[1] Basser P J, Mattiello J. LeBihan D. MR diffusion tensor spectroscopy and imaging[J]. Biophysical Journal, 66, 259-267(1994). http://www.cell.com/biophysj/abstract/S0006-3495(94)80775-1

[2] Landman B A, Bogovic J A, Wan H L et al. Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI[J]. NeuroImage, 59, 2175-2186(2012). http://www.ncbi.nlm.nih.gov/pubmed/20396591

[3] Ramirez-Manzanares A, Rivera M, Vemuri B C et al. Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry[J]. IEEE Transactions on Medical Imaging, 26, 1091-1102(2007). http://ieeexplore.ieee.org/document/4280898

[4] Chu C Y, Huang J P, Sun C Y et al. Resolving intravoxel fiber architecture using nonconvex regularized blind compressed sensing[J]. Physics in Medicine & Biology, 60, 2339-2354(2015). http://europepmc.org/abstract/med/25716031

[5] Özarslan E, Mareci T H. Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging[J]. Magnetic Resonance in Medicine, 50, 955-965(2003). http://europepmc.org/abstract/MED/14587006

[6] Tuch D S. Q-ball imaging[J]. Magnetic Resonance in Medicine, 52, 1358-1372(2004).

[7] Descoteaux M, Angelino E, Fitzgibbons S et al. Regularized, fast, and robust analytical Q-ball imaging[J]. Magnetic Resonance in Medicine, 58, 497-510(2007).

[8] Wedeen V J, Hagmann P. Tseng W Y I, et al. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging[J]. Magnetic Resonance in Medicine, 54, 1377-1386(2005). http://onlinelibrary.wiley.com/doi/10.1002/mrm.20642/full

[9] Tournier J D, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution[J]. NeuroImage, 35, 1459-1472(2007). http://www.sciencedirect.com/science/article/pii/S1053811907001243

[10] Tournier J D, Calamante F, Gadian D G et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution[J]. NeuroImage, 23, 1176-1185(2004). http://europepmc.org/abstract/MED/15528117

[11] Kaden E, Knösche T R, Anwander A. Parametric spherical deconvolution: inferring anatomical connectivity using diffusion MR imaging[J]. NeuroImage, 37, 474-488(2007). http://www.sciencedirect.com/science/article/pii/S1053811907004211

[12] Dell'Acqua F, Scifo P, Rizzo G et al. . A modified damped Richardson-Lucy algorithm to reduce isotropic background effects in spherical deconvolution[J]. NeuroImage, 49, 1446-1458(2010). http://www.sciencedirect.com/science/article/pii/S105381190901012X

[13] Dell'Acqua F, Rizzo G, Scifo P et al. . A model-based deconvolution approach to solve fiber crossing in diffusion-weighted MR imaging[J]. IEEE Transactions on Biomedical Engineering, 54, 462-472(2007). http://europepmc.org/abstract/MED/17355058

[14] Kim S, Jeong J W, Singh M. Estimation of multiple fiber orientations from diffusion tensor MRI using independent component analysis[J]. IEEE Transactions on Nuclear Science, 52, 266-273(2005). http://ieeexplore.ieee.org/document/1417140/

[15] Wu Y C, Alexander A L. Hybrid diffusion imaging[J]. NeuroImage, 36, 617-629(2007).

[16] Hasan K M, Walimuni I S, Abid H et al. A review of diffusion tensor magnetic resonance imaging computational methods and software tools[J]. Computers in Biology and Medicine, 41, 1062-1072(2011). http://europepmc.org/articles/PMC3135778

[17] Assemlal H E, Tschumperlé D, Brun L et al. Recent advances in diffusion MRI modeling: angular and radial reconstruction[J]. Medical Image Analysis, 15, 369-396(2011). http://europepmc.org/abstract/MED/21397549

[18] Ma H Q, Ma S P, Xu Y L et al. Adaptive image denoising based on improved stacked sparse denoising auto-encoder[J]. Acta Optica Sinica, 38, 1010001(2018).

[19] Zhang Y G, Yi B S, Wu C Y et al. Low-dose CT image denoising method based on convolutional neural network[J]. Acta Optica Sinica, 38, 0410003(2018).

[20] Xue Z X, Yu X C, Tan X et al. Local hypergraph Laplacian regularized low-rank representation for noise reduction of hyperspectral images[J]. Acta Optica Sinica, 37, 0510001(2017).

[21] Goh A, Lenglet C, Thompson P M et al. Estimating orientation distribution functions with probability density constraints and spatial regularity[M]. ∥Yang G Z, Hawkes D, Rueckert D, et al. Medical image computing and computer-assisted intervention. Lecture notes in computer science, Berlin, Heidelberg: Springer, 5761, 877-885(2009).

[22] Reisert M, Kiselev V G. Fiber continuity: an anisotropic prior for ODF estimation[J]. IEEE Transactions on Medical Imaging, 30, 1274-1283(2011). http://www.ncbi.nlm.nih.gov/pubmed/21317082

[23] Cabales-Rodríguez E J. Richardson-Lucy spherical deconvolution under Rician noise with total variation spatial regularization. [C]∥2013 IEEE 10th International Symposium on Biomedical Imaging(ISBI 2013), April 7-11, 2013, San Francisco, CA, USA. New York: IEEE(2013).

[24] Xu J, Ma J W, Zhang D M et al. Improved total variation minimization method for compressive sensing by intra-prediction[J]. Signal Processing, 92, 2614-2623(2012). http://dl.acm.org/citation.cfm?id=2305715

[25] Dey N, Blanc-Feraud L, Zimmer C et al. Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution[J]. Microscopy Research and Technique, 69, 260-266(2006). http://www.ncbi.nlm.nih.gov/pubmed/16586486

[26] Poupon C, Rieul B, Kezele I et al. New diffusion phantoms dedicated to the study and validation of high-angular-resolution diffusion imaging (HARDI) models[J]. Magnetic Resonance in Medicine, 60, 1276-1283(2008). http://www.ncbi.nlm.nih.gov/pubmed/19030160