1327 (2008).
http://dx.doi.org/10.1109/MM.2008.57
4.
4. S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, and K. Skadron, “A performance study of general-purpose applications on graphics processors using CUDA,” J. Parallel Distrib. Comput. 68(10), 13701380 (2008).
http://dx.doi.org/10.1016/j.jpdc.2008.05.014
5.
5. B. Cabral, N. Cam, and J. Foran, “Accelerated volume rendering and tomographic reconstruction using texture mapping hardware,” in Proceedings of the Volume Visualization (ACM, New York, USA, 1994), pp. 9198.
6.
6. K. Asanovic, R. Bodik, B. Catanzaro, J. Gebis, P. Husbands, K. Keutzer, D. Patterson, W. Plishker, J. Shalf, S. Williams, and K. Yelick, “The landscape of parallel computing research: A view from Berkeley,” Technical Report, 2006 (UCB/EECS-2006-183).
7.
7. J. Nickolls, I. Buck, M. Garland, and K. Skadron, “Scalable parallel programming with CUDA,” in Comp. Graph (ACM, New York, USA, 2008), pp. 114.
8.
8. J. Li, C. Papachristou, and R. Shekhar, “An FPGA-based computing platform for real-time 3D medical imaging and its application to cone-beam CT reconstruction,” J. Imaging Sci. Technol. 49, 237245 (2005).
9.
9. S. Roujol, B. D. de Senneville, E. Vahala, T. Sorensen, C. Moonen, and M. Ries, “Online real-time reconstruction of adaptive TSENSE with commodity CPU/GPU hardware,” Magn. Reson. Med. 62(6), 16581664 (2009).
http://dx.doi.org/10.1002/mrm.22112
10.
10. L. Xing, L. Lee, and R. Timmerman, “Adaptive radiation therapy and clinical perspectives,” in Image Guided and Adaptive Radiation Therapy, edited by R. Timmerman and L. Xing (Lippincott Williams & Wilkins, Baltimore, MD, 2009), pp. 1640.
11.
11. M. Hansen, D. Atkinson, and T. Sorensen, “Cartesian SENSE and k-t SENSE reconstruction using commodity graphics hardware,” Magn. Reson. Med. 59(3), 463468 (2008).
http://dx.doi.org/10.1002/mrm.v59:3
12.
12. G. Dasika, A. Sethia, V. Robby, T. Mudge, and S. Mahlke, “Medics: Ultra-portable processing for medical image reconstruction,” in Proceedings of the PACT’10, (ACM, New York, NY, 2010), pp. 181192.
13.
13. R. A. Neri-Calderon, S. Alcaraz-Corona, and R. M. Rodriguez-Dagnino, “Cache-optimized implementation of the filtered backprojection algorithm on a digital signal processor,” J. Electron. Imaging 16(4), 043010 (2007).
http://dx.doi.org/10.1117/1.2815987
14.
14. K. Mueller and R. Yagel, “Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware,” IEEE Trans. Med. Imaging 19(12), 12271237 (2000).
http://dx.doi.org/10.1109/42.897815
15.
15. F. Xu and K. Mueller, “Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware,” IEEE Trans. Nucl. Sci. 52(3), 654663 (2005).
http://dx.doi.org/10.1109/TNS.2005.852703
16.
16. F. Xu and K. Mueller, “Real-time 3D computed tomographic reconstruction using commodity graphics hardware,” Phys. Med. Biol. 52(12), 34053419 (2007).
http://dx.doi.org/10.1088/0031-9155/52/12/006
17.
17. X. Zhao, J.-J. Hu, and P. Zhang, “GPU-based 3D cone-beam CT image reconstruction for large data volume,” Int. J. Biomed. Imaging 2009, 1 (2009).
18.
18. G. Yan, J. Tian, S. Zhu, C. Qin, Y. Dai, F. Yang, D. Dong, and P. Wu, “Fast Katsevich algorithm based on GPU for helical cone-beam computed tomography,” IEEE Trans. Inf. Technol. Biomed. 14(4), 10531061 (2010).
http://dx.doi.org/10.1109/TITB.2009.2036368
19.
