Filipiak Patryk, Shepherd Timothy, Basler Lee, Zuccolotto Anthony, Placantonakis Dimitris G, Schneider Walter, Boada Fernando E, Baete Steven H
Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, NYU Langone Health, New York, NY, USA.
Psychology Software Tools, Inc., Pittsburgh, PA, USA.
Comput Diffus MRI. 2022 Nov;13722:89-100. doi: 10.1007/978-3-031-21206-2_8. Epub 2022 Dec 1.
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.
白质多室生物物理模型的拟合是一个不适定的优化问题。使其在计算上易于处理的一种方法是通过取向分布函数(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对微观结构参数进行采样,要么以均匀分布随机抽取这些参数。在本文中,我们提出了一种逐步随机适应机制,以生成专门针对手头扩散加权图像的ODF字典。我们在扩散模型和体内人脑图像上获得的结果表明,与ODF字典的先前(均匀)分布相比,我们重建的扩散率噪声更小,自由水分数的分离更明显。