Improved Arterial Input Function For Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Phase and T1 Measurements
Notice bibliographique
Résumé
Perfusion is a bodily function that describes the transport of blood and nutrients to an organ.If characteristics of the blood flow to an organ can be measured, clinically useful quantitative data about the organ can be obtained.This is particularly useful in the diagnosis of tumours as their rapid and poorly-formed capillary network is unique compared to healthy tissue.Tumour perfusion information can be obtained by performing Dynamic Contrast-Enhanced (DCE)-MRI.Using DCE-MRI, blood flow into a tumour from a nearby artery can be computed by measuring the concentration of an injected contrast agent as a function of time.This measurement, known as the arterial input function (AIF), can be computed based on the change of either the intensity or the phase of the MR signal from the blood, due to the presence of a contrast agent.While both methods can be used to acquire the AIF, the change in phase is preferred due to its superior accuracy.Even so, the method conventionally used to obtain the phase-derived AIF has deficiencies that lead to an AIF intensity overestimation.The goal of this thesis was to determine a better method to obtain AIF data for patients with brain tumours by using a combination of MR signal phase and accurate T 1 relaxation measurements.This was done by first characterizing and deriving equations for phase measurement errors and simulating these effects on the AIF using realistic, clinical parameters.T 1 measurement methodology was developed and validated in staticiii water phantoms and applied to a flowing-water phantom system on which phase AIF data were also acquired.The novel AIF method was tested on this system before it was applied clinically to patients diagnosed with high-grade gliomas.The differences between the quantitative perfusion parameters from the new method and previous AIF methods were compared.The novel AIF measurement method presented in this thesis was designed to be less prone to experimental error than current clinical methods.Theoretical predictions, computed simulations, experimental work with MRI test objects, and clinical results all show that the new method for measuring the AIF is significantly superior to procedures currently used clinically.From his help with study development, to spending many long days at the scanner, to our love and passion for GCSC, Dr. Cron will never stop being a friend and mentor to me.Last but not least, I would like to thank those close to me, especially my wife Laura.This thesis could not be possible without your continuous love and support, especially when I needed it the most.I'd also like to thank my siblings and parents who helped to develop my love of science from a young age.They have supported me endlessly and have always been my biggest cheerleaders not only for this work, but for my entire life.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,006 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».