A survey of medical image registration on graphics hardware

Computer Methods and Programs in Biomedicine

Volumn 104, Issue 3, 2011, Pages

  Fluck, O ^a,b   Vetter, C ^a,c   Wein, W ^a   Kamen, A ^a   Preim, B ^b   Westermann, R ^c  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Acceleration; GPU; Graphics hardware; Image registration; Medical imaging

Indexed keywords

BUILDING BLOCKES; GPU; GRAPHICS HARDWARE; GRAPHICS PROCESSING UNITS; GRAPHICS PROCESSOR; HARDWARE ARCHITECTURE; MEDICAL IMAGE REGISTRATION; PROGRAMMING MODELS; PROGRAMMING SUPPORT;

COMPUTER GRAPHICS EQUIPMENT; COMPUTER PROGRAMMING; MEDICAL IMAGING; PROGRAM PROCESSORS; SURVEYS;

IMAGE REGISTRATION;

ARTICLE; COMPUTER GRAPHICS; COMPUTER PROGRAM; DIAGNOSTIC IMAGING; HISTOGRAM; INFORMATION PROCESSING; QUESTIONNAIRE;

COMPUTER GRAPHICS; DIAGNOSTIC IMAGING; USER-COMPUTER INTERFACE;

EID: 80655144789     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2010.10.009     Document Type: Article

References (80)

