Daianu Madelaine, Jacobs Russell E, Weitz Tara M, Town Terrence C, Thompson Paul M
Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, United States of America.
Department of Neurology, UCLA School of Medicine, Los Angeles, CA, United States of America.
PLoS One. 2015 Dec 18;10(12):e0145205. doi: 10.1371/journal.pone.0145205. eCollection 2015.
Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. Diffusion tensor imaging (DTI) and high-angular resolution imaging (HARDI) are frequently used in radiology and neuroscience research but can be limited in describing the signal behavior in composite nerve fiber structures. Here, we developed and assessed the benefit of a comprehensive diffusion encoding scheme, known as hybrid diffusion imaging (HYDI), composed of 300 DWI volumes acquired at 7-Tesla with diffusion weightings at b = 1000, 3000, 4000, 8000 and 12000 s/mm2 and applied it in transgenic Alzheimer rats (line TgF344-AD) that model the full clinico-pathological spectrum of the human disease. We studied and visualized the effects of the multiple concentric "shells" when computing three distinct anisotropy maps-fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA). We tested the added value of the multi-shell q-space sampling scheme, when reconstructing neural pathways using mathematical frameworks from DTI and q-ball imaging (QBI). We show a range of properties of HYDI, including lower apparent anisotropy when using high b-value shells in DTI-based reconstructions, and increases in apparent anisotropy in QBI-based reconstructions. Regardless of the reconstruction scheme, HYDI improves FA-, GFA- and NQA-aided tractography. HYDI may be valuable in human connectome projects and clinical research, as well as magnetic resonance research in experimental animals.
扩散加权成像（DWI）被广泛用于研究大脑的微观结构特征。扩散张量成像（DTI）和高角分辨率成像（HARDI）在放射学和神经科学研究中经常使用，但在描述复合神经纤维结构中的信号行为时可能存在局限性。在此，我们开发并评估了一种综合扩散编码方案——混合扩散成像（HYDI）的优势，该方案由在7特斯拉下采集的300个DWI容积组成，扩散权重为b = 1000、3000、4000、8000和12000 s/mm2，并将其应用于模拟人类疾病完整临床病理谱的转基因阿尔茨海默病大鼠（TgF344-AD品系）。我们在计算三种不同的各向异性图——分数各向异性（FA）、广义分数各向异性（GFA）和归一化定量各向异性（NQA）时，研究并可视化了多个同心“壳”的影响。我们在使用来自DTI和q球成像（QBI）的数学框架重建神经通路时，测试了多壳q空间采样方案的附加值。我们展示了HYDI的一系列特性，包括在基于DTI的重建中使用高b值壳时较低的表观各向异性，以及在基于QBI的重建中表观各向异性的增加。无论重建方案如何，HYDI都能改善基于FA、GFA和NQA的纤维束成像。HYDI在人类连接组计划和临床研究以及实验动物的磁共振研究中可能具有重要价值。
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