19. G. C. Sharp, N. Kandasamy, H. Singh, and M. Folkert, “GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration,” Phys. Med. Biol. 52(19), 57715783 (2007).
http://dx.doi.org/10.1088/0031-9155/52/19/003
20.
20. P. B. Noel, A. M. Walczak, J. Xu, J. J. Corso, K. R. Hoffmann, and S. Schafer, “GPU-based cone beam computed tomography,” Comput. Methods Programs Biomed. 98(3), 271277 (2010).
http://dx.doi.org/10.1016/j.cmpb.2009.08.006
21.
21. Y. Okitsu, F. Ino, and K. Hagihara, “High-performance cone beam reconstruction using CUDA compatible GPUs,” Parallel Comput. 36(2–3), 129141 (2010).
http://dx.doi.org/10.1016/j.parco.2010.01.004
22.
22. H. Yan, D. J. Godfrey, and F.-F. Yin, “Fast reconstruction of digital tomosynthesis using on-board images,” Med. Phys. 35(5), 21622169 (2008).
http://dx.doi.org/10.1118/1.2896077
23.
23. H. Yan, L. Ren, D. J. Godfrey, and F.-F. Yin, “Accelerating reconstruction of reference digital tomosynthesis using graphics hardware,” Med. Phys. 34(10), 37683776 (2007).
http://dx.doi.org/10.1118/1.2779945
24.
24. K. Chidlow and T. Moller, “Rapid emission tomography reconstruction,” in Proceedings of the Eurographics (ACM, New York, USA, 2003), pp. 1526.
25.
25. F. Xu, W. Xu, M. Jones, B. Keszthelyi, J. Sedat, D. Agard, and K. Mueller, “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs,” Comput. Methods Programs Biomed. 98(3), 261270 (2010).
http://dx.doi.org/10.1016/j.cmpb.2009.09.003
26.
26. J. S. Kole and F. J. Beekman, “Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware,” Phys. Med. Biol. 51(4),875889 (2006).
http://dx.doi.org/10.1088/0031-9155/51/4/008
27.
27. X. Jia, Y. Lou, R. Li, W. Y. Song, and S. B. Jiang, “GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation,” Med. Phys. 37(4), 17571760 (2010).
http://dx.doi.org/10.1118/1.3371691
28.
28. G. Pratx, G. Chinn, P. Olcott, and C. Levin, “Accurate and shift-varying line projections for iterative reconstruction using the GPU,” IEEE Trans. Med. Imaging 28(3), 415422 (2009).
http://dx.doi.org/10.1109/TMI.2008.2006518
29.
29. G. Pratx, S. Surti, and C. Levin, “Fast list-mode reconstruction for time-of-flight PET using graphics hardware,” IEEE Trans. Nucl. Sci. 58(1), 105109 (2011). (Place: San Francisco, CA)
http://dx.doi.org/10.1109/TNS.2010.2081376
30.
30. G. Pratx, J.-Y. Cui, S. Prevrhal, and C. S. Levin, “3-D tomographic image reconstruction from randomly ordered lines with CUDA,” in GPU Computing Gems Emerald Edition, edited by W. mei Hwu (Morgan Kaufmann, San Francisco, CA 2011), pp. 679691.
31.
31. T. Schiwietz, T. C. Chang, P. Speier, and R. Westermann, “MR image reconstruction using the GPU,” Proc. SPIE 6142, 61423T (2006).
http://dx.doi.org/10.1117/12.652223
32.
32. T. Sorensen, T. Schaeffter, K. Noe, and M. Hansen, “Accelerating the nonequispaced fast Fourier transform on commodity graphics hardware,” IEEE Trans. Med. Imaging 27(4), 538547 (2008).
http://dx.doi.org/10.1109/TMI.2007.909834
33.
