Stochastic DT-MRI connectivity mapping on the GPU

IEEE Transactions on Visualization and Computer Graphics

Volumn 13, Issue 6, 2007, Pages 1504-1511

  McGraw, Tim ^a,b   Nadar, Mariappan ^a,c  

^a IEEE

^b WEST VIRGINIA UNIVERSITY   (United States)

^c SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

Diffusion tensor; Magnetic resonance imaging; Stochastic tractography

Indexed keywords

BAYESIAN FORMULATION; DIFFUSION TENSOR; GRAPHICS PROCESSING UNIT (GPU); STOCHASTIC TRACTOGRAPHY;

ALGORITHMS; BAYESIAN NETWORKS; ITERATIVE METHODS; NEURONS; STOCHASTIC PROGRAMMING;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER SIMULATION; DIFFUSION WEIGHTED IMAGING; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; MATHEMATICAL COMPUTING; METHODOLOGY; MYELINATED NERVE; NERVE TRACT; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS; ULTRASTRUCTURE;

ALGORITHMS; BRAIN; COMPUTER GRAPHICS; COMPUTER SIMULATION; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, BIOLOGICAL; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NERVE FIBERS, MYELINATED; NEURAL PATHWAYS; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STOCHASTIC PROCESSES; USER-COMPUTER INTERFACE;

EID: 35948998080     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2007.70597     Document Type: Article

References (60)