1. Pietrzyk U., Herholz K., Schuster A., Stockhausen H.-M.v., Lucht H., Heiss W.-D. Clinical applications of registration and fusion of multimodality brain images from PET, SPECT, CT, and MRI. Eur. J. Radiol. 1996, 21(3):174-182.
2. Image-Guided Interventions: Technology and Applications 2008, Springer.
3. Tsao J. Interpolation artifacts in multimodality image registration based on maximization of mutual information. IEEE Trans. Med. Imaging 2003, 22(7):854-864. doi:10.1109/TMI.2003.815077.
4. Pluim J.P.W., Maintz J.B.A., Viergever M.A. Interpolation artefacts in mutual information-based image registration. Comput. Vis. Image Underst. 2000, 77(9):211-232.
5. GPGPU, Website of general purpose computation on the GPU, http://www.gpgpu.org/.
6. Owens J.D., Luebke D., Govindaraju N., Harris M., Krüger J., Lefohn A.E., Purcell T.J. A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 2007, 26(1):80-113.
7. Shams R., Sadeghi P., Kennedy R.A., Hartley R.I. A survey of medical image registration on multicore and the GPU. IEEE Signal Process. Mag. 2010, 27(2):50-60.
8. Thall A. Extended-precision floating-point numbers for GPU computation. SIGGRAPH'06: ACM SIGGRAPH 2006 Research Posters 2006, ACM, New York, NY, USA, p. 52. doi:http://doi.acm.org/10.1145/1179622.1179682.
9. Vetter C., Guetter C., Xu C., Westermann R. Non-rigid multi-modal registration on the GPU. Proceedings of SPIE Medical Imaging, vol. 6512 2007, pp. 651228-1-651228-8.
10. Fan Z., Vetter C., Guetter C., Yu D., Westermann R., Kaufman A., Xu C. Optimized GPU implementation of learning-based non-rigid multi-modal registration. Proceedings of SPIE Medical Imaging 2008, pp. 69142Y-1-169142Y-10.
11. V. Podlozhnyuk, Histogram calculation in CUDA, Tech. rep., NVidia, 2007.
12. Shams R., Barnes N. Speeding up mutual information computation using NVIDIA CUDA hardware. DICTA'07: Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications 2007, 555-560. IEEE Computer Society, Washington, DC, USA.
13. Rost R.J. OpenGL Shading Language 2006, Addison-Wesley Professional. 2nd edition.
14. Neider J., Davis T. OpenGL Programming Guide: The Official Guide to Learning OpenGL, Release 1 1993, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.
15. Blythe D. The Direct3D 10 system. ACM Trans. Graph. 2006, 25(3):724-734. doi:http://doi.acm.org/10.1145/1141911.1141947.
16. Mark W.R., Glanville R.S., Akeley K., Kilgard M.J. Cg: a system for programming graphics hardware in a C-like language. SIGGRAPH'03: ACM SIGGRAPH 2003, 896-907. ACM Press, New York, NY, USA, doi:http://doi.acm.org/10.1145/1201775.882362.
17. Buck I., Foley T., Horn D., Sugerman J., Fatahalian K., Houston M., Hanrahan P. Brook for GPUs: stream computing on graphics hardware. ACM Trans. Graph. 2004, 23(3):777-786. doi:http://doi.acm.org/10.1145/1015706.1015800.
18. Owens J. Streaming architectures and technology trends. GPU Gems 2 2005, 457-470. Addison-Wesley Professional.
19. AMD, CAL SDK, http://ati.amd.com/developer/.
20. Sharp G.C., Kandasamy N., Singh H., Folkert M. GPU-based streaming architectures for fast cone-beam ct image reconstruction and demons deformable registration. Phys. Med. Biol. 2004, 52(19):5771-5783.
21. Khronos Group, The OpenCL specification, http://www.khronos.org/opencl/.
22. Strzodka R., Droske M., Rumpf M. Fast image registration in DX9 graphics hardware. J. Med. Inform. Technol. 2003, 6:43-49.
23. Köhn A., Drexl J., Ritter F., Koenig M., Peitgen H.-O. GPU accelerated image registration in two and three dimensions. Bildverarbeitung für die Medizin, Informatik Aktuell 2006, Springer, pp. 261-265.
24. Courty N., Hellier P. Accelerating 3D non-rigid registration using graphics hardware. Int. J. Image Graph. 2008, 8(1):1-18.
25. Muyan-Özçelik P., Owens J.D., Xia J., Samant S.S. Fast deformable registration on the GPU: a CUDA implementation of demons. The 2008 International Conference on Computational Science and its Applications, ICCSA 2008 2008, 223-233. IEEE Computer Society.
26. Samant S.S., Xia J., Muyan-Özçelik P., Owens J.D. High performance computing for deformable image registration: towards a new paradigm in adaptive radiotherapy. Med. Phys. 2008, 35(8):3546-3553.
27. Schiwietz T., Georgii J., Westermann R. Interactive model-based image registration. Proceedings of Vision, Modeling and Visualization 2007 2007, 213-221.
28. Noe K., Tanderup K., Lindegaard J., Grau C., Sørensen T. Gpu accelerated viscous-fluid deformable registration for radiotherapy. Stud. Health Technol. Inform. 2008, 132:327-332.
29. ur Rehman T., Pryor G., Melonakos J., Tannenbaum A. Multi-resolution 3d nonrigid registration via optimal mass transport on the gpu. Proc. Comput. Biomech. Med.-II 2007, 122-132.
