Experimental demonstration of real‐time cardiac physiology‐based radiotherapy gating for improved cardiac radioablation on an MR‐linac
Notice bibliographique
Résumé
Abstract Background Cardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high‐dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire images in a predefined cardiac phase, thus mitigating cardiac motion during image acquisition. Purpose We demonstrate real‐time cardiac physiology‐based radiotherapy beam gating within a preset cardiac phase on an MR‐linac. Methods MR images were acquired in healthy volunteers (n = 5, mean age = 29.6 years, mean heart‐rate (HR) = 56.2 bpm) on the 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden) after obtaining written informed consent. The images were acquired using a single‐slice balance steady‐state free precession (bSSFP) sequence in the coronal or sagittal plane (TR/TE = 3/1.48 ms, flip angle = 48, SENSE = 1.5, , voxel size = , partial Fourier factor = 0.65, frame rate = 13.3 Hz). In parallel, a 4‐lead ECG‐signal was acquired using MR‐compatible equipment. The feasibility of ECG‐based beam gating was demonstrated with a prototype gating workflow using a Quasar MRI4D motion phantom (IBA Quasar, London, ON, Canada), which was deployed in the bore of the MR‐linac. Two volunteer‐derived combined ECG‐motion traces (n = 2, mean age = 26 years, mean HR = 57.4 bpm, peak‐to‐peak amplitude = 14.7 mm) were programmed into the phantom to mimic dose delivery on a cardiac target in breath‐hold. Clinical ECG‐equipment was connected to the phantom for ECG‐voltage‐streaming in real‐time using research software. Treatment beam gating was performed in the quiescent phase (end‐diastole). System latencies were compensated by delay time correction. A previously developed MRI‐based gating workflow was used as a benchmark in this study. A 15‐beam intensity‐modulated radiotherapy (IMRT) plan ( Gy) was delivered for different motion scenarios onto radiochromic films. Next, cardiac motion was then estimated at the basal anterolateral myocardial wall via normalized cross‐correlation‐based template matching. The estimated motion signal was temporally aligned with the ECG‐signal, which were then used for position‐ and ECG‐based gating simulations in the cranial–caudal (CC), anterior–posterior (AP), and right–left (RL) directions. The effect of gating was investigated by analyzing the differences in residual motion at 30, 50, and 70% treatment beam duty cycles. Results ECG‐based (MRI‐based) beam gating was performed with effective duty cycles of 60.5% (68.8%) and 47.7% (50.4%) with residual motion reductions of 62.5% (44.7%) and 43.9% (59.3%). Local gamma analyses (1%/1 mm) returned pass rates of 97.6% (94.1%) and 90.5% (98.3%) for gated scenarios, which exceed the pass rates of 70.3% and 82.0% for nongated scenarios, respectively. In average, the gating simulations returned maximum residual motion reductions of 88%, 74%, and 81% at 30%, 50%, and 70% duty cycles, respectively, in favor of MRI‐based gating. Conclusions Real‐time ECG‐based beam gating is a feasible alternative to MRI‐based gating, resulting in improved dose delivery in terms of high rates, decreased dose deposition outside the PTV and residual motion reduction, while by‐passing cardiac MRI challenges.
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 | 0,000 | 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 ».