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

2018/12/14 by Lucilio Cordero‐Grande, Daan Christiaens, Cordero-Grande, Lucilio +7 · 12 citations
Medicine · #62P10 #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #MRI in cancer diagnosis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1812.05954

openalex publication_date 2018/12/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a patch-based singular value shrinkage method for diffusion\nmagnetic resonance image estimation targeted at low signal to noise ratio and\naccelerated acquisitions. It operates on the complex data resulting from a\nsensitivity encoding reconstruction, where asymptotically optimal signal\nrecovery guarantees can be attained by modeling the noise propagation in the\nreconstruction and subsequently simulating or calculating the limit singular\nvalue spectrum. Simple strategies are presented to deal with phase\ninconsistencies and optimize patch construction. The pertinence of our\ncontributions is quantitatively validated on synthetic data, an in vivo adult\nexample, and challenging neonatal and fetal cohorts. Our methodology is\ncompared with related approaches, which generally operate on magnitude-only\ndata and use data-based noise level estimation and singular value truncation.\nVisual examples are provided to illustrate effectiveness in generating denoised\nand debiased diffusion estimates with well preserved spatial and diffusion\ndetail.\n

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