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Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARDI

Part of the Mathematics and Visualization book series (MATHVISUAL)

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.

Keywords

  • Compressed sensing
  • Dictionary learning
  • HARDI
  • Multishell

Suyash P. Awate thanks funding via IIT Bombay Seed Grant 14IRCCSG010. All work done at IIT Bombay.

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Correspondence to Suyash P. Awate .

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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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