33. S. Stone, J. Haldar, S. Tsao, W. W. Hwu, B. Sutton, and Z.-P. Liang, “Accelerating advanced MRI reconstructions on GPUs,” J. Parallel Distrib. Comput. 68(10), 13071318 (2008).
http://dx.doi.org/10.1016/j.jpdc.2008.05.013
34.
34. T. Sorensen, D. Atkinson, T. Schaeffter, and M. Hansen, “Real-time reconstruction of sensitivity encoded radial magnetic resonance imaging using a graphics processing unit,” IEEE Trans. Med. Imaging 28(12), 19741985 (2009).
http://dx.doi.org/10.1109/TMI.2009.2027118
35.
35. D. Johnson, S. Narayan, C. A. Flask, and D. L. Wilson, “Improved fat–water reconstruction algorithm with graphics hardware acceleration,” J. Magn. Reson. Imaging 31(2), 457465 (2010).
http://dx.doi.org/10.1002/jmri.22051
36.
36. Y. Watanabe and T. Itagaki, “Real-time display on Fourier domain optical coherence tomography system using a graphics processing unit,” J. Biomed. Opt. 14(6), 060506 (2009).
http://dx.doi.org/10.1117/1.3275463.1
37.
37. C. Vinegoni, L. Fexon, P. F. Feruglio, M. Pivovarov, J.-L. Figueiredo, M. Nahrendorf, A. Pozzo, A. Sbarbati, and R. Weissleder, “High throughput transmission optical projection tomography using low cost graphics processing unit,” Opt. Express 17(25), 2232022332 (2009).
http://dx.doi.org/10.1364/OE.17.022320
38.
38. L. W. Chang, K. H. Hsu, and P. C. Li, “Graphics processing unit-based high-frame-rate color doppler ultrasound processing,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 56(9), 18561860 (2009).
http://dx.doi.org/10.1109/TUFFC.2009.1261
39.
39. D. Liu and E. Ebbini, “Real-time two-dimensional temperature imaging using ultrasound,” IEEE Trans. Biomed. Eng. 57(1), 1216 (2010).
40.
40. P. Coupé, P. Hellier, N. Azzabou, and C. Barillot, “3D freehand ultrasound reconstruction based on probe trajectory,” Lecture Notes in Computer Science (Springer, Berlin, Germany, 2005), Vol. 3749, pp. 597604.
41.
41. I. Goddard, T. Wu, S. Thieret, A. Berman, and H. Bartsch, “Implementing an iterative reconstruction algorithm for digital breast tomosynthesis on graphics processing hardware,” Proc SPIE 6142(1), 61424V (2006).
http://dx.doi.org/10.1117/12.652605
42.
42. M. de Greef, J. Crezee, J. C. van Eijk, R. Pool, and A. Bel, “Accelerated ray tracing for radiotherapy dose calculations on a GPU,” Med. Phys. 36(9), 40954102 (2009).
http://dx.doi.org/10.1118/1.3190156
43.
43. A. Badal and A. Badano, “Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit,” Med. Phys. 36(11), 48784880 (2009).
http://dx.doi.org/10.1118/1.3231824
44.
44. X. Jia, X. Gu, J. Sempau, D. Choi, A. Majumdar, and S. B. Jiang, “Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport,” Phys. Med. Biol. 55(11), 30773086 (2010).
http://dx.doi.org/10.1088/0031-9155/55/11/006
45.
45. S. Hissoiny, B. Ozell, H. Bouchard, and P. Despres, “GPUMCD: A new GPU-oriented Monte Carlo dose calculation platform,” Med. Phys. 38(2), 754764 (2011).
http://dx.doi.org/10.1118/1.3539725
46.
46. E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
http://dx.doi.org/10.1117/1.3041496
47.
47. Q. Fang and D. A. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units,” Opt. Express 17(22), 2017820190 (2009).
http://dx.doi.org/10.1364/OE.17.020178
48.
48. W. Lo, T. Han, J. Rose, and L. Lilge, “GPU-accelerated Monte Carlo simulation for photodynamic therapy treatment planning,” Proc. SPIE 7373, 737313 (2009).
http://dx.doi.org/10.1117/12.831944
49.
