Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry

BackgroundMRI-based hippocampus volume (HV) is widely used as neurodegeneration marker in Alzheimer's disease.ObjectiveAn easy-to-use and easy-to-interpret method to categorize T1-weighted MR sequences with respect to test-retest stability of hippocampus volumetry based on general image quality metrics (IQM).MethodsThe study included 446 3D T1-weighted MRI scans of one healthy middle-aged man obtained during 32 months in 122 scanning sessions performed with 96 different scanners at 76 different sites. Each scanning session represented a different acquisition sequence of ≥2 back-to-back repeat scans (3.7 ± 0.7 on average). Unilateral HVs were determined with 18 different tools for automatic volumetry. An acquisition sequence was considered "poor" if the z-score of the within-session coefficient-of-variation of the HV estimates from the session, averaged across all volumetry tools and both hemispheres, exceeded one standard deviation. General IQM were computed for each scanning session using the freely available MRI Quality Control Tool. A classification-and-regression tree (CART) was trained to discriminate between good and poor acquisition sequences using the IQM as input.ResultsThe CART selected the left-right width of the acquisition field-of-view and the contrast-to-noise ratio as predictor variables. Overall accuracy of the CART was 79.5%. CART-based classification increased the ratio of good-to-poor acquisition sequences from 3.5 among all sequences to 7.4 among the sequences predicted to be good. This was at the expense of losing 15% of the good sequences.ConclusionsThe IQM-based decision tree model provides useful performance for the differentiation of T1-weighted sequences associated with good versus poor test-retest stability of hippocampus volumetry.

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Publication Details

Journal
Journal of Alzheimer s Disease
Published
2025-09-24
DOI
https://doi.org/10.1177/13872877251380301
Primary Topic
Advanced MRI Techniques and Applications
Type
article
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article

Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry

Robert Dahnke, Hans‐Jürgen Huppertz, Christian Ledig, Aziz Müfit Uluğ et al.
Journal of Alzheimer s Disease
Advanced MRI Techniques and Applications
article

Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry

Robert Dahnke, Hans‐Jürgen Huppertz, Christian Ledig, Aziz Müfit Uluğ, Gabriel A. Devenyi, Fuensanta Bellvís Bataller, Pierrick Bourgeat, Pierrick Coupé, Bénédicte Maréchal, Andreas Lemke, Lothar Spies, Simon Fristed Eskildsen, Roland Opfer, Ralph Buchert, Babak A. Ardekani, Per Suppa, Diana M. Sima, Clara Fischer, Jose Vincente Manjón Herrera
article en

Abstract

BackgroundMRI-based hippocampus volume (HV) is widely used as neurodegeneration marker in Alzheimer's disease.ObjectiveAn easy-to-use and easy-to-interpret method to categorize T1-weighted MR sequences with respect to test-retest stability of hippocampus volumetry based on general image quality metrics (IQM).MethodsThe study included 446 3D T1-weighted MRI scans of one healthy middle-aged man obtained during 32 months in 122 scanning sessions performed with 96 different scanners at 76 different sites. Each scanning session represented a different acquisition sequence of ≥2 back-to-back repeat scans (3.7 ± 0.7 on average). Unilateral HVs were determined with 18 different tools for automatic volumetry. An acquisition sequence was considered "poor" if the z-score of the within-session coefficient-of-variation of the HV estimates from the session, averaged across all volumetry tools and both hemispheres, exceeded one standard deviation. General IQM were computed for each scanning session using the freely available MRI Quality Control Tool. A classification-and-regression tree (CART) was trained to discriminate between good and poor acquisition sequences using the IQM as input.ResultsThe CART selected the left-right width of the acquisition field-of-view and the contrast-to-noise ratio as predictor variables. Overall accuracy of the CART was 79.5%. CART-based classification increased the ratio of good-to-poor acquisition sequences from 3.5 among all sequences to 7.4 among the sequences predicted to be good. This was at the expense of losing 15% of the good sequences.ConclusionsThe IQM-based decision tree model provides useful performance for the differentiation of T1-weighted sequences associated with good versus poor test-retest stability of hippocampus volumetry.

Journal of Alzheimer s DiseaseVol. 108(2)
Nathan Kline Institute for Psychiatric Research (US), Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), Inserm (FR), Universität Hamburg (DE), Aarhus University (DK), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Douglas Mental Health University Institute (CA), Assistance Publique – Hôpitaux de Paris (FR), Olympus (Germany) (DE), University Medical Center Hamburg-Eppendorf (DE), Center For Biomarker Research In Medicine (AT), Jena University Hospital (DE), Australian e-Health Research Centre (AU), CSIRO Health and Biosecurity (AU), Laboratoire Bordelais de Recherche en Informatique (FR), CorTechs Labs (United States) (US), Swiss Epilepsy Center (CH), Siemens (Switzerland) (CH), Icometrix (Belgium) (BE), Centre d'Acquisition et de Traitement des Images (FR), Universitat Politècnica de València (ES), University of Bamberg (DE)
Openalex Percentile: Top 61%
Advanced MRI Techniques and Applications
DOIOpen Access PDFOpenAlex
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