Data-driven normative values based on generative manifold learning for quantitative MRI.

ABSTRACT: In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often provided as deviations from so-called normal values. These normative reference values are traditionally calculated based on the quantitative values from a control group, which can be adjusted for relevant clinical co-variables, such as age or sex. However, these average normative values do not take into account the globality of the available quantitative information. For example, quantitative analysis of T1-weighted magnetic resonance images based on anatomical structure segmentation frequently includes over 100 cerebral structures in the quantitative reports, and these tend to be analyzed separately. In this study, we propose a global approa

SUBMITTER: Attye A 

PROVIDER: S-EPMC10981723 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature