Within-subject template estimation for unbiased longitudinal image analysis
Open accessAbstract
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.
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FreeSurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths.
“FreeSurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths 31,41 .”
From OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019) · cites this paper as [41]
The Results and discussion sections on Data and Test–retest reliability report reliable FreeSurfer measurements in separate 3T and 1.5T test–retest datasets. Both datasets used Siemens scanners, however, and the paper does not demonstrate test–retest reliability across scanner manufacturers or across field strengths within a test–retest comparison. Page numbers were not available in our copy. (LLM, conf=0.88)
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BackgroundThe association between white matter hyperintensities and amyloid and tau deposition
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- Authors
- Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B.
- Affiliations
- Harvard University (US)
- Corresponding author
- Reuter, Martin
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 61
- Issue
- 4
- Pages
- 1402-1418
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3389460
- OpenAlex
- W2139886607
- Cited by
- 2,356
- References
- 70
- Citations by year
- 2026: 160 · 2025: 179 · 2024: 191 · 2023: 196 · 2022: 221 · 2021: 227 · 2020: 219 · 2019: 185 · 2018: 178 · 2017: 164 · 2016: 158 · 2015: 105 · 2014: 112 · 2013: 43 · 2012: 17
- FWCI
- 91.19
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Center for Research Resources (P41RR14075, BIRN002, U24RR021382)
- National Institute of Neurological Disorders and Stroke (R21NS072652, R01NS052585, R01NS042861, P01NS058793, R01NS070963)
- Ellison Medical Foundation
- National Institute of Biomedical Imaging and Bioengineering (R01EB006758)
- National Institute on Aging (U01AG024904, R01AG022381)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- longitudinal MRI, processing bias, brain surface reconstruction, brain segmentation, structural MRI, overregularization, neurodegenerative diseases, normal aging, disease biomarkers
- MeSH terms
- Algorithms, Brain, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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