Adaptive phase correction of diffusion-weighted images

被引:15
作者
Pizzolato, Marco [1 ]
Gilbert, Guillaume [2 ]
Thiran, Jean-Philippe [1 ,3 ,4 ]
Descoteaux, Maxime [5 ]
Deriche, Rachid [6 ]
机构
[1] Ecole Polytech Fed Lausanne, Signal Proc Lab LTS5, Lausanne, Switzerland
[2] Philips Healthcare Canada, MR Clin Sci, Markham, ON, Canada
[3] CHU Vaudois, Radiol Dept, Lausanne, Switzerland
[4] Univ Lausanne, Lausanne, Switzerland
[5] Univ Sherbrooke, SCIL, Sherbrooke, PQ, Canada
[6] Univ Cote Azur, Inria Sophia Antipolis Mediterranee, Nice, France
基金
瑞士国家科学基金会; 欧盟地平线“2020”;
关键词
Phase correction; Phase estimation; Oriented laplacian; Diffusion MRI; Rician noise; MAGNETIC-RESONANCE IMAGES; TO-NOISE RATIO; MR-IMAGES; FOURIER RECONSTRUCTION; ANISOTROPIC DIFFUSION; RICIAN NOISE; MAP-MRI; TENSOR; REGULARIZATION; FRAMEWORK;
D O I
10.1016/j.neuroimage.2019.116274
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页数:17
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