Simulation of a novel, non‐invasive radiation detector to measure the arterial input function for dynamic positron emission tomography
Notice bibliographique
Résumé
Abstract Background Dynamic positron emission tomography (dPET) is a nuclear medicine imaging technique providing functional images for organs of interest with applications in oncology, cardiology, and drug discovery. This technique requires the acquisition of the time‐course arterial plasma activity concentration, called the arterial input function (AIF), which is conventionally acquired via arterial blood sampling. Purpose The aim of this study was to (A) optimize the geometry for a novel and cost efficient non‐invasive detector called NID designed to measure the AIF for dPET scans through Monte Carlo simulations and (B) develop a clinical data analysis chain to successfully separate the arterial component of a simulated AIF signal from the venous component. Methods The NID was optimized by using an in‐house Geant4‐based software package. The sensitive volume of the NID consists of a band of 10 cm long and 1 mm in diameter scintillating fibers placed over a wrist phantom. The phantom was simulated as a cylinder, 10 cm long and 6.413 cm in diameter comprised of polyethylene with two holes placed through it to simulate the patient's radial artery and vein. This phantom design was chosen to match the wrist phantom used in our previous proof of concept work. Two geometries were simulated with different arrangements of scintillating fibers. The first design used a single layer of 64 fibers. The second used two layers, an inner layer with 29 fibers and an outer layer with 30 fibers. Four positron emitting radioisotopes were simulated:18F,11C,15O, and68Ga with 100 million simulated decay events per run. The total and intrinsic efficiencies of both designs were calculated as well as the full width half maximum (FWHM) of the signal. In addition, contribution by the annihilation photons versus positrons to the signal was investigated. The results obtained from the two simulated detector models were compared. A clinical data analysis chain using an expectation maximization maximum likelihood algorithm was tested. This analysis chain will be used to separate arterial counts from the total signal. Results The second NID design with two layers of scintillating fibers had a higher efficiency for all simulations with a maximum increase of 17% total efficiency for11C simulation. All simulations had a significant annihilation photon contribution. The signal for18F and11C was almost entirely due to photons. The clinical data analysis chain was within 1% of the true value for 434 out of 440 trials. Further experimental studies to validate these simulations will be required. Conclusions The design of the NID was optimized and its efficiency increased through Monte Carlo simulations. A clinical data analysis chain was successfully developed to separate the arterial component of an AIF signal from the venous component. The simulations show that the NID can be used to accurately measure the AIF non‐invasively for dPET scans.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».