30. Strzodka R., Droske M., Rumpf M. Image registration by a regularized gradient flow. A streaming implementation in DX9 graphics hardware. Computing 2004, 73(4):373-389.
31. Grabner M., Pock T., Gross T., Kainz B. Automatic differentiation for GPU-accelerated 2D/3D registration. Advances in Automatic Differentiation, vol. 64 of Lecture Notes in Computational Science and Engineering 2008, 259-269. Springer. C.H. Bischof, H.M. Bücker, P. Hovland, U. Naumann, J. Utke (Eds.).
32. Lin Y., Medioni G. Mutual information computation and maximization using gpu. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW'08 2008, 1-6. doi:10.1109/CVPRW.2008.4563101.
33. Sherhouse G.W., Novins K.L., Chaney E.L. Computation of digitally reconstructed radiographs for use in radiotherapy treatment design. Int. J. Radiat. Oncol. Biol. Phys. 1990, 18(3):651-658.
34. Wein W., Roeper B., Navab N. 2D/3D registration based on volume gradients. Proceedings of SPIE Medical Imaging 2005, 144-150.
35. D. LaRose, Iterative X-ray/CT registration using accelerated volume rendering, Ph.D. thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, May 2001.
36. T.J. Cullip, U. Neumann, Accelerating volume reconstruction with 3D texture hardware, Tech. rep., University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 1994.
37. Krüger J., Westermann R. Acceleration techniques for GPU-based volume rendering. Proceedings IEEE Visualization 2003 2003, 287-292.
38. Ino F., Gomita J., Kawasaki Y., Hagihara K. A GPGPU Approach for Accelerating 2-D/3-D Rigid Registration of Medical Images 2006, ISPA, pp. 939-950.
39. Ino F., Kawasaki Y., Tashiro T., Nakajima Y., Sato Y., Tamura S., Hagihara K. A parallel implementation of 2D-3D image registration for computer-assisted surgery. Int. J. Bioinform. Res. Appl. 2006, 2(4):341-358. doi:http://dx.doi.org/10.1504/IJBRA.2006.011034.
40. Demirci S., Kutter O., Manstad-Hulaas F., Bauernschmitt R., Navab N. Advanced 2D-3D registration for endovascular aortic interventions: addressing dissimilarity in images. Proceedings of SPIE Medical Imaging 2008, pp. 69182S-1-169182S-8.
41. Ruijters D., ter Haar-Romeny B.M., Suetens P. GPU-accelerated digitally reconstructed radiographs. BioMED'08: Proceedings of the Sixth IASTED International Conference on Biomedical Engineering 2008, 431-435. ACTA Press, Anaheim, CA, USA.
42. Birkfellner W., Seemann R., Figl M., Hummel J., Ede C., Homolka P., Yang X., Niederer P., Bergmann H. Wobbled splatting-a fast perspective volume rendering method for simulation of X-ray images from ct. Phys. Med. Biol. 2005, 50(9):N73-N84.
43. B.K. Horn, B.G. Schunck, Determining optical flow, Tech. rep., Massachusetts Institute of Technology, Cambridge, MA, USA, 1980.
44. Gu X., Pan H., Liang Y., Castillo R., Yang D., Choi D., Castillo E., Majumdar A., Guerrero T., Jiang S.B. Implementation and evaluation of various demons deformable image registration algorithms on a gpu. Phys. Med. Biol. 2010, 55(1):207. URL http://stacks.iop.org/0031-9155/55/i=1/a=012.
45. Khamene A., Schaller C., Hornegger J., Celi J.C., Ofstad B., Rietzel E., Li X.A., Tai A., Bayouth J. A novel image based verification method for respiratory motion management in radiation therapy. Eleventh IEEE International Conference on Computer Vision, DVD Proceedings 2007, 1-7.
46. Fluck O., Aharon S., Khamene A. Efficient framework for deformable 2d-3d registration. Proceedings of SPIE Medical Imaging, vol. 6918, SPIE 2008, pp. 69181E-1-69181E-10, doi:10.1117/12.772911.
47. Rezk-Salama C., Scheuering M., Soza G., Greiner G. Fast volumetric deformation on general purpose hardware. Proc. SIGGRAPH/Eurographics Workshop on Graphics Hardware 2001, 17-24.
48. Soza G., Hastreiter P., Bauer M., Rezk-Salama C., Nimsky C., Greiner G. Intraoperative registration on standard PC graphics hardware. Bildverarbeitung für die Medizin 2002, 334-337.
49. Soza G., Bauer M., Hastreiter P., Nimsky C., Greiner G. Non-rigid registration with use of hardware-based 3D Bézier functions. MICCAI'02: Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II 2002, 549-556. Springer-Verlag, London, UK.
50. Chen H., Hesser J., Männer R. Fast free-form volume deformation using inverse-ray-deformation. VIIP 2001, 163-168. ACTA Press.
51. Li B., Young A.A., Cowan B.R. Gpu accelerated non-rigid registration for the evaluation of cardiac function. MICCAI 2008: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention 2008, 880-887. Springer-Verlag, Berlin, Heidelberg.
52. Ruijters D., ter Haar-Romeny B.M., Suetens P. Efficient GPU-accelerated elastic image registration. BioMED'08: Proceedings of the Sixth IASTED International Conference on Biomedical Engineering 2008, 419-424. ACTA Press, Anaheim, CA, USA.
