Within-subject template estimation for unbiased longitudinal image analysis

By Martin Reuter, N. Schmansky, H. Rosas, Bruce Fischl. NeuroImage, 2012

2,382 citations recorded by the source corpus.

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.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/df42b6954e64770a46fd2d5480727920ef8c1e03

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Citations in the corpus

Growth of language-related brain areas after foreign language learning (2012, methodology): “FreeSurfer uses intensity and continuity information from MR volumes to reconstruct and measure cortical thickness at each vertex of the brains surface (Dale et al., 1999; Fischl and Dale, 2000).”
Statistical Analysis of Longitudinal Neuroimage Data with Linear Mixed Effects Models (2012, methodology): “See also (Reuter et al., 2012) where a similar bias was quantified by processing the first time point cross-sectionally and the second longitudinally (initializing it with results from the first) in a test–retest study with no expected structural change.”
Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach (2017, methodology): “For skull stripping, we use FreeSurfer [63] for the OASIS dataset and AutoSeg [64] for the IBIS dataset.”
Mindboggling morphometry of human brains (2016, result): “Our absolute % errors in test-retest volumetric estimates are comparable to those reported by previous studies (Kruggel et al., 2010; Morey et al., 2010; Reuter et al., 2012).””
What is normal in normal aging? Effects of Aging, Amyloid and Alzheimer’s Disease on the Cerebral Cortex and the Hippocampus (2014, background): “With high-quality MR-scans and state-of-the-art analysis tools (Reuter et al., 2010; Holland and Dale, 2011; Holland et al., 2012b; Reuter et al., 2012), brain changes can be detected reliably and monitored over intervals as”
OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019, neutral): “FreeSurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths 31,41 .”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “romising approach for segmenting serial scans. Longitudinal image analysis is an area of growing importance and the detection of subtle longitudinal changes can call for highly accurate segmentation (Reuter et al., 2012). Encouraged by some recent applications (Wolz et al. 2010band Li et al. 2014), ”
Unique developmental trajectories of cortical thickness and surface area (2014, positive): “This reduced the potential over-regularization of longitudinal image processing (Reuter et al., 2012).”
Head motion during MRI acquisition reduces gray matter volume and thickness estimates (2015, positive): “7; Gray matter (GM) volumes were estimated using voxel based morphometry VBM8 toolbox [12] of the SPM8 package [2]; Cortical thickness and gray matter volume were estimated using both the independent [11,10] and longitudinal image-processing stream [23,22] of FreeSurfer 5.”
Structural brain development between childhood and adulthood: Convergence across four longitudinal samples (2016, methodology): “Images were then processed using FreeSurfer 5.3's longitudinal stream (Reuter et al., 2012).”
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements (2014, methodology): “This study was limited to a cross-sectional investigation thus limiting extrapolations of ANTs performance to longitudinal data unlike recent FreeSurfer extensions which accommodate longitudinal data (Jovicich et al., 2013; Reuter et al., 2012).”