Investigating the Roles of Reconstruction and the Self-Calibration Factor for 90Y SPECT/CT Image-based Dosimetry
Notice bibliographique
Résumé
1017 Objectives: The local deposition, dose-point kernel convolution, and Monte Carlo methods are currently the three image-based dosimetry techniques for Yttrium-90 (90Y) radioembolization. Image-based, these dosimetry techniques are dependent on the method of image reconstructions performed. In regards to dosimetry, the different effects between subset and iteration numbers have not been investigated. 90Y SPECT/CT dosimetry further utilizes a self-calibration factor to equate counts per voxel to activity per voxel based on the rationale that all the activity is confined within any specified ROI. Consequently, we investigate the role of these factors on image-based dosimetry through the local deposition method. Methods: In this presented work, a single patient’s 90Y SPECT/CT image was retrospectively used to investigate the impact of different reconstruction parameters and self-calibration factors. This patient was treated with of 90Y glass microspheres. In this work, we performed a total of seven OSEM reconstructions with HybridRecon (v1.0 Hermes) with a varying number of subsets and iterations to an equivalent of 150 MLEM iterations (e.g. 5sub30iter). All reconstructions were either performed with 8.5mm Gaussian post-filtering or reconstructed with OSEM MAP with median root prior (MRP) and a Bayesian weight of .3. A recommend clinical reconstruction was further performed with 5 iterations, 15 iterations, and a .4 cm-1 Butteworth filter (Clinical). These reconstructions were corrected for collimator scatter, scatter, had resolution recovery, and attenuation corrections based on CT. Contours of the liver, lung, and body were drawn using MIM Maestro v6.6. All processing of data and the local deposition method was developed in python code. Voxel densities were calculated using a scanner specific linear lookup table based on electron density phantom scans. The local deposition used the resulting voxel densities from the CT and interpolated SPECT voxels to perform absorbed dose calculations. For dose calculations, the 90Y half-life was set at 64.24 hours, used an average energy of .935 MeV per disintegration, and set the self-calibration factor dependent on the a specific contoured ROI (FOV, Body, and LiverLung). Due to an incomplete lung volume shown in the CT, an adjusted self-calibration method was additionally introduced that combined technetium macro-aggregated albumin based lung-shunting fraction with the same LiverLung contours. This method (TcMAA) allowed the liver and lung activities to be adjusted by a lung-shunt fraction resulting in two self-calibration factors specific to the liver and lung. Results: The range for liver and lung mean absorbed doses was 9.92 to 24.93 Gy and 2.84 to 8.08 Gy, respectively. These doses were dependent upon the combination of the self-calibration factor and type of reconstruction algorithm. Figure 1’s boxplots illustrate that the self-calibration factor had a bigger effect in determining the change in mean absorbed dose calculations, than reconstructions. Conclusions: The provided results illustrate that mean absorbed dose calculations to the target and healthy tissues can vary depending on the self-calibration factor and type of the reconstruction algorithms used. Consensus regarding which parameters to use is important for more accurate dosimetry.
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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 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 | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 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 ».