Development of lesion and organ negative cast modelling technique for quality assurance and optimization of nuclear medicine images
Notice bibliographique
Résumé
Nuclear medicine imaging allows for a wide variety of data acquisition and image generation methods in the clinical setting. Imaging phantoms are routinely used to evaluate and optimize image quality and quantitative accuracy of features, but few phantoms realistically model the anatomy or heterogeneity of target regions within patient images, such as tumours that are commonly observed in oncology. We developed a negative cast modelling (NCM) technique which enables applications such as non-standard shape tumour phantoms, organ phantoms for radiation dosimetry, and quality control phantoms with small lesions. Tumour templates were derived from segmented PET images of primary mediastinal B-cell lymphoma (PMBCL) patients. Lesion segmentations were saved and 3D-printed. Negatives were developed using silicone-based molding materials, and final models cast using a composition of liquid plastic, pigment, and PET radiotracer. Images of lesions were acquired using the GE DMI PET/CT scanner, and image features were quantified. Mean absolute error (MAE) for tumour volume between the original template and casted models is 13.8%, indicating that the method is reasonably accurate. The high viscosity of the liquid plastic used in the casting process establishes non-uniform tumour models, which is very useful in practice for evaluating image features related to heterogeneity. PET images using the NCM method is determined to be highly realistic by an experienced nuclear medicine physician, due to the non-standard shapes that can be established within the tumours. The NCM method has potential to enable more realistic phantom studies within nuclear medicine imaging. The cost for the lymphoma tumour phantom study is less than $400 USD, making it feasible for large-scale studies. Radioactive substances are used to diagnose and treat disease. Special testing devices called phantoms are used to ensure that the scanners used to detect the radioactive substances work properly. These are usually simple shapes, such as spheres, which do not accurately represent the complexity of real human anatomy. In this study, we developed a technique that uses medical images from patients to create phantoms with more realistic shapes, such as irregular tumors, salivary glands, and very small lesions. These phantoms are made using a low-cost molding and casting process and can be reused. By improving how scanners are tested and optimized, this approach may lead to better diagnosis and treatment for conditions such as cancer. Fedrigo et al. present a technique to create realistic nuclear medicine phantoms from patient-derived anatomical contours using negative cast modelling (NCM). This method enables non-spherical tumor models, organ dosimetry replicas, and small-lesion phantoms, which supports advanced quality assurance and protocol optimization in nuclear medicine.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 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 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».