A generative model for resolution enhancement of diffusion MRI data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8151 LNCS, Issue PART 3, 2013, Pages 527-534

  Yap, Pew Thian ^a   An, Hongyu ^a   Chen, Yasheng ^a   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFUSION MAGNETIC RESONANCE IMAGING; HIGH RESOLUTION IMAGE; IMAGE GENERATIONS; MARKOV CHAIN MONTE CARLO METHOD; METROPOLIS-HASTINGS ALGORITHM; RESOLUTION ENHANCEMENT; SPATIAL RESOLUTION; WHITE MATTER CONNECTIVITIES;

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; DIFFUSION; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; MEDICAL COMPUTING; MONTE CARLO METHODS;

MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING;

ARTICLE; AUDIOVISUAL EQUIPMENT; BIOLOGICAL MODEL; BRAIN; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; CONNECTOME; CYTOLOGY; DIFFUSION TENSOR IMAGING; METHODOLOGY; MYELINATED NERVE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; ULTRASTRUCTURE;

BRAIN; COMPUTER SIMULATION; CONNECTOME; DIFFUSION TENSOR IMAGING; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, ANATOMIC; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NERVE FIBERS, MYELINATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STATISTICAL DISTRIBUTIONS;

EID: 84887009056     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40760-4_66     Document Type: Conference Paper

References (11)

1. Calamante, F., Tournier, J., Jackson, G., Connelly, A.: Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping. Neuroimage 53(4), 1233-1243 (2010)
2. Hastings, W.K.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97-109 (1970)
3. Johansen-Berg, H., Behrens, T.E. (eds.): Diffusion MRI - From Quantitative Measurement to In vivo Neuroanatomy. Elsevier (2009)
4. Kreher, B., Mader, I., Kiselev, V.: Gibbs tracking: A novel approach for the reconstruction of neuronal pathways. Magnetic Resonance in Medicine 60(4), 953-963 (2008)
5. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equations of state calculations by fast computing machines. Journal of Chemical Physics 21(6), 1087-1092 (1953)
6. Porter, D.A., Heidemann, R.M.: High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition. Magnetic Resonance in Medicine 62(2), 468-475 (2009)
7. Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., Kiselev, V.: Global fiber reconstruction becomes practical. NeuroImage 54(2), 955-962 (2011)
8. Reisert, M., Mader, I., Kiselev, V.: Global reconstruction of neuronal fibres. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), Diffusion Modelling Workshop (2009)
9. Scherrer, B., Gholipour, A., Warfield, S.: Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions. Medical Image Analysis 16(7), 1465-1476 (2012)
10. Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23(3), 1176-1185 (2004)
11. Yap, P.-T., Shen, D.: Resolution enhancement of diffusion-weighted images by local fiber profiling. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 18-25. Springer, Heidelberg (2012)