1. P. J. Basser. Inferring microstructural features and the physiological state of tissues from diffusion weighted images. NMR in Biomed., 8:333-344, 1995.
2. P. J. Basser. New histological and physiological stains derived from diffusion-tensor MR images. Ann. N. Y. Acad. Sci., 820:123-138, 1997.
3. P. J. Basser and C. Pierpaoli. Microstructural and physiological features of tissue elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B, 110:209-219, 1996.
4. T. Behrens, H. Johansen-Berg, M. Woolrich, S. Smith, C. Wheeler-Kingshott, P. Boulby, G. Barker, E. Sillery, K. Sheehan, O. Ciccarelli, A. Thompson, J. Brady, and P. Matthews. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6(7):750-757, July 2003.
5. T. Behrens, M. Woolrich, M. Jenkinson, H. Johansen-Berg, R. Nunes, S. Clare, P. Matthews, J. Brady, and S. Smith. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine, 50:1077-1088, 2003.
6. M. Björnemo, A. Brun, R. Kikinis, and C. F. Westin. Regularized stochastic white matter tractography using diffusion tensor MRI. In Fifth Intl. Conf. on MICCAI, volume 1, pages 435-442, 2002.
7. B. Cabral and L. Leedom. Imaging vector fields using line integral convolution. In Proceedings of Siggraph, pages 263-270, New York, 1993. ACM Press, New York.
8. J. Cates, A. Lefohn, and R. Whitaker. GIST: an interactive, GPU-based level set segmentation tool for 3d medical images. Medical Image Analysis, 8(3):217-231, 2004.
9. L. Devroye. Non-uniform random variate generation. Springer-Verlag New York, 1986.
10. R. A. Drebin, L. Carpenter, and P. Hanrahan. Volume Rendering. In Computer Graphics, Proceedings of SIGGRAPH 88, volume 22, pages 65-74, 1988.
11. F. Enders, N. Sauber, D. Merhof, P. Hastreiter, C. Nimsky, and M. Stamminger. Visualization of White Matter Tracts with Wrapped Streamlines. IEEE Visualization, pages 51-58, 2005.
12. O. Friman, G. Farneback, and C.-F. Westin. A Bayesian approach for stochastic white matter tractography. Transactions on Medical Imaging, 25(8):965-978, 2006.
13. O. Friman and C.-F. Westin. Uncertainty in fiber tractography. In Eighth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'05), Lecture Notes in Computer Science 3749, pages 107-114, Palm Springs, CA, USA, October 2005.
14. E. Hsu. Generalized Line Integral Convolution Rendering of Diffusion Tensor Fields. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), page 790, 2001.
15. D. K. Jones, A. Simmons, S. C. R. Williams, and M. A. Horsfield. Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn. Reson. Med., 42:37-41, 1999.
16. G. Kindlmann. Superquadric tensor glyphs. In Proceedings of IEEE TVCG/EG Symposium on Visualization 2004, pages 147-154, May 2004.
17. G. Kindlmann and C. Westin. Diffusion tensor visualization with glyph packing. IEEE Transactions on Visualization and Computer Graphics, 1.2(5): 1329-1335, 2005.
18. G. L. Kindlmann and D. M. Weinstein. Hue-balls and lit-tensors for direct volume rendering of diffusion tensor fields. In IEEE Visualization '99, pages 183-190, 1999.
19. P. Kipfer, M. Segal, and R. Westermann. UberFlow: a GPU-based particle engine. Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware, pages 115-122, 2004.
20. M. Koch, D. Norris, and M. Hund-Georgiadis. An investigation of functional and anatomical connectivity using magnetic resonance imaging. Neuroimage, 16(1):241.-250, 2002.
21. P. Kondratieva, J. Krüger, and R. Westermann. The application of GPU particle tracing to diffusion tensor field visualization. In Proceedings IEEE Visualization 2005, 2005.
22. J. Kruger and R. Westermann. Acceleration Techniques for GPU-based Volume Rendering. Proceedings of the 14th IEEE Visualization 2003, pages 287-292, 2003.
23. D. H. Laidlaw, E. T. Ahrens, D. Kremers, M. J. Avalos, C. Readhead, and R. E. Jacobs. Visualizing diffusion tensor images of the mouse spinal cord. In Proceedings of IEEE Visualization 1998, pages 127-134. IEEE Computer Society Press, New York, October 1998.
24. M. Lazar and A. Alexander. Bootstrap white matter tractography (BOOTTRAC). Neuroimage, 24(2):524-532, 2005.
25. M. Lazar, D. M.Weinstein, J. S.Tsuruda, K. M.Hasan, K. Arfanakis, M. E. Meyerand, B. Badie, H. A.Rowley, V. Haughton, A. Field, and A. L.Alexander. White matter tractography using diffusion tensor deflection. Human Brain Mapping, 18:306-321, 2003.
26. A. Lefohn, J. Cates, and R. Whitaker. Interactive, GPU-Based Level Sets for 3D Segmentation. Medical Image Computing and Computer Assisted Intervention (MICCAI), pages 564-572, 2003.
27. A. Lefohn, J. Kniss, C. Hansen, and R. Whitaker. A. streaming narrowband algorithm: interactive computation and visualization of level sets. Visualization and Computer Graphics, IEEE Transactions on, 10(4):422-433, 2004.
28. M. Levoy. Display of surfaces from volume data. IEEE Comput. Graph. Appl., 8(3):29-37, 1988.
29. J.-F. Mangin, C. Poupon, Y. Cointepas, D. Rivière, D. PapadopoulosOrfanos, C. A. Clark, J. Régis, and D. L. Bihan. A framework based on spin glass models for the inference of anatomical connectivity from diffusion-weighted MR data. NMR in Biomedicine, 15:481-492, 2002.
30. K. V. Mardia and P. Jupp. Directional Statistics. John Wiley and Sons Ltd., New York, 2nd edition, 2000.
31. T. McGraw, B. Vemuri, Z. Wang, Y. Chen, M. Rao, and T. Mareci. Line integral convolution for visualization of fiber tract maps from DTI. In Fifth Intl. Conf. on MICCAI, pages 615-622, Tokyo, Japan, 2002.
32. D. Merhof, M. Sonntag, F. Enders, C. Nimsky, P. Hastreiter, and G. Ordner. Hybrid visualization for white matter tracts using triangle strips and point sprites. IEEE Transactions on Visualization and Computer Graphics, 12(5): 1181.-1188, 2006.
33. B. Moberts, A. Vilanova, and J. van Wijk. Evaluation of Fiber Clustering Methods for Diffusion Tensor Imaging. IEEE Visualization, pages 65-72, 2005.
34. S. Mori, B. J. Crain, V. P. Chacko, and P. C. M. van Zijl. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol., 45:265-269, 1999.
35. S. Mori and P. C. van Zijl. Fiber tracking: principles and strategies - a technical review. NMRBlomed, 15(7-8):468-480, 2002.
36. J. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Kruger, A. Lefohn, and T. Purcell. A survey of general-purpose computation on graphics hardware. Computer Graphics Forum, 26(1 or 2):to appear, 2007.
37. G. Parker and D. Alexander. Probabilistic monte carlo based mapping of cerebral connections utilising whole-brain crossing fibre information. In Proc. of Information Processing in Medical Imaging, pages 684-695, 2003.
38. G. Parker and D. Alexander. Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral, tissue. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1457):893-902, 2005.
39. C. Pierpaoli and P. J. Basser. Toward a quantitative assessment of diffusion anisotropy. Magn. Resort. Med., 36:893-906,1.996.
40. C. Pierpaoli, P. Jezzard, P. J. Basser, A. Barnett, and G. D. Chiro. Diffusion tensor mr imaging of the human brain. Radiology, 201:637-648, 1996.
41. C. Poupon, C. A. Clark, V. Frouin, J. Régis, I. Bloch, D. L. Binan, and J.-F. Mangin. Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles. Neuroimage, 12(2):184-195, 2000.
42. C. Poupon, J. Mangin, C. Clark, V. Frouin, J. Regis, D. L. Bihan, and I. Bloch. Towards inference of human brain connectivity from. MR diffusion tensor data. Med. Image Anal., 5(1): 1-15, 2001.
43. G. Reina, K. Bidmon, F. Enders, P. Hastreiter, and T. Ertl. GPU-Based Hyperstreamlines for Diffusion Tensor Imaging. In Proceedings of EUROGRAPHICS - IEEE VGTC Symposium on Visualization 2006, pages 35-42, 2006.
44. R. Rost. OpenGL Shading Language. Addison-Wesley, 2006.
45. M. Rumpf and R. Strzodka. Level set segmentation in graphics hardware. Image Processing, 2001. Proceedings. 2001 International Conference on, 3, 2001.
46. M. Rumpf and R. Strzodka. Nonlinear diffusion in graphics hardware. Proceedings of Eurographics/IEEE TCVG Symposium on Visualization, pages 75-84, 2001.
47. T. Schiwietz, T. Chang, P. Speier, and R. Westermann. MR image reconstruction using the GPU. Proceedings of the SPIE, Advanced Optical and Quantum Memories and Computing III., 6142:1279-1290, 2006.
48. A. Sorensen, O. Wu, W. Copen, T. Davis, R. Gonzalez, W. Koroshetz, T. Reese, B. Rosen, V. Wedeen, and R. Weisskoff. Human acute cerebral, ischemia: Detection of changes in water diffusion anisotropy by using MR imaging. Radiology, 212:785-792, 1999.
49. R. Strzodka, M. Droske, and M. Rumpf. Fast image registration in DX9 graphics hardware. Journal of Medical Informatics and Technologies, 6(43-49): 143, 2003.
50. R. Strzodka, M. Droske, and M. Rumpf. Image Registration by a Regularized Gradient Flow. A Streaming Implementation in DX9 Graphics Hardware. Computing, 73(4):373-389, 2004.
51. T. Sumanaweera and D. Liu. Medical image reconstruction with the FFT. GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation, 2005.
52. The Scientific Computing and Imaging (SCI) Institute, University of Utah. Diffusion tensor MRI datasets (http://www.sci.utah.edu/~gk/DTI-data/). 2000.
53. T.Preußer and M. Rumpf. Anisotropic nonlinear diffusion in flow visualization. In IEEE Visualization, pages 325-332, 1999.
54. D. Werring, C. Clark, G. Barker, D. Miller, G. Parker, M. Brammer, E. Bullmore, V. Giampietro, and A. Thompson. The structural and functional mechanisms of motor recovery: complementary use of diffusion tensor and functional magnetic resonance imaging in a traumatic injury of the internal capsule. Journal of Neurology, Neurosurgery & Psychiatry, 65(6):863-869, 1998.
55. D. Werring, C. Clark, G. Barker, A. Thompson, and D. Miller. Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology, 52:1626, 1999.
56. D. Werring, A. Toosy, C. Clark, G. Parker, G. Barker, D. Miller, and A. Thompson. Diffusion tensor imaging can detect and quantify corti-cospinal tract degeneration after stroke. J Neurol Neurosurg Psychiatry, 69:269-272, 2000.
57. C.-F. Westin, S. E. Maier, H. Mamata, A. Nabavi, F. A. Jolesz, and R. Kikinis. Processing and visualization of diffusion tensor MRI. Medical Image Analysis, 6(2):93-108, 2002.
58. F. Xu and K. Mueller. Accelerating popular tomographic reconstruction algorithms on commodity pc graphics hardware. IEEE Transactions on Nuclear Science, 52(3):654-663, 2005.
59. S. Zhang, Ç. Demiralp, and D. H. Laidlaw. Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. IEEE Transactions on Visualization and Computer Graphics, 9(4):454-462, 2003.
60. X. Zheng and A. Pang. Hyperlic. In VIS '03: Proceedings of the 14th IEEE Visualization 2003 (VIS'03), page 33, Washington, DC, USA, 2003. IEEE Computer Society.