逐步随机字典自适应改进取向分布函数指纹显微结构重建。

Patryk Filipiak, Timothy Shepherd, Lee Basler, Anthony Zuccolotto, Dimitris G Placantonakis, Walter Schneider, Fernando E Boada, Steven H Baete
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引用次数: 0

摘要

白质多室生物物理模型的拟合是一个不适定优化问题。使其易于计算处理的一种方法是通过方向分布函数(ODF)指纹识别。然而,该方法的准确性仅依赖于ODF字典生成机制,该机制要么在多维网格上采样微观结构参数,要么以均匀分布的方式随机绘制它们。在本文中,我们提出了一种逐步随机自适应机制来生成专门针对手头扩散加权图像的ODF字典。我们在扩散幻像和活体人脑图像上获得的结果表明,我们重建的扩散系数噪声更小,自由水分数的分离比先前(均匀)分布的ODF字典更明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.

Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.

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