Within-subject template estimation for unbiased longitudinal image analysis

• Published in: NeuroImage (Orlando, Fla.) Vol. 61; no. 4; pp. 1402 - 1418
• Main Authors: Reuter, Martin, Schmansky, Nicholas J., Rosas, H. Diana, Fischl, Bruce
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 16.07.2012; Elsevier Limited
• Subjects: Algorithms; Bias; Brain - anatomy & histology; Brain - physiology; FreeSurfer; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Methods; MRI biomarkers; Neurodegeneration; Noise; Reliability and power; Software; Standard deviation; Studies; Unbiased longitudinal image processing; Within-subject template; Reliability and power; Unbiased longitudinal image processing; Within-subject template; FreeSurfer; MRI biomarkers
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.02.084

Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects. ► We introduce unbiased longitudinal processing of brain MRI of several time points. ► We demonstrate that inerpolation asymmetries are not the only source of bias. ► We create a robust within-subject template to initialize all time points. ► Reliability is significantly increased, while over-regularization is avoided. ► Precision allows for smaller sample sizes in clinical trials to assess biomarkers.