Comparison of optimized magnetic resonance sequences for patient‐specific treatment planning in surface brachytherapy
Notice bibliographique
Résumé
BACKGROUND: The clinical standard practice of surface brachytherapy (SB) planning has long been to use computed tomography (CT) imaging to visualize applicators for catheter reconstruction in the treatment planning process. Recent work in SB has suggested that magnetic resonance (MR)-guidance can be used in place of CT-guidance in SB planning to utilize the increased soft tissue contrast for visualization of diseased tissue. This soft tissue visualization can be used to verify the target depth for enhanced coverage of the clinical target volume. Two optimized MR sequences (pointwise encoded time reduction with radial acquisition (PETRA) and volumetric interpolated breath-hold examination (VIBE) obtaining Dixon in-phase (DIP) and Dixon opposed-phase (DOP)) have been shown to detect sufficient signal from the silicone-based applicators to perform accurate catheter reconstruction and produce SB treatment plans. PURPOSE: This study compares three in-house MR series optimized for applicator visualization to determine which is best-suited for SB planning based on tissue contrast and applicator visibility. This study then applies this series to produce MR-only SB treatment plans geometrically and dosimetrically comparable to those produced by CT-only for a phantom and eight patients. METHODS: An anthropomorphic phantom (True Phantom Solutions, Canada) with applicators (Elekta, Netherlands) on the foot and hand and eight patients undergoing SB for Dupuytren's Contracture/Palmar fascial fibromatosis were imaged by two optimized MR sequences: 1) PETRA and 2) VIBE obtaining DIP and DOP images. CT scans were acquired for verification. SB planning was performed in Oncentra Brachy (Elekta, Netherlands) treatment planning software using three MR series and CT. MR-based and CT-based plans were compared for geometric and dosimetric accuracy. Geometric accuracy was determined by registering CT-based to MR-based catheter digitizations and calculating distances between corresponding dwell positions. Patient MR images were compared using signal-to-noise ratios (SNR's) and contrast-to-noise ratios (CNR's) for various regions of interest (ROIs) including bone, fat, muscle, and applicator. The series with the greatest tissue contrast and applicator visualization was used to produce treatment plans. MR-based plans were compared to CT-based plans by point-based dose differences (DD's). The MR-based plan was rigidly registered to the CT-based plans, and the isodose volumes were segmented to V150, V125, V100, V95, V90, V80, and V65 and compared using the Dice similarity coefficient (DSC) and volumetric similarity (VS) metric. RESULTS: The distances between the CT-based and MR-based dwell positions were on average 1 mm. The DOP series displayed superior SNR's for all ROIs compared to PETRA and DIP. CNR's for DOP were equivalent to DIP and superior to PETRA. DD's were all below 5% between MR-based and CT-based plans. DSC's were above 0.9 for all segmentations associated with the phantoms and 0.8 for those associated with the patients. VS was above 0.98 for all segmentations across all subjects. CONCLUSIONS: The geometric accuracy of each MR sequence suggests that each can produce accurate treatment plans. The higher SNR's for DOP suggest DOP's suitability for SB, and DOP was utilized to create plans comparable to CT. This novel approach can result in more robust target coverage and potentially improve patient outcomes.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 | 0,000 |
| 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 ».