S1904
Physics - Dose prediction/calculation, optimisation and applications for photon and electron planning
ESTRO 2026
Besseries 5 , Omar Bohoudi 6 , Riccardo Dal Bello 7 , Stephane Dufreneix 8 , Christopher Kurz 9 , Guillaume Landry 9 , Lisa Milan 10 , Miguel Palacios 6 , Gabriella Pastore 11 , Charlotte Robert 12 , Enrica Seravalli 2 , Natalia Tejedor 13 , Petra Trnkova 14 , Fernanda Villegas 15 , Laure Vieillevigne 16 , Jonathan Wyatt 17 , Poonam Yadav 18 , Lorenzo Placidi 19 , Marco Fusella 20 1 Medical Physics Unit, Mater Olbia Hospital, Olbia, Italy. 2 Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands. 3 Radiation Oncology, Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany. 4 Medical Physics, University of Rennes, Rennes, France. 5 Radiation Oncology, 7Université Bourgogne Europe, Centre Georges-François Leclerc, Dijon, France. 6 Radiation Oncology, Amsterdam UMC, Amsterdam, Netherlands. 7 Radiation Oncology, University Hospital Zurich, Zurich, Switzerland. 8 Medical Physics Unit, Institut de Cancerologie de l’Ouest, Angers, France. 9 Radiation Oncology, LMU University Hospital, Munich, Germany. 10 Medical Physics, Ente Ospedaliero Cantonale, Bellinzona, Switzerland. 11 Radiation Oncology, Ecomedica, Empoli, Italy. 12 Radiation Oncology, Gustave Roussy, Paris, France. 13 Medical Physics Unit, 5Hospital de la Santa Creu i Sant Pau, Barcelona, Spain. 14 Medical Physics, 6Czech Technical University, Prague, Czech Republic. 15 Medical Physics, Karolinska University Hospital, Stockholm, Sweden. 16 Medical Physics, Université Toulose, Toulose, France. 17 Medical Physics, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, United Kingdom. 18 Medical Physics, 21Northwestern University Feinberg School of Medicine, Chicago, USA. 19 Medical Physics Unit, Fondazione Policlinico Agostino Gemelli IRCCS, Rome, Italy. 20 Radiation Oncology, Abano Terme Hospital, Abano Terme, Italy Purpose/Objective: MRI-only radiotherapy (RT) workflows are rapidly reshaping clinical practice by replacing simulation CT with synthetic CT (sCT) derived from MR. However, the lack of standardized commissioning procedures hinders consistent clinical implementation, leaving full responsibility to individual medical physicists.The MESCAL (Multicenter Evaluation of commercial Synthetic-Computed tomography Algorithms) project is a multicenter initiative aimed at establishing a benchmark dataset, clear guidelines, and defined tolerance levels to support safe and reproducible MRI- only RT implementation. Material/Methods: Thirty-two patients (16 brain, 16 pelvis) were retrospectively collected from two centers, including planning CT, simulation MRI, segmentations, and daily CBCTs. Four CE/FDA-cleared sCT solutions were evaluated: MRI Planner (Spectronic), SyngoAI (Siemens), MR-Box (Therapanacea), and MRCAT
(Philips). For each patient, multiple sCTs were generated from the same MRI following vendor- specific protocols. Quantitative analyses focused on image quality, dose accuracy, and positioning verification. Image quality was assessed against CT using mean error (ME), peak-signal-to-noise ratio (PSNR), structural-similarity-index (SSIM), and mean absolute error (MAE) calculated in body, with MAE calculated also within bone region. A VMAT plan was optimized on CT and recalculated on sCTs for dose analysis: DVH indicators (D2%, D50%, D98%) for PTV and one OAR (bladder for pelvis, brainstem for brain) were compared between CT and sCT. Shift estimation accuracy was evaluated by comparing translational and rotational differences when CBCTs were rigidly aligned using sCT versus CT as reference.Plans meeting acceptability criteria (DVH differences<2%, gamma-passing-rate>95% at 2%/2mm as defined in [1]) were selected to define tolerance intervals. These intervals were derived from the distribution of accepted cases, with mandatory thresholds set at 5th- 95th percentiles, and optimal at 10th-90th percentiles Results: Three/four sCTs were generated per case, depending on sequence compatibility (Fig.1 as visual example). Greater inter-software variability was observed in brain than in pelvis, especially in image metrics (e.g., MAE body range:30–70HU for pelvis, 40–130HU for brain). DVH metrics were within 3% in pelvis and 5% in brain, while shift estimation was more accurate in brain (87.5% within 1 mm and 1°) than pelvis (66%). Acceptability criteria were met by 35/45(77.8%) pelvic and 31/39(79.5%) brain cases. Table 1 contains tolerance values derived from these cases. Conclusion: This study proposes a commissioning framework for sCT clinical implementation, offering a benchmark dataset and tolerance levels. Physicists can generate sCTs from the dataset and compare results to Table 1. These benchmarks support standardized adoption, though full validation remains site-specific responsibility and may require additional testing. References: 10.1016/j.phro.2024.100652 Keywords: SyntheticCT evaluation; Commissioning sCT
Digital Poster 2872
Impact of post-optimization dose rate manipulation on plan delivery times Claas Wessels, Martin Sabel Strategic Technologies Product Management, Varian Imaging Laboratory GmbH, Baden, Switzerland
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