Abstract
Among the current tools of diffusion Magnetic Resonance Imaging (dMRI), high-angular resolution diffusion imaging (HARDI) excels in its ability to delineate complex directional patterns of water diffusion at any predefined location within the brain. It is known that HARDI signals present a practical trade-off between their directional resolution and their signal-to-noise ratio (SNR), suggesting the need for effective denoising algorithms for HARDI measurements. The most effective approaches to alleviate this problem have been proven to be those which exploit both the directional and the spatial regularity of HARDI signals. Unfortunately, many of these algorithms entail substantial computational burdens. Accordingly, we propose a formulation of the problem of reconstruction of HARDI signals which leads to a particularly simple numerical implementation. The proposed algorithm allows for the separation of the original problem into procedural steps which can be executed in parallel, which suggests its computational advantage.
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References
Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. 103(3), 247–254 (1994)
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. Magn. Reson. Med. 48, 577–582 (2002)
Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52, 1358–1372 (2004)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical Q-ball imaging. Magn. Res. Med. 58(3), 497–510 (2007)
Michailovich, O., Rathi, Y.: On approximation of orientation distributions by means of spherical ridgelets. IEEE Trans. Image Proc. 19(3), 1–17 (2010)
Aja-Fernández, S., Niethammer, M., Kubicki, M., Shenton, M., Westin, C.-F.: Restoration of DWI data using a Rician LMMSE estimator. IEEE Trans. Med. Imaging 27(10), 1389–1403 (2008)
Kim, Y., Thompson, P.M., Toga, A.W., Vese, L., Zhan, L.: HARDI denoising: Variational regularization of the spherical apparent diffusion coefficient sADC. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 515–527. Springer, Heidelberg (2009)
Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S.P., Barillot, C.: Rician noise removal by Non-Local Means filtering for low signal-to-noise ratio MRI: Applications to DT-MRI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 171–179. Springer, Heidelberg (2008)
Michailovich, O., Rathi, Y., Dolui, S.: Spatially regularized compressed sensing for high angular resolution diffusion imaging. IEEE Trans. Med. Imaging 30(5), 1100–1115 (2011)
Chen, Y., Guo, W., Zeng, Q., Yan, X., Huang, F., Zhang, H., Guojun, H., Vemuri, B.C., Liu, Y.: Estimation, smoothing, and characterization of apparent diffusion coefficient profiles from high angular resolution DWI. In: Proceed. of CVPR, pp. 588–593 (2004)
Esser, E.: Applications of Lagrangian-based alternating direction methods and connections to split Bregman. Technical Report (2009)
Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imag. Vis. 20, 89–97 (2004)
Bresson, X., Chan, T.F.: Fast dual minimization of the vectorial total variation norm and applications to color image processing. Inverse Problems and Imaging 2(4), 455–484 (2008)
Poupon, C., Rieul, B., Kezel, 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. Res. Med. 60(6), 1276–1283 (2008)
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Patarroyo, I.C.S., Dolui, S., Michailovich, O.V., Vrscay, E.R. (2013). Reconstruction of HARDI Data Using a Split Bregman Optimization Approach. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_67
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DOI: https://doi.org/10.1007/978-3-642-39094-4_67
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