Fast and accurate reconstruction of HARDI data using compressed sensing

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

Volumn 6361 LNCS, Issue PART 1, 2010, Pages 607-614

  Michailovich, Oleg ^a   Rathi, Yogesh ^b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE ESTIMATION; ALTERNATIVE APPROACH; CLINICAL PRACTICES; COMPRESSED SENSING; COMPRESSIVE SAMPLING; DIFFUSION GRADIENTS; HIGH ANGULAR RESOLUTION DIFFUSION IMAGING; MEAN SQUARED ERROR; PRACTICAL IMPLEMENTATION; WATER DIFFUSION; WHITE MATTER;

DIFFUSION; MEDICAL COMPUTING; SIGNAL RECONSTRUCTION;

MEDICAL IMAGING;

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

ALGORITHMS; BRAIN; DATA COMPRESSION; 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;

EID: 84883834336     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15705-9_74     Document Type: Conference Paper

References (24)

1. Mori, S.: Introduction to diffusion tensor imaging. Elsevier, Amsterdam (2007)
2. Johansen-Berg, H., Behrens, T.E.J.: Diffusion MRI: From quantitative measurements to in-vivo neuroanatomy, 1st edn. Academic Press, London (2009)
3. Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. Imag. B 103(3), 247-254 (1994)
4. Bihan, D.L., Mangin, J.F., Poupon, C., Clark, C., Pappata, S., Molko, N., Chabriat, H.: Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging 13, 534-546 (2001)
5. Behrens, T., Woolrich, M., Jenkinson, M., Johansen-Berg, H., Nunes, R., Clare, S., Matthews, P., Brady, J., Smith, S.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine 50, 1077-1088 (2003)
6. Frank, L.: Anisotropy in high angular resolution diffusion-tensor MRI. Magnetic Resonance in Medicine 45, 935-939 (2001)
7. Alexander, D.C., Barker, G.J., Arridge, S.R.: Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Journal of Magnetic Resonance Imaging 48(2), 331-340 (2002)
8. Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Wedeen, V.J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magnetic Resonance in Medicine 48, 577-582 (2002)
9. Alexander, D.C.: Multiple-fiber reconstruction algorithms for diffusion MRI. Annals of the New York Academy of Science 1064, 113-133 (2005)
10. Frank, L.R.: Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magnetic Resonance in Medicine 47, 1083-1099 (2002)
11. Anderson, A.W.: Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magnetic Resonance in Medicine 54, 1194-1206 (2005)
12. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Apparent diffusion coefficients from high angular resolution diffusion images: Estimation and applications. Magnetic Resonance in Medicine 56(2), 395-410 (2006)
13. Behrens, T., Johansen-Berg, H., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fiber orientations: What can we gain? NeuroImage 34, 144-155 (2007)
14. Candes, E., Romberg, J.: Quantitative robust uncer tainty principles and optimally sparse decompositions. Foundations of Computational Mathematics 6(2), 227-254 (2006)
15. Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52(2), 489-509 (2006)
16. Donoho, D.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289-1306 (2006)
17. Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine 58(6), 1182-1195 (2007)
18. Jung, H., Ye, J.C., Kim, E.Y.: Improved k-t BLASK and k-t SENSE using FOCUSS. Physics in Medicine and Biology 52, 3201-3226 (2007)
19. Jung, H., Sung, K., Nayak, K.S., Kim, E.Y., Ye, J.C.: k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI. Magnetic Resonance in Medicine 61, 103-116 (2009)
20. Michailovich, O., Rathi, Y.: On approximation of orientation distributions by means of spherical ridgelets. IEEE Transactions on Image Processing 19(2), 461-477 (2010)
21. Groemer, H.: Geometric applications of Fourier series and spherical harmonics. Cambridge University Press, Cambridge (1996)
22. Poupon, C., Rieul, B., Kezele, I., Perrin, M., Poupon, F., Mangin, J.F.: New diffusion phantoms dedicated to the study and validation of high-angular-resolution diffusion imaging (HARDI) models. Magn. Reson. Med. 60(6), 1276-1283 (2008)
23. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical Q-ball imaging. Magnetic Resonance in Medicine 58, 497-510 (2007)
24. Hess, C.P., Mukherjee, P., Han, E.T., Xu, D., Vigneron, D.R.: Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magnetic Resonance in Medicine 56, 104-117 (2006)