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Purpose To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography. Methods and Materials Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets. On-board images from 3 systems were used: megavoltage computed tomography from Tomotherapy and 2 kinds of CBCT from a clinical linear accelerator. Image quality of the system was evaluated. The algorithms’ accuracy was assessed by comparing the DIR-mapped ROIs returned by each center with those of the reference, using the Dice similarity coefficient and mean distance to conformity metrics. Statistical inference on the validation results was carried out to identify the prognostic factors of DIR performance. Results Analyzing 840 DIR-mapped ROIs returned by the centers, it was demonstrated that DVF intensity and image quality were significant prognostic factors of DIR performance. The accuracy of the propagated contours was generally high, and acceptable DIR performance can be obtained with lower-dose CBCT image protocols. Conclusions The performance of the systems proved to be image quality specific, depending on the DVF type and only partially on the platforms. All systems proved to be robust against image artifacts and noise, except the demon-based software.

To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset.90 patients (pts) treated for head-neck cancer (HN) formed a multicenter data-set. 41 HN pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG). Using deformable image registration (DIR) and GPU, patients' conditions were analyzed automatically. Support Vector Machines (SVM) was used for time-series evaluation. "Inadequate" class identified patients that might benefit from replanning. Double-blind evaluation by two radiation oncologists (ROs) was carried out to validate day/week selected for re-planning by the classifier.The cohort was affected by PG mean reduction of 23.7±8.8%. During the first 3weeks, 86.7% cases show PG deformation aligned with predefined tolerance, thus not requiring re-planning. From 4th week, an increased number of pts would potentially benefit from re-planning: a mean of 58% of cases, with an inter-center variability of 8.3%, showed "inadequate" conditions. 11% of cases showed "bias" due to DIR and script failure; 6% showed "warning" output due to potential positioning issues. Comparing re-planning suggested by tool with recommended by ROs, the 4th week seems the most favorable time in 70% cases.SVM and decision-making tool was applied to overcome ART challenges. Pts would benefit from ART and ideal time for re-planning intervention was identified in this retrospective analysis.

Purpose To investigate the accuracy and robustness of commercial algorithms for Deformable Image Registration (DIR) to propagate regions of interest (ROIs), against image quality typical of Cone Beam CT (CBCT) and Megavoltage Cone-beam CT (MVCT) images, using computational phantoms based on real head and neck (HN) patient images. Methods Eleven institutions joined the study with five commercial DIR solutions. Three real patient data-sets from different on board imaging devices (OBI by Varian, XVI by Elekta, and MVCT by Tomotherapy) were used with different image quality. Two specific Deformation Vector Fields (DVF) with realistic level of deformations, were generated with a dedicated software and then applied to each data-set. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Prognostic factors of DIR performances were carried out. Results Based on more than 700 DIR-mapped ROIs the 4-way ANOVA analysis states that DIR algorithm, image quality and organ type are significant predictors of DIR performances. A slightly dependence from applied DVF is detected (p = 0.035) with a post hoc test. Putting together all CI data of all institutions for the first DVF, the mean CI was 0.88 ± 0.05 (1 SD), and the mean MDC is 0.38 ± 0.96 (1 SD); for the second DVF, the mean CI was 0.86 ± 0.11 (1 SD), and the mean MDC is 0.43 ± 1.08 (1 SD). The thyroid, located near the FOV border, was the underperformer organ with large errors (unacceptable). All other organs were mapped with an acceptable accuracy (at voxel size level). One imaging system performs statistically better than the others, and one algorithm produced unsatisfactory results respect all the others. Conclusions This work studies the impact of CBCT and MVCT image quality on DIR performance in a ground truth provided scenario. Clinical issues like Adaptive Radiation Therapy (ART) need accurate and robust DIR software. The reported result highlight that in HN district the image quality and the implemented algorithm are strictly related to DIR performances.

To present our methods and results regarding the modeling of a carbon fiber couch (Varian Exact IGRT) in the RayStation treatment planning system (TPS).Three geometrical-models (GMs) were implemented in the TPS to represent the three different regions of the couch (thick, medium and thin). The materials and densities of each GM component were tuned to maximize the agreement between measured and calculated attenuations. Moreover, a couch computed-tomography (CT) scan was acquired and dosimetrically compared with the GMs. For validation, plan-specific quality assurance (QA) of VMAT plans (TG-119 cases, 5 prostate and 5 HN clinical cases) was performed by comparing measured dose distributions with doses computed with and without including the GMs in the TPS.Couch attenuations up to 4.3% were measured (energy: 6MV). Compared to couch CT, GMs could be modified to optimize the agreement with measurements and reduce dependence on the dose grid resolution. For both couch CT and GM, absolute deviations between measured and calculated attenuations were within 1.0%. When including the GMs in plan-specific QA, global 2%/2mm γ-pass rates showed an average improvement of 4.8% (p-value0.001, max +18.6%). The couch reduced the mean dose to targets by up to 2.4% of the prescribed dose for prostate cases and up to 1.4% for HN cases.RayStation accurately considers the implemented couch GMs replicating measured attenuations within an uncertainty of 1.0%. Materials and densities are proposed for the Varian Exact IGRT couch. The results obtained justify introducing couch GMs in clinical routine.

Introduction The European Federation of Organisations in Medical Physics (EFOMP) has recently released a protocol for quality control (QC) in digital mammography with the intent of harmonizing and simplifying the QC procedures across the European countries. Purpose The aim of this work was to report our experience regarding the implementation of the EFOMP protocol for QC in digital mammography in a multi-hospital/vendor context. Materials and methods QC tests suggested by the EFOMP protocol were merged with mandatory tests by national (Italian) regulations in order to build a local QC protocol. The protocol was applied to one DR and three CR mammography systems developed by different vendors. Time needed for routine QCs was measured. A fast weekly test was implemented to assess the long term reproducibility of relevant quantities (dose and SNR). Results By employing one physicist and one technologist, the time needed for routine QC was: 4.5 h ± 0.5 h (annual tests), 2.5 h ± 0.5 h (semiannual tests), 0.2 h ± 0.1 h (weekly tests). For weekly tests, coefficients of variation of dose and SNR were below 6.0% (four-months period evaluated). By applying the developed protocol, two major and five minor anomalies were identified and corrected. Conclusions The EFOMP protocol could be adapted to local (Italian) regulations with minor modifications. Flexibility and simplification of routine QCs were advantageous in a busy multi-hospital/vendor context. Disclosure None.