Low dose positron emission tomography emulation from decimated high statistics: A clinical validation study
Notice bibliographique
Résumé
PURPOSE: The fundamental nature of positron emission tomography (PET), as an event detection system, provides some flexibility for data handling, including retrospective data manipulation. The reorganization of acquisition data allows the emulation of new scans arising from identical radiotracer spatial distributions, but with different statistical compositions, and is especially useful for evaluating the stability and reproducibility of reconstruction algorithms or when investigating extremely low count conditions. This approach is ubiquitous in the research literature but has only been validated, from the point of view of the noise properties, with numerical simulations and phantom data. We present here the first experiment comparing PET images of the same human subjects generated with two separate injections of radiotracer, using actual low dose (LD) data to validate a randomly decimated emulation from a standard dose scan. A key point of the work is focused on the randoms fractions, which scale differently than the trues at varying activity levels. METHODS: Eleven patients with non-small cell lung cancer were enrolled in the study. Each imaging session consisted of two independent FDG-PET/CT scans: a LD scan followed by a standard dose (SD) scan. Images were first reconstructed, using filtered back-projection (FBP) and OSEM incorporating time-of-flight information and point-spread function modeling (PSFTOF), from the LD and SD datasets comprising all counts from each scanned bed position. The number of true counts was recorded for all LD scans, and independent, count-matched emulations (ELD) were reconstructed from the SD data. Noise distribution within the liver and standardized uptake value reproducibility within a population of contoured, tracer-avid lesion volumes were evaluated across scans and statistics. RESULTS: The randoms fraction estimates were 17.4 ± 1.6% (14.9-19.4) in the LD data and 42 ± 2.3% (37.1-45.5) in the SD data. Eleven lesions were identified and volumes of interest were generated with a 50% threshold isocontour for each lesion, in every image. The distributions of metabolic volumes, means and maxima defined by the contoured volumes-of-interest (VOIs) were similar between the LD and SD sets. A two-tailed, matched t-test was performed on the populations of region statistics for both LD and ELD reconstructions, and the t-statistics were 1.1 (P = 0.267) and -0.22 (P = 0.828) for the background liver VOIs and -0.54 (P = 0.603) and 0.23 (P = 0.821) for the lesion VOIs, for FBP and PSFTOF respectively. In every test, the null hypothesis that the two populations had the same mean could not be rejected at the 5% significance level. CONCLUSIONS: Our results demonstrate that clinical LD PET scans can indeed be accurately emulated by the statistical decimation of standard dose scans, and this was achieved through validation by images generated with unbiased random coincidence estimations.
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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 | 0,000 |
| 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».