49. H. Shen and G. Wang, “A tetrahedron-based inhomogeneous Monte Carlo optical simulator,” Phys. Med. Biol. 55(4), 947962 (2010).
http://dx.doi.org/10.1088/0031-9155/55/4/003
50.
50. S. G. Parker, J. Bigler, A. Dietrich, H. Friedrich, J. Hoberock, D. Luebke, D. McAllister, M. McGuire, K. Morley, A. Robison, and M. Stich, “Optix: A general purpose ray tracing engine,” ACM Trans. Graphics 29(4), 113 (2010).
http://dx.doi.org/10.1145/1778765.1778803
51.
51. X. Gu, D. Choi, C. Men, H. Pan, A. Majumdar, and S. B. Jiang, “GPU-based ultra-fast dose calculation using a finite size pencil beam model,” Phys. Med. Biol. 54(20), 62876297 (2009).
http://dx.doi.org/10.1088/0031-9155/54/20/017
52.
52. R. Jacques, R. Taylor, J. Wong, and T. McNutt, “Towards real-time radiation therapy: GPU accelerated superposition/convolution,” Comput. Methods Programs Biomed. 98(3), 285292 (2010).
http://dx.doi.org/10.1016/j.cmpb.2009.07.004
53.
53. S. Hissoiny, B. Ozell, and P. Despres, “A convolution-superposition dose calculation engine for GPUs,” Med. Phys. 37(3), 10291037 (2010).
http://dx.doi.org/10.1118/1.3301618
54.
54. W. Lu, “A non-voxel-based broad-beam (NVBB) framework for IMRT treatment planning,” Phys. Med. Biol. 55(23), 71757210 (2010).
http://dx.doi.org/10.1088/0031-9155/55/23/002
55.
55. S. Hissoiny, B. Ozell, and P. Despres, “Fast convolution-superposition dose calculation on graphics hardware,” Med. Phys. 36(6), 19982005 (2009).
http://dx.doi.org/10.1118/1.3120286
56.
56. R. Jacques, J. Wong, R. Taylor, and T. McNutt, “Real-time dose computation: GPU-accelerated source modeling and superposition/convolution,” Med. Phys. 38(1), 294305 (2011).
http://dx.doi.org/10.1118/1.3483785
57.
57. B. Zhou, C. X. Yu, D. Z. Chen, and X. S. Hu, “GPU-accelerated Monte Carlo convolution/superposition implementation for dose calculation,” Med. Phys. 37(11), 55935603 (2010).
http://dx.doi.org/10.1118/1.3490083
58.
58. Q. Chen, M. Chen, and W. Lu, “Ultrafast convolution/superposition using tabulated and exponential cumulative-cumulative-kernels on GPU,” in Proceedings of the 16th International Conference on the Use of Computers in Radio Therapy, edited by J.-J. Sonke (2010).
59.
59. C. Men, X. Gu, D. Choi, A. Majumdar, Z. Zheng, K. Mueller, and S. B. Jiang, “GPU-based ultrafast IMRT plan optimization,” Phys. Med. Biol. 54(21), 65656573 (2009).
http://dx.doi.org/10.1088/0031-9155/54/21/008
60.
60. C. Cotrutz and L. Xing, “Segment-based dose optimization using a genetic algorithm,” Phys. Med. Biol. 48(18), 29872998 (2003).
http://dx.doi.org/10.1088/0031-9155/48/18/303
61.
61. C. Men, X. Jia, and S. Jiang, “GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy,” Phys. Med. Biol. 55, 43094319 (2010).
http://dx.doi.org/10.1088/0031-9155/55/15/008
62.
62. C. Men, H. E. Romeijn, X. Jia, and S. B. Jiang, “Ultrafast treatment plan optimization for volumetric modulated arc therapy (VMAT),” Med. Phys. 37(11), 57875791 (2010).
http://dx.doi.org/10.1118/1.3491675
63.
