TY - JOUR
T1 - Complex diffusion-weighted image estimation via matrix recovery under general noise models
AU - Cordero Grande, Lucilio
AU - Christiaens, Daan Julia Paul
AU - Hutter, Jana Maria
AU - Price, Anthony Neil
AU - Hajnal, Joseph Vilmos
PY - 2019/10/15
Y1 - 2019/10/15
N2 - 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.
AB - 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.
KW - Asymptotic risk
KW - Diffusion weighted imaging
KW - Optimal shrinkage
KW - Random matrix denoising
KW - Rician bias
UR - http://www.scopus.com/inward/record.url?scp=85068410237&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2019.06.039
DO - 10.1016/j.neuroimage.2019.06.039
M3 - Article
SN - 1053-8119
VL - 200
SP - 391
EP - 404
JO - NeuroImage
JF - NeuroImage
ER -