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Record W7056404772

Exploring the Impact of Anatomical References for Quantifying Spinal Cord Morphometrics Using Magnetic Resonance Imaging

2024· other· fr· W7056404772 on OpenAlexfundno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2024
Typeother
Languagefr
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaInstitut de Valorisation des DonnéesPolytechnique MontréalCanadian Institutes of Health ResearchRéseau en Bio-Imagerie du Quebec
KeywordsSection (typography)Context (archaeology)ExpansivePopulation
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: Contexte: L’évaluation de l’atrophie de la moelle épinière par l’analyse de sa morphologie est pertinente pour comprendre diverses pathologies neurodégénératives, telles que la sclérose en plaques. Un indicateur clé de cette dégénérescence est l’aire de la section transversale de la moelle épinière, mesurable via l’imagerie par résonance magnétique (IRM). Toutefois, cette mesure possède une importante variabilité inter-individuelle, limitant son utilité comme biomarqueur. Habituellement, l’aire de la section transversale est calculée en référence à un niveau vertébral précis, facilitant ainsi les comparaisons temporelles et inter-individuelles. Cependant, l’approximation des niveaux spinaux par les niveaux vertébraux n’est pas précise, augmentant la variabilité des mesures d’aire de section transversale. Bien que d’autres points de référence, tels que le sillon ponto-médullaire, aient été suggérés, ils n’ont jamais été utilisé pour mesurer l’aire de la section transversale. Objectif: Ce projet vise à élaborer une méthode automatique pour le calcul de l’aire de la section transversale de la moelle épinière en se référant au sillon ponto-médullaire. Plus pré-cisément, nous développons un pipeline automatique permettant de déterminer l’aire de sec-tion trasnversale à une distance spécifique du sillon ponto-médullaire, et évaluons l’efficacité de cette nouvelle référence pour minimiser la variabilité inter-individuelle dans un vaste en-semble d’images IRM de sujets sains. Ensuite, nous étudions la contribution de divers facteurs confondants sur la variabilité de l’aire de la section transversale. Finalement, nous mesurons l’impact de cette nouvelle référence sur la variabilité intra-individuelle, avec différentes posi-tions du cou, sur des images IRM structurelles. Méthodes: Tout d’abord, un pipeline automatique a été développé pour calculer l’aire de la section transversale à une distance définie du sillon ponto-médullaire, calculée le long de la ligne centrale de la moelle épinière. Cette méthode a été appliquée sur un large échan-tillon d’images IRM pondérée T1 de la base de données UK Biobank (n = 804). Nous avons comparé l’aire de la section transversale obtenue avec celle calculée en se référant aux niveaux vertébraux C2-C3, et exploré divers facteurs confondants afin d’établir un modèle de normalisation. ABSTRACT: Context: Analysing the shape of the spinal cord is relevant to assess atrophy in various neurodegenerative diseases like multiple sclerosis. Typically, spinal cord cross-sectional area (CSA), derived from magnetic resonance imaging (MRI), is computed as a measure of atrophy and is a relevant biomarker. However, spinal cord CSA suffers from considerable inter-subject variability limiting its use. CSA is typically computed in reference to a specific vertebral level to infer the positions of the spinal levels and allowing comparison through time and individuals. However, the vertebral levels do not provide a precise estimation of the spinal levels, contributing to the observed CSA variability. Other references were proposed to bypass the use of the vertebral levels like the pontomedullary junction (PMJ), but were never used to compute CSA. Objective: The main goal of this project is to develop an automatic pipeline to compute CSA using the PMJ as a reference and to validate its relevance to reduce CSA variability. More precisely, we develop an automatic pipeline to compute CSA using a new reference part of the central nervous system, the PMJ, instead of the vertebral levels. We first investigate the relevance of using the PMJ as a reference for CSA computation to reduce inter-subject variability. To do so, we use a large dataset of healthy controls, and we examine additional factors that contribute to CSA variability. Then, we explore the relevance of PMJ-based CSA on intra-subject variability by varying neck positions. Methods: First, we developed an automatic pipeline to compute spinal cord CSA at a specified distance from the PMJ, measured along the spinal cord centerline using arc-length. The method was applied to T1-weighted MRI images from a subset of the UK Biobank database (n = 804). PMJ-based CSA was compared to CSA computed at the C2-C3 vertebral levels. We explored other confounding factors contributing to CSA variability and developed a normalization model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.306
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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