Two-tensor tractography using a constrained filter

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

Volumn 5761 LNCS, Issue PART 1, 2009, Pages 894-902

  Malcolm, James G ^a   Shenton, Martha E ^a,b   Rathi, Yogesh ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b VETERANS AFFAIRS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANGULAR RESOLUTION; CURRENT ESTIMATES; EIGENVALUES; FIBER MODEL; FIBER TRACKING; IN-VIVO EXPERIMENTS; LOCAL FIBER ORIENTATION; LOCAL MODEL; LOCAL STRUCTURE; MIXTURE OF GAUSSIANS; MODEL CONSTRAINTS; RECURSIVE ESTIMATION; SEED POINT; SYNTHETIC EXPERIMENTS; TRACTOGRAPHY; UNSCENTED KALMAN FILTER;

EIGENVALUES AND EIGENFUNCTIONS; KALMAN FILTERS; MEDICAL COMPUTING; TENSORS; UNCERTAINTY ANALYSIS;

FIBERS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; CYTOLOGY; DIFFUSION TENSOR IMAGING; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; MYELINATED NERVE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; DIFFUSION TENSOR IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SUBTRACTION TECHNIQUE;

EID: 79551681001     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04268-3_110     Document Type: Conference Paper

References (23)

1. Alexander, A., Hasan, K., Tsuruda, J., Parker, D.: Analysis of partial volume effects in diffusion-tensor MRI. Magnetic Resonance in Medicine 45, 770-780 (2001) (Pubitemid 32402019)
2. Tuch, D., Reese, T., Wiegell, M., Makris, N., Belliveau, J., Wedeen, V.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magnetic Resonance in Medicine 48, 577-582 (2002)
3. Kreher, B., Schneider, J., Mader, I., Martin, E., Hennig, J., Il'yasov, K.: Multitensor approach for analysis and tracking of complex fiber configurations. Magnetic Resonance in Medicine 54, 1216-1225 (2005)
4. Peled, S., Friman, O., Jolesz, F., Westin, C.F.: Geometrically constrained two-tensor model for crossing tracts in DWI. Magnetic Resonance in Medicine 24(9), 1263-1270 (2006) (Pubitemid 44635063)
5. Basser, P., Pajevic, S.: Spectral decomposition of a 4th-order covariance tensor: Applications to diffusion tensor MRI. Signal Processing 87, 220-236 (2007)
6. Kaden, E., Knøsche, T., Anwander, A.: Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging. NeuroImage 37, 474-488 (2007) (Pubitemid 47444258)
7. Anderson, A.: Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magnetic Resonance in Medicine 54(5), 1194-1206 (2005)
8. Descoteaux, M., Deriche, R., Anwander, A.: Deterministic and probabilistic Q-ball tractography: from diffusion to sharp fiber distributions. Technical Report 6273, INRIA (2007)
9. Jian, B., Vemuri, B.: A unified computational framework for deconvolution to reconstruct multiple fibers from diffusion weighted MRI. Trans. on Medical Imaging 26(11), 1464-1471 (2007) (Pubitemid 350025484)
10. Jansons, K., Alexander, D.: Persistent angular structure: New insights from diffusion MRI data. Inverse Problems 19, 1031-1046 (2003)
11. Tournier, J.D., Calamante, F., Gadian, D., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23, 1176-1185 (2004) (Pubitemid 39446549)
12. Özarslan, E., Shepherd, T., Vemuri, B., Blackband, S., Mareci, T.: Resolution of complex tissue microarchitecture using the diffusion orientation transform. NeuroImage 31(3) (2006)
13. Guo, W., Zeng, Q., Chen, Y., Liu, Y.: Using multiple tensor deflection to reconstruct white matter fiber traces with branching. In: Int. Symp. on Biomedical Imaging, pp. 69-72 (2006)
14. Zhan, W., Yang, Y.: How accurately can the diffusion profiles indicate multiple fiber orientations? A study on general fiber crossings in diffusion MRI. J. of Magnetic Resonance 183, 193-202 (2006) (Pubitemid 44739334)
15. Schultz, T., Seidel, H.: Estimating crossing fibers: A tensor decomposition approach. Trans. on Visualization and Computer Graphics 14(6), 1635-1642 (2008)
16. Gössl, C., Fahrmeir, L., Pütz, B., Auer, L., Auer, D.: Fiber tracking from DTI using linear state space models: Detectability of the pyramidal tract. NeuroImage 16, 378-388 (2002) (Pubitemid 34852453)
17. Björnemo, M., Brun, A., Kikinis, R.,Westin, C.-F.: Regularized stochastic white matter tractography using diffusion tensor MRI. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 435-442. Springer, Heidelberg (2002)
18. Zhang, F., Goodlett, C., Hancock, E., Gerig, G.: Probabilistic fiber tracking using particle filtering. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 144-152 (2007)
19. Zhukov, L., Barr, A.: Oriented tensor reconstruction: Tracing neural pathways from diffusion tensor MRI. Visualization, 387-394 (2002)
20. Poupon, C., Roche, A., Dubois, J., Mangin, J.F., Poupon, F.: Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering. Medical Image Analysis 12(5), 527-534 (2008)
21. Behrens, T., Johansen-Berg, H., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34, 144-155 (2007)
22. Julier, S., Uhlmann, J.: Unscented filtering and nonlinear estimation. IEEE 92(3), 401-422 (2004)
23. Simon, D., Simon, D.: Kalman filtering with inequality constraints for turbofan engine health estimation. IEE Proc.-Control Theory and Appl. 153(3), 371-378 (2006)