Complex diffusion-weighted image estimation via matrix recovery under general noise models

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100 Citations (Scopus)
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We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.
Original languageEnglish
Pages (from-to)391-404
Number of pages14
Early online date18 Jun 2019
Publication statusPublished - 15 Oct 2019


  • Asymptotic risk
  • Diffusion weighted imaging
  • Optimal shrinkage
  • Random matrix denoising
  • Rician bias


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