Abstract
This paper presents a new compressed sensing framework for multishell HARDI. Unlike methods that model diffusion signals using analytical bases, we learn a dictionary of multishell diffusion signals, with a proposed regularization term to handle low signal-to-noise ratios at high b values. We combine the dictionary model for diffusion signals together with a multiscale (wavelet-based) spatial model on images for compressed sensing. To control overfitting of the dictionary to tracts with unknown orientations, we use a strong non-sparsity penalty that behaves close to the desirable L 0 pseudo-norm. Our framework allows undersampling gradient directions, shells, and k-space. The results show improved reconstructions from our framework, over the state of the art.
Suyash P. Awate thanks funding via IIT Bombay Seed Grant 14IRCCSG010. All work done at IIT Bombay.
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Gupta, K., Adlakha, D., Agarwal, V., Awate, S.P. (2017). Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARDI. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. MICCAI 2016. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-54130-3_3
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