Evidence map›Paper›PMID 40990841›Full record

ArticleJournal of Alzheimer's disease : JAD2025

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.

Ralph Buchert, Per Suppa, Babak A Ardekani, Fuensanta Bellvís Bataller, Pierrick Bourgeat, Pierrick Coupé, Robert Dahnke, Gabriel A Devenyi, Simon Fristed Eskildsen, Clara Fischer and 9 more

Abstract read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

19 authors.

Ralph BuchertDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.ORCID 0000-0002-0945-0724
Per SuppaOlympus Winter & Ibe GmbH, Hamburg, Germany.
Babak A ArdekaniCenter for Brain Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
Fuensanta Bellvís BatallerQuantitative Imaging Biomarkers in Medicine (Quibim), Valencia, Spain.
Pierrick BourgeatAustralian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Australia.
Pierrick CoupéLaBRI - UMR 5800, University of Bordeaux, Talence, France.
Robert DahnkeStructural Brain Mapping Group, Department of Neurology, Jena University Hospital, Jena, Germany.
Gabriel A DevenyiDepartment of Psychiatry, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montréal, Quebec, Canada.ORCID 0000-0002-7766-1187
Simon Fristed EskildsenDepartment of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark.
Clara FischerCATI, US52-UAR2031, CEA, ICM, SU, CNRS, INSERM, APHP, Ile de France, Paris, France.
Jose Vincente Manjón HerreraApplied Physics Department, MIA Lab, ITACA Institute, Universidad Politécnica de Valencia, Valencia, Spain.
Christian LedigxAILab Bamberg, University of Bamberg, Bamberg, Germany.
Andreas LemkeMediaire GmbH, Berlin, Germany.
Bénédicte MaréchalAdvanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.
Roland OpferJung Diagnostics GmbH, Hamburg, Germany.
Diana M SimaIcometrix, Leuven, Belgium.
Lothar SpiesJung Diagnostics GmbH, Hamburg, Germany.
Aziz M UlugCortechs Labs, Inc., San Diego, CA, USA.
Hans-Jürgen HuppertzSwiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

HippocampusImaging, Three-DimensionalMagnetic Resonance ImagingAdultHumansImage Processing, Computer-AssistedMaleMiddle AgedOrgan SizeQuality ControlReproducibility of ResultsAlzheimer's diseaseclassification-and-regression treecontrast-to-noise ratiofield-of-viewhippocampusimage qualityoutlierstructural MRIvolumetry

Identifiers

PMID40990841
PMCPMC12605328

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.