Adaptive phase correction of diffusion-weighted images.

Autor: Pizzolato M; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. Electronic address: marco.pizzolato@epfl.ch., Gilbert G; MR Clinical Science, Philips Healthcare Canada, Markham, ON, Canada., Thiran JP; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland., Descoteaux M; Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada., Deriche R; Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, France.
Jazyk: angličtina
Zdroj: NeuroImage [Neuroimage] 2020 Feb 01; Vol. 206, pp. 116274. Date of Electronic Publication: 2019 Oct 17.
DOI: 10.1016/j.neuroimage.2019.116274
Abstrakt: Phase correction (PC) is a preprocessing technique that exploits the phase of images acquired in Magnetic Resonance Imaging (MRI) to obtain real-valued images containing tissue contrast with additive Gaussian noise, as opposed to magnitude images which follow a non-Gaussian distribution, e.g. Rician. PC finds its natural application to diffusion-weighted images (DWIs) due to their inherent low signal-to-noise ratio and consequent non-Gaussianity that induces a signal overestimation bias that propagates to the calculated diffusion indices. PC effectiveness depends upon the quality of the phase estimation, which is often performed via a regularization procedure. We show that a suboptimal regularization can produce alterations of the true image contrast in the real-valued phase-corrected images. We propose adaptive phase correction (APC), a method where the phase is estimated by using MRI noise information to perform a complex-valued image regularization that accounts for the local variance of the noise. We show, on synthetic and acquired data, that APC leads to phase-corrected real-valued DWIs that present a reduced number of alterations and a reduced bias. The substantial absence of parameters for which human input is required favors a straightforward integration of APC in MRI processing pipelines.
(Copyright © 2019 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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