Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Karolinska Institutet, Department of Neuroscience, Stockholm, Sweden; Brain and Mind Institute, Swiss Federal Institute of Technology in Lausanne, Lausanne, Switzerland.
Neuroimage. 2018 Nov 15;182:62-79. doi: 10.1016/j.neuroimage.2018.06.049. Epub 2018 Jun 18.
Extracting microanatomical information beyond the image resolution of MRI would provide valuable tools for diagnostics and neuroscientific research. A number of mathematical models already suggest microstructural interpretations of diffusion MRI (dMRI) data. Examples of such microstructural features could be cell bodies and neurites, e.g. the axon's diameter or their orientational distribution for global connectivity analysis using tractography, and have previously only been possible to access through conventional histology of post mortem tissue or invasive biopsies. The prospect of gaining the same knowledge non-invasively from the whole living human brain could push the frontiers for the diagnosis of neurological and psychiatric diseases. It could also provide a general understanding of the development and natural variability in the healthy brain across a population. However, due to a limited image resolution, most of the dMRI measures are indirect estimations and may depend on the whole chain from experimental parameter settings to model assumptions and implementation. Here, we review current literature in this field and highlight the integrative work across anatomical length scales that is needed to validate and trust a new dMRI method. We encourage interdisciplinary collaborations and data sharing in regards to applying and developing new validation techniques to improve the specificity of future dMRI methods.
超越 MRI 图像分辨率提取微观解剖信息将为诊断和神经科学研究提供有价值的工具。许多数学模型已经提出了扩散 MRI(dMRI)数据的微观结构解释。例如,细胞体和神经突等微观结构特征的例子,或者使用轨迹追踪进行整体连通性分析的轴突直径或它们的取向分布,以前只能通过死后组织的传统组织学或侵入性活检来获得。从整个活体人脑无创获得相同知识的前景可能会推动神经和精神疾病诊断的前沿。它还可以提供对整个人群中健康大脑的发育和自然变异性的一般了解。然而,由于图像分辨率有限,大多数 dMRI 测量都是间接估计,可能取决于从实验参数设置到模型假设和实现的整个链条。在这里,我们回顾了该领域的当前文献,并强调了需要跨解剖学长度尺度进行综合工作,以验证和信任新的 dMRI 方法。我们鼓励跨学科合作和数据共享,以应用和开发新的验证技术来提高未来 dMRI 方法的特异性。