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Lookup NU author(s): Dr Corey Ratcliffe, Professor Peter TaylorORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Structural neuroimaging analyses require “research quality” images, procured with costly MRI acquisitions. Isotropic (3D-T1) images are desirable for quantitative analyses, however, a routine compromise in the clinical setting is to acquire anisotropic (2D-T1) analogues for qualitative visual inspection. ML (machine learning-based) software have shown promise in addressing some of the limitations of 2D-T1 scans in research applications, yet their efficacy in quantitative research is generally poorly understood. Quantitative morphometric analyses have previously identified pathology-related abnormalities of the subcortical structures in idiopathic generalised epilepsy (IGE), which have been overlooked based on visual inspection. As such, IGE biomarkers present a suitable model in which to evaluate the applicability of image preprocessing methods. This study, therefore, explores subcortical structural biomarkers of IGE, first in our “silver standard” 3D-T1 scans, then in 2D-T1 scans that were either untransformed, resampled using a classical interpolation approach, or synthesised with a resolution and contrast agnostic ML model. 2D-T1 and 3D-T1 MRI scans were acquired during the same scanning session for 33 individuals with drug-sensitive IGE (age mean 32.16 ± SD = 14.20, male n = 14) and 42 individuals with drug-resistant IGE (31.76 ± 11.12, 17), all diagnosed at the Walton Centre NHS Foundation Trust Liverpool, alongside 39 age- and sex-matched healthy controls (32.32 ± 8.65, 16). The untransformed 2D-T1 scans were resampled into isotropic images using NiBabel (res-T1), and preprocessed into synthetic isotropic images using SynthSR (syn-T1). For the 3D-T1, 2D-T1, res-T1, and syn-T1 images, the recon-all command from FreeSurfer 8.0.0 was used to create parcellations of 174 anatomical regions (equivalent to the 174 regional parcellations provided as part of the DL+DiReCT pipeline), defined by the aseg and Destrieux atlases, and FSL run_first_all was used to segment subcortical surface shapes. The new ML FreeSurfer pipeline, recon-all-clinical, was also tested in the 2D-T1, 3D-T1, and res-T1 images. As a model comparison for SynthSR, the DL+DiReCT pipeline was used to provide segmentations of the 2D-T1 and res-T1 images, including estimates of regional volume and thickness. Spatial overlap and intraclass correlations between the morphometrics of the eight resulting parcellations were first determined, then subcortical surface shape abnormalities associated with IGE were identified by comparing the FSL run_first_all outputs of patients with controls. When standardised to the metrics derived from the 3D-T1 scans, cortical volume and thickness estimates trended lower for the 2D-T1, res-T1, syn-T1, and DL+DiReCT outputs, whereas subcortical volume estimates were more coherent. Dice coefficients revealed an acceptable spatial similarity between the cortices of the 3D-T1 scans and the other images overall, and was higher in the subcortical structures. Intraclass correlation coefficients were consistently lowest when metrics were computed for model-derived inputs, and estimates of thickness were less similar to the ground truth than those of volume. For the people with epilepsy, the 3D-T1 scans showed significant surface deflations across various subcortical structures when compared with those of healthy controls. Analysis of the 2D-T1 scans enabled the reliable detection of a subset of subcortical abnormalities, whereas analyses of the res-T1 and syn-T1 images were more prone to false-positive results. Resampling and ML image synthesis methods do not currently attenuate partial volume effects resulting from low through plane resolution in anisotropic MRI scans, instead quantitative analyses using 2D-T1 scans should be interpreted with caution, and researchers should consider the potential implications of preprocessing. The recon-all-clinical pipeline is promising, but requires further evaluation, especially when considered as an alternative to the classical pipeline.
Author(s): Ratcliffe C, Taylor PN, de Bezenac C, Das K, Biswas S, Marson A, Keller SS
Publication type: Article
Publication status: Published
Journal: Imaging Neuroscience
Year: 2025
Volume: 3
Online publication date: 21/10/2025
Acceptance date: 10/10/2025
Date deposited: 27/11/2025
ISSN (electronic): 2837-6056
Publisher: Massachusetts Institute of Technology
URL: https://doi.org/10.1162/IMAG.a.997
DOI: 10.1162/IMAG.a.997
Data Access Statement: Data available on request from the authors. Code freely available on GitHub: https://github.com/C-Ratcliffe /221216_Proj-IS
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