Adaptive phase correction of diffusion-weighted images

Abstract : 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.
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Contributor : Rachid Deriche <>
Submitted on : Tuesday, December 10, 2019 - 11:58:06 AM
Last modification on : Tuesday, December 17, 2019 - 2:06:23 AM




Marco Pizzolato, Guillaume Gilbert, Jean-Philippe Thiran, Maxime Descoteaux, Rachid Deriche. Adaptive phase correction of diffusion-weighted images. NeuroImage, Elsevier, 2019, pp.116274. ⟨10.1016/j.neuroimage.2019.116274⟩. ⟨hal-02402015⟩



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