Chamberland Maxime, Genc Sila, Tax Chantal M W, Shastin Dmitri, Koller Kristin, Raven Erika P, Cunningham Adam, Doherty Joanne, van den Bree Marianne B M, Parker Greg D, Hamandi Khalid, Gray William P, Jones Derek K
Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands.
Nat Comput Sci. 2021 Sep;1:598-606. doi: 10.1038/s43588-021-00126-8. Epub 2021 Sep 22.
Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside.
大多数疾病的扩散磁共振成像研究依靠对大量患者和健康参与者进行统计比较，以推断大脑中组织状态的改变；然而，临床异质性会极大地挑战其辨别能力。目前迫切需要摆脱当前的组间比较方法，转向能够在个体水平检测组织状态改变的敏感方法。这最终将能够早期检测和解释个体患者的微观结构异常，这是转化成像中迈向个性化医疗的重要一步。为此，开发了Detect软件，以推动扩散磁共振成像纤维束示踪术向单患者分析发展。通过在白质通路流形上操作并学习规范的微观结构特征，我们的框架捕捉了沿白质通路模式中的特质。我们的方法为从传统的基于组的比较转向真正的个性化放射学铺平了道路，将微观结构成像从实验室带到了床边。