63. R. Shams, P. Sadeghi, R. Kennedy, and R. Hartley, “A survey of medical image registration on multicore and the GPU,” IEEE Signal Process. Mag. 27(2), 5060 (2010).
http://dx.doi.org/10.1109/MSP.2009.935387
64.
64. R. Strzodka, M. Droske, and M. Rumpf, “Image registration by a regularized gradient flow. A streaming implementation in DX9 graphics hardware,” Computing 73, 373389 (2004).
http://dx.doi.org/10.1007/s00607-004-0087-x
65.
65. T. ur Rehman, E. Haber, G. Pryor, J. Melonakos, and A. Tannenbaum, “3D nonrigid registration via optimal mass transport on the GPU,” Med. Image Anal. 13(6), 931940 (2009).
http://dx.doi.org/10.1016/j.media.2008.10.008
66.
66. G. Soza, M. Bauer, P. Hastreiter, C. Nimsky, and G. Greiner, “Non-rigid registration with use of hardware-based 3D Bezier functions,” Lecture Notes in Computer Science (Springer, Berlin, Germany, 2002), Vol. 2489, pp. 549556.
67.
67. D. Levin, D. Dey, and P. Slomka, “Acceleration of 3D, nonlinear warping using standard video graphics hardware: Implementation and initial validation,” Comput. Med. Imaging Graph. 28(8), 471483 (2004).
http://dx.doi.org/10.1016/j.compmedimag.2004.07.005
68.
68. J. A. Shackleford, N. Kandasamy, and G. C. Sharp, “On developing B-spline registration algorithms for multi-core processors,” Phys. Med. Biol. 55(21), 63296351 (2010).
http://dx.doi.org/10.1088/0031-9155/55/21/001
69.
69. M. Modat, G. R. Ridgway, Z. A. Taylor, M. Lehmann, J. Barnes, D. J. Hawkes, N. C. Fox, and S. Ourselin, “Fast free-form deformation using graphics processing units,” Comput. Methods Programs Biomed. 98(3), 278284 (2010).
http://dx.doi.org/10.1016/j.cmpb.2009.09.002
70.
70. S. S. Samant, J. Xia, P. Muyan-Ozcelik, and J. D. Owens, “High performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy,” Med. Phys. 35(8), 35463553 (2008).
http://dx.doi.org/10.1118/1.2948318
71.
71. X. Gu, H. Pan, Y. Liang, R. Castillo, D. Yang, D. Choi, E. Castillo, A. Majumdar, T. Guerrero, and S. B. Jiang, “Implementation and evaluation of various demons deformable image registration algorithms on a GPU,” Phys. Med. Biol. 55(1), 207219 (2010).
http://dx.doi.org/10.1088/0031-9155/55/1/012
72.
72. P. Muyan-Ozcelik, J. D. Owens, J. Xia, and S. S. Samant, “Fast deformable registration on the GPU: A CUDA implementation of Demons,” in Proceedings of the International Conference on Computational Sciences and Its Applications (IEEE Computer Society Washington, DC, USA, 2008), pp. 223233.
73.
73. G. R. Joldes, A. Wittek, and K. Miller, “Real-time nonlinear finite element computations on GPU—Application to neurosurgical simulation,” Comput. Methods Appl. Mech. Eng. 199(49–52), 33053314 (2010).
http://dx.doi.org/10.1016/j.cma.2010.06.037
74.
74. Y. Liu, A. Fedorov, R. Kikinis, and N. Chrisochoides, “Real-time non-rigid registration of medical images on a cooperative parallel architecture,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (IEEE Computer Society Washington, DC, USA, 2009), pp. 401404.
75.
75. R. Shams, P. Sadeghi, R. Kennedy, and R. Hartley, “Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images,” Comput. Methods Programs Biomed. 99(2), 133146 (2010).
http://dx.doi.org/10.1016/j.cmpb.2009.11.004
76.
76. M. Rumpf and R. Strzodka, “Level set segmentation in graphics hardware,” in Proceedings of IEEE International Conference on Image Processing (IEEE Computer Society Washington, DC, USA, 2001), Vol. 3, pp. 11031106.
