Abstract
The cortical thickness has been used as a biomarker to assess different cerebral conditions and to detect alterations in the cortical mantle. In this work, we compare methods from the FreeSurfer software, the Computational Anatomy Toolbox (CAT12), a Laplacian approach and a new method here proposed, based on the Euclidean Distance Transform (EDT), and its corresponding computational phantom designed to validate the calculation algorithm. At region of interest (ROI) level, within- and inter-method comparisons were carried out with a test–retest analysis, in a subset comprising 21 healthy subjects taken from the Multi-Modal MRI Reproducibility Resource (MMRR) dataset. From the Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD) data, classification methods were compared in their performance to detect cortical thickness differences between 23 healthy controls (HC) and 45 subjects with Alzheimer’s disease (AD). The validation of the proposed EDT-based method showed a more accurate and precise distance measurement as voxel resolution increased. For the within-method comparisons, mean test–retest measures (percentages differences/intraclass correlation/Pearson correlation) were similar for FreeSurfer (1.80%/0.90/0.95), CAT12 (1.91%/0.83/0.91), Laplacian (1.27%/0.89/0.95) and EDT (2.20%/0.88/0.94). Inter-method correlations showed moderate to strong values (R > 0.77) and, in the AD comparison study, all methods were able to detect cortical alterations between groups. Surface- and voxel-based methods have advantages and drawbacks regarding computational demands and measurement precision, while thickness definition was mainly associated to the cortical thickness absolute differences among methods. However, for each method, measurements were reliable, followed similar trends along the cortex and allowed detection of cortical atrophies between HC and patients with AD.
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Data Availability
Data used in the preparation of this article were obtained from the public MIRIAD database (https://www.ucl.ac.uk/drc/research/methods/minimal-interval-resonance-imaging-alzheimers-disease-miriad) And the Multi-Modal MRI Reproducibility Resource at (https://www.nitrc.org/projects/multimodal).
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Acknowledgements
This work was supported by grant number 479002, and registration number 629404, from CONACYT (Consejo Nacional de Ciencia y Tecnología, Mexico), for a Master’s in Science in Medical Physics at the UNAM. Data used in the preparation of this article were obtained from the MIRIAD database. We are also grateful to M.Sc. L. González-Santos for technical support and J. González-Norris for editing of the manuscript. The MIRIAD investigators did not participate in analysis or writing of this report. The MIRIAD dataset is made available through the support of the UK Alzheimer’s Society (Grant RF116).
Funding
The original data collection was funded through an unrestricted educational grant from GlaxoSmithKline (Grant 6GKC) and funding from the UK Alzheimer’s Society and the Medical Research Council. Also receives funding from the EPSRC (EP/H046410/1) and the Comprehensive Biomedical Research Centre (CBRC) Strategic Investment Award (Ref. 168). And support by the Medical Research Council (grant number MR/J014257/1). This work was supported by the National Institute for Health Research (NIHR) Biomedical Research Unit in Dementia based at University College London Hospitals (UCLH), University College London (UCL). The Multi-Modal MRI Reproducibility Resource (MMRR) dataset received funding from NIH/NCRR P41RR15241 NIH/NINDS 1R01NS056307. The MIRIAD or MMMRIRR investigators did not participate in analysis or writing of this report.
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Velázquez, J., Mateos, J., Pasaye, E.H. et al. Cortical Thickness Estimation: A Comparison of FreeSurfer and Three Voxel-Based Methods in a Test–Retest Analysis and a Clinical Application. Brain Topogr 34, 430–441 (2021). https://doi.org/10.1007/s10548-021-00852-2
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DOI: https://doi.org/10.1007/s10548-021-00852-2