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Bayesian Dictionary Learning and Undersampled Multishell HARDI Reconstruction

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10265)

Abstract

High angular resolution diffusion imaging (HARDI) at higher b values leads to signal measurements having (exponentially) lower magnitudes, a strong Rician bias, and more corruptions from artifacts. Typical undersampled-HARDI reconstruction methods assume Gaussian noise models and limited/no regularization, leading to underestimated tract anisotropy and reduced ability to detect crossings. We propose novel Bayesian frameworks to model Rician statistics during dictionary learning and reconstruction. For dictionary learning, we propose edge-preserving smoothness priors on dictionary atoms. For reconstruction, we employ sparsity-based multiscale smoothness priors on the reconstructed image. In both frameworks, we propose kernel-based non-local regularization on dictionary coefficients and stronger sparsity via quasi norms. The results show improved dictionaries and reconstructions, over the state of the art.

Keywords

  • HARDI
  • Dictionary learning
  • Reconstruction
  • Bayesian estimation
  • Rician statistics
  • Non-local regularization
  • Quasi-norm sparsity

S.P. Awate—Thanks to funding from IIT Bombay Seed Grant 14IRCCSG010.

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Correspondence to Suyash P. Awate .

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Gupta, K., Awate, S.P. (2017). Bayesian Dictionary Learning and Undersampled Multishell HARDI Reconstruction. In: , et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_36

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  • DOI: https://doi.org/10.1007/978-3-319-59050-9_36

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