Efficient and robust computation of PDF features from diffusion MR signal

被引:62
作者
Assemlal, Haz-Edine [1 ]
Tschumperle, David [1 ]
Brun, Luc [1 ]
机构
[1] GREYC, CNRS, UMR 6072, F-14050 Caen, France
关键词
Diffusion MRI; Gaussian-Laguerre; Spherical harmonics; Rician noise; Variational framework; SELF-DIFFUSION; NMR DIFFUSION; HUMAN BRAIN; IN-VIVO; NOISE; RESOLUTION; TENSOR; FIELD; COEFFICIENTS; DIFFRACTION;
D O I
10.1016/j.media.2009.06.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:715 / 729
页数:15
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