77.
77. A. E. Lefohn, J. M. Kniss, C. D. Hansen, and R. T. Whitaker, “Interactive deformation and visualization of level set surfaces using graphics hardware,” in Proceedings of the IEEE Visualization (IEEE Computer Society Washington, DC, USA, 2003), p. 11.
78.
78. A. Sherbondy, M. Houston, and S. Napel, “Fast volume segmentation with simultaneous visualization using programmable graphics hardware,” in Proceedings of the IEEE Visualization (IEEE Computer Society Washington, DC, USA, 2003), pp. 171176.
79.
79. J. E. Cates, A. E. Lefohn, and R. T. Whitaker, “GIST: An interactive, GPU-based level set segmentation tool for 3D medical images,” Med. Image Anal. 8(3), 217231 (2004).
http://dx.doi.org/10.1016/j.media.2004.06.022
80.
80. W.-K. Jeong, J. Beyer, M. Hadwiger, A. Vazquez, H. Pfister, and R. Whitaker, “Scalable and interactive segmentation and visualization of neural processes in EM datasets,” IEEE Trans. Vis. Comput. Graph. 15(6), 15051514 (2009).
http://dx.doi.org/10.1109/TVCG.2009.178
81.
81. A. Narayanaswamy, S. Dwarakapuram, C. Bjornsson, B. Cutler, W. Shain, and B. Roysam, “Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation,” IEEE Trans. Med. Imaging 29(3), 583597 (2010).
http://dx.doi.org/10.1109/TMI.2009.2022086
82.
82. J. Spoerk, H. Bergmann, F. Wanschitz, S. Dong, and W. Birkfellner, “Fast DRR splat rendering using common consumer graphics hardware,” Med. Phys. 34(11), 43024308 (2007).
http://dx.doi.org/10.1118/1.2789500
83.
83. Q. Zhang, R. Eagleson, and T. M. Peters, “Dynamic real-time 4D cardiac MDCT image display using GPU-accelerated volume rendering,” Comput. Med. Imaging Graph. 33(6), 461476 (2009).
http://dx.doi.org/10.1016/j.compmedimag.2009.04.002
84.
84. D. Levin, U. Aladl, G. Germano, and P. Slomka, “Techniques for efficient, real-time, 3D visualization of multi-modality cardiac data using consumer graphics hardware,” Comput. Med. Imaging Graph. 29(6), 463475 (2005).
http://dx.doi.org/10.1016/j.compmedimag.2005.02.007
85.
85. T.-H. Lee, J. Lee, H. Lee, H. Kye, Y. G. Shin, and S. H. Kim, “Fast perspective volume ray casting method using GPU-based acceleration techniques for translucency rendering in 3D endoluminal CT colonography,” Comput. Biol. Med. 39(8), 657666 (2009).
http://dx.doi.org/10.1016/j.compbiomed.2009.04.007
86.
86. A. Kruger, C. Kubisch, G. Strauss, and B. Preim, “Sinus endoscopy—Application of advanced GPU volume rendering for virtual endoscopy,” IEEE Trans. Vis. Comput. Graph. 14(6), 14911498 (2008).
http://dx.doi.org/10.1109/TVCG.2008.161
87.
87. C. Kubisch, C. Tietjen, and B. Preim, “GPU-based smart visibility techniques for tumor surgery planning,” Int. J. Comput. Assist. Radiol. Surg. 5(6), 667678 (2010).
http://dx.doi.org/10.1007/s11548-010-0420-0
88.
88. Z. Taylor, M. Cheng, and S. Ourselin, “High-speed nonlinear finite element analysis for surgical simulation using graphics processing units,” IEEE Trans. Med. Imaging 27(5), 650663 (2008).
http://dx.doi.org/10.1109/TMI.2007.913112
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/content/aapm/journal/medphys/38/5/10.1118/1.3578605
2011-05-09
2016-07-24

Abstract

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction,dose calculation and treatment plan optimization, and image processing.

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