53. Modat M., Ridgway G.R., Taylor Z.A., Lehmann M., Barnes J., Hawkes D.J., Fox N.C., Ourselin S. Fast free-form deformation using graphics processing units. Comput. Methods Prog. Biomed. 2010, 98(3):278-284. doi:http://dx.doi.org/10.1016/j.cmpb.2009.09.002.
54. NVidia, CUDA SDK, http://www.nvidia.com/object/cuda_get.html.
55. Ruiz A., Ujaldon M., Cooper L., Huang K. Non-rigid registration for large sets of microscopic images on graphics processors. J. Signal Process. Syst. 2009, 55(1-3):229-250. doi:http://dx.doi.org/10.1007/s11265-008-0208-4.
56. Kubias A., Deinzer F., Feldmann T., Paulus S., Paulus D., Schreiber B., Brunner T. 2d/3d image registration on the gpu. Int. J. Pattern Recognit. Image Anal. 2008, 18(3):381-389. URL http://springerlink.com/content/l173468423228282/.
57. Khamene A., Bloch P., Wein W., Svatos M., Sauer F. Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy. Med. Image Anal. 2006, 96-112.
58. Collignon A., Maes F., Delaere D., Vandermeulen D., Suetens P., Marchal G. Automated multi-modality image registration based on information theory. Inf. Process. Med. Imaging 1995, 263-274.
59. Wells W., Viola P., Atsumi H., Nakajima S., Kikinis R. Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1996, 1(1):35-51.
60. Guetter C., Xu C., Sauer F., Hornegger J. Learning based non-rigid multi-modal image registration using kullback-leibler divergence. MICCAI, no. 2 2005, 255-262.
61. Maes F., Collignon A., Vandermeulen D., Marchal G., Suetens P. Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 1997, 16(2):187-198. doi:10.1109/42.563664.
62. Cheng W.-H., Lu C.-C. Acceleration of medical image registration using graphics process units in computing normalized mutual information. Fifth International Conference on Image and Graphics, 2009. ICIG'09 2009, 814-818. doi:10.1109/ICIG.2009.48.
63. NVidia, NVIDIA CUDA - compute unified device architecture - programming guide - version 2.3, Tech. rep., NVidia, 2007.
64. C. Sigg, M. Hadwiger, GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation, Addison-Wesley Professional, 2005, Ch. Fast Third-Order Texture Filtering, pp. 313-329.
65. Ruijters D., ter Haar Romeny B.M., Suetens P. Efficient gpu-based texture interpolation using uniform b-splines. J. Graph. gpu Game Tools 2008, 13(4):61-69.
66. Horn D. Stream reduction operations for GPGPU applications. GPU Gems 2 2005, 573-589. Addison-Wesley Professional.
67. Roger D., Assarsson U., Holzschuch N. Efficient stream reduction on the GPU. Workshop on General Purpose Processing on Graphics Processing Units 2007.
68. M. Harris, Optimizing parallel reduction in cuda, Tech. rep., NVIDIA Corporation, 2008.
69. ARB, EXT_histogram. URL http://opengl.org/registry/specs/EXT/histogram.txt.
70. Hastreiter P., Ertl T. Integrated registration and visualization of medical image data. Proceedings of Computer Graphics International (CGI) 1998, 78-85.
71. Teßmann M., Eisenacher C., Enders F., Stamminger M., Hastreiter P. Gpu accelerated normalized mutual information and b-spline transformation. Proceedings of the Eurographics Workshop on Visual Computing for Biomedicine (EG VCBM) 2008, 117-124. The Eurographics Association.
72. Fluck O., Aharon S., Cremers D., Rousson M. GPU histogram computation. SIGGRAPH'06: ACM SIGGRAPH 2006 Research Posters 2006.
73. Scheuermann T., Hensley J. Efficient histogram generation using scattering on GPUs. ACM Symposium on Interactive 3D Graphics and Games 2007, 33-37.
74. Shams R., Kennedy R.A. Efficient histogram algorithms for NVIDIA CUDA compatible devices. Proc. Int. Conf. on Signal Processing and Communications Systems (ICSPCS) 2007, 418-422.
75. Ohara M., Yeo H., Savino F., Iyengar G., Gong L., Inoue H., Komatsu H., Sheinin V., Daijavaa S., Erickson B. Real-time Mutual-information-based Linear Registration on the Cell Broadband Engine Processor 2007, pp. 33-36. doi:10.1109/ISBI.2007.356781.
76. Rohrer J., Gong L. Accelerating 3d nonrigid registration using the cell broadband engine processor. IBM J. Res. Dev. 2009, 53(5). 12:1-12:10. doi:10.1147/JRD.2009.5429078.
77. Göddeke D., Strzodka R., Turek S. Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations. Int. J. Parallel Emergent Distributed Syst. 2007, 22(4):221-256. doi:10.1080/17445760601122076.
78. Borkar S., Dubey P., Kahn K., Kuck D., Mulder H., Pawlowski S., Rattner J. Technology Intel Magazine: Platform 2015: Intel Processor and Platform Evolution for the Next Decade 2005, http://www.intel.com/technology/magazine/.
79. Hastreiter P., Rezk-Salama C., Soza G., Greiner G., Fahlbusch R., Ganslandt O., Nimsky C. Strategies for brain shift evaluation. Med. Image Anal. 2004, 8(4):447-464.
80. ur Rehman T., Haber E., Pryor G., Melonakos J., Tannenbaum A. 3d nonrigid registration via optimal mass transport on the gpu. Med. Image Anal. 2009, 13(6):931-940.