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Enregistrement W7038747201

Investigation of Quantitative Magnetization Transfer Magnetic Resonance Imaging as a Non-Invasive Technique to Assess the Biochemical, Mechanical, and Histologic Properties of Healthy and Osteoarthritic Meniscus and Cartilage

2021· dissertation· en· W7038747201 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2021
Typedissertation
Langueen
DomaineMedicine
ThématiqueOsteoarthritis Treatment and Mechanisms
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOsteoarthritisMagnetic resonance imagingCartilageArticular cartilageMagnetization transferMeniscusPopulationCadaverJoint diseaseKnee Joint
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Osteoarthritis is a degenerative disease affecting entire joints and leading to pain, stiffness, and loss of mobility. It affects around 13% of the Canadian population and commonly presents in the knee. Traditionally, osteoarthritis has been visualized using radiography because it is the most accessible imaging method and can detect bone alterations, but this method is unable to show changes to the articular cartilage and meniscus, which have been shown to play an important role in the disease process. Quantitative magnetic resonance imaging (qMRI) is able to provide images of the soft tissue within the knee joint as well as numerical values representative of the state of the tissue health. One particular qMRI technique is quantitative magnetization transfer (qMT), and it allows for the determination of the properties of the bound pool within tissues (macromolecules such as proteoglycan and collagen) that has resonance too short to be captured with conventional MRI. Because qMT probes the properties of the hydrogen bound to macromolecules, it is expected to be more sensitive to the changes in composition of a tissue associated with osteoarthritis. The primary objective of this research is to establish a relationship between qMT parameters (f, k, T2b relaxation time, T2f relaxation time, and T1f relaxation time) and the biochemical, histological, and mechanical properties of human articular cartilage and meniscus, and a secondary objective is to compare in vivo to ex situ qMT parameters. Two separate studies were conducted using differing populations in order to accomplish these objectives. The first study assessed six human cadaver knees with no history of injury or illness in order to validate the methods and gain a baseline of values to be expected in a healthy population. Intact cadaver knees were imaged using qMT MRI techniques and qMT parameters extracted. Subsequent to imaging, core samples were taken from each meniscus and digested and assayed to determine the liquid, collagen, and proteoglycan contents. Menisci were dissected into pieces for histology and scored using an established histological scoring system customized to the meniscus. Pearson product moment and Spearman’s rho correlation coefficients were calculated for the biochemistry and histology results respectively compared to the qMT parameters to determine if any of the imaging metrics were predictive of the biochemical content or histological score. Results of this study showed several significant correlations between the qMT parameters and tissue properties. Some of these key findings included correlations in the collective samples where increasing liquid content was associated with decreasing bound pool fraction (r=-0.248, p<0.5); increasing collagen per dry mass showed increasing T1f (r=0.413, p<0.01) and T2f (r=0.510, p<0.01); and an increase in total histology score was related to a decrease in T1f (ρ=-0.232, p<0.05), T2f (ρ=-0.277, p<0.01), and T2b (ρ=-0.207, p<0.05). In the medial side samples, key correlations were observed between increasing collagen per dry mass and increasing T1f (r=0.477, p<0.01), T2f (r=0.585, p<0.01), and T2b (r=0.415, p<0.05); and increasing histology score and decreasing T1f (ρ=-0.232, p<0.05), T2f (ρ=-0.277, p<0.01), and T2b (ρ=-0.207, p<0.05). In the lateral side samples, key correlations were between increasing liquid content and decreasing f (r=-0.380, p<0.05) and increasing sulfated glycosaminoglycan (sGAG) per wet mass was associated with increases in f (r=0.391, p<0.05) and kf (r=0.404, p<0.05). The second study focused on an end-stage osteoarthritis population by assessing total knee arthroplasty patients. The aim of this study was to explore the relationships between qMT parameters and tissue properties in damaged tissue. Two patients were scanned using the qMT MRI protocol prior to their surgery, and the excised tissues were scanned post-operatively using the same sequence. From these samples, seven separate articular cartilage and meniscus surfaces (both medial and lateral) were assessed. After imaging, the surfaces underwent mechanical indentation testing and the instantaneous modulus, elastic fit mean squared error, and tissue thicknesses were determined. Core samples were then removed from the surfaces for biochemical and histological analysis. Biochemistry protocols were the same as utilized in the cadaver study, and histology preparation was the same as well with different scoring methods used depending on the tissue type (articular cartilage versus meniscus). Pearson and Spearman correlation coefficients were once again determined in order to assess correlations between the qMT parameters and the tissue properties. A Wilcoxon signed rank test was performed to assess differences between in vivo and ex situ qMT results. The key results of this study showed significant correlations in the in vivo cartilage between increasing instantaneous modulus and decreasing T1f (r=-0.221, p<0.05) and T2f (r=-0.233, p<0.05) in the lateral side samples; increasing liquid content and T1f (r=0.836, p<0.05) in the lateral samples; and histology score and f in the combined samples (ρ=0.670, p<0.05) and medial samples (ρ=1.000, p<0.01). In the ex situ cartilage, significant correlations were found between increasing histology score and decreasing T2b (ρ=-0.896, p<0.01) in the lateral samples. In the lateral menisci samples in vivo, key correlations were found between increasing liquid content and decreasing kf (r=-0.890, p<0.05); increasing sGAG/dry mass and increasing T2b (r=0.869, p<0.05); and increasing collagen/wet mass and increasing kf (r=0.820, p<0.05). In the lateral ex situ menisci, a negative correlation was observed between instantaneous modulus and T2f (r=-0.563, p<0.05). In the global surface analysis (combining all cartilage and meniscus surfaces), key correlations were between increasing liquid content and increasing T1f (r=0.926, p<0.01) and T2f (r=0.864, p<0.05); increasing sGAG/dry mass and increasing T1f (r=826 p<0.05) and T2f (r=0.964, p<0.01); increasing collagen/dry mass and decreasing T1f (r=-0.780, p<0.05); and increasing histology score and increasing T2f (ρ=0.893, p<0.01). Significant decreases in T1obs, T1f, T2f and T2b were also found from in vivo to ex situ scanning environments. The findings in the correlation analysis of this project show the potential of qMT MRI imaging as a valuable modality for determining the structure, function, and composition of osteoarthritic articular cartilage and meniscus. It has been shown that ex situ qMT parameters are not the same as in vivo but steps have been made in a direction towards quantifying the relationships between the differing environments. Possible uses of this technique lie in early diagnosis of OA, monitoring of disease progression, and evaluation of potential treatments.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,199
Écart entre enseignants0,185 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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