An innovative dose rate independent 2D Ce-doped YAG scintillating dosimetry system for time resolved beam monitoring in ultra-high dose rate electron FLASH radiation therapy

FLASH-RT has proven beneficial in preclinical studies. However, the lack of accurate real time 2D dosimetry is a limiting factor. In this work, an innovative solution for 2D real time dosimetry in UHDR electron beams is presented. The in-house developed ImageDosis system consists of a scientific camera, with high temporal resolution, and a coating, containing 12% of Y$_3$Al$_5$O$_{12}$:Ce$^{3+}$ as scintillating material. Reference dosimetry was performed by means of radiochromic film, and a (C$_{38}$H$_{34}$P$_2$)MnBr$_4$ point scintillator was used to validate the pulse discrimination properties of the ImageDosis system. Irradiations were performed in two centers (Antwerp and Orsay), with an ElectronFlash accelerator, with varying number of pulses, pulse length, pulse repetition frequency (PRF) and energy. Also, the temporal resolution and 2D properties were investigated. For doses > 3.5 Gy, the ImageDosis showed a linear dose response up to at least 13 Gy. No dose rate dependence was found for an average dose rate up to 140 Gy/s, a dose per pulse up to 2 Gy and a PRF up to 300 Hz. The ImageDosis system showed capable of measuring the dose of the individual pulses up to a PRF of 250 Hz, but did not detect 3% of the pulses, because these pulses were delivered during the dead time of the camera. The maximal difference in FWHM of the field size between the ImageDosis system and the reference was 3.6%. For a nominal field size of 100 mm and 120 mm, a decreased output was observed on the superior part of the field. The ImageDosis system showed linear dose response and no dose rate nor energy dependence. It showed capable of discriminating and measuring the dose of individual pulses and promising 2D characteristics that need further optimization

Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm

Objective. The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially leads to significant errors with impact on the overall treatment quality. Additionally, when the time needed to correct auto-segmentations approaches the time to delineate target and organs at risk from scratch, the usability of the DL model can be questioned. Therefore, an automated quality assurance framework was developed with the aim to detect in advance aberrant auto-segmentations. Approach. Five organs (prostate, bladder, anorectum, femoral head left and right) were auto-delineated on CT acquisitions for 48 prostate patients by an in-house trained primary DL model. An experienced radiation oncologist assessed the correctness of the model output and categorised the auto-segmentations into two classes whether minor or major adaptations were needed. Subsequently, an independent, secondary DL model was implemented to delineate the same structures as the primary model. Quantitative comparison metrics were calculated using both models' segmentations and used as input features for a machine learning classification model to predict the output quality of the primary model. Main results. For every organ, the approach of independent validation by the secondary model was able to detect primary auto-segmentations that needed major adaptation with high sensitivity (recall = 1) based on the calculated quantitative metrics. The surface DSC and APL were found to be the most indicated parameters in comparison to standard quantitative metrics for the time needed to adapt auto-segmentations. Significance. This proposed method includes a proof of concept for the use of an independent DL segmentation model in combination with a ML classifier to improve time saving during QA of auto-segmentations. The integration of such system into current automatic segmentation pipelines can increase the efficiency of the radiotherapy contouring workflow

Development of an ultra-thin parallel plate ionization chamber for dosimetry in FLASH radiotherapy

Conventional air ionization chambers (ICs) exhibit ion recombination correction factors that deviate substantially from unity when irradiated with dose per pulse magnitudes higher than those used in conventional radiotherapy. This fact makes these devices unsuitable for the dosimetric characterization of beams in ultra-high dose per pulse as used for FLASH radiotherapyParticipating States; Horizon 2020; European Metrology Programme for Innovation and Research, Grant/Award Number: 18HLT04UHD PulseS

Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy

Abstract: Background and purpose: Treatment plans in radiotherapy are subject to measurement-based pre-treatment verifications. In this study, plan complexity metrics (PCMs) were calculated per beam and used as input features to develop a predictive model. The aim of this study was to determine the robustness against differences in machine type and institutional-specific quality assurance (QA). Material and methods: A number of 567 beams were collected, where 477 passed and 90 failed the pre-treatment QA. Treatment plans of different anatomical regions were included. One type of linear accelerator was represented. For all beams, 16 PCMs were calculated. A random forest classifier was trained to distinct between acceptable and non-acceptable beams. The model was validated on other datasets to investigate its robustness. Firstly, plans for another machine type from the same institution were evaluated. Secondly, an inter-institutional validation was conducted on three datasets from different centres with their associated QA.Results: Intra-institutionally, the PCMs beam modulation, mean MLC gap, Q1 gap, and Modulation Complexity Score were the most informative to detect failing beams. Eighty-tree percent of the failed beams (15/18) were detected correctly. The model could not detect over-modulated beams of another machine type. Interinstitutionally, the model performance reached higher accuracy for centres with comparable equipment both for treatment and QA as the local institute.Conclusions: The study demonstrates that the robustness decreases when major differences appear in the QA platform or in planning strategies, but that it is feasible to extrapolate institutional-specific trained models between centres with similar clinical practice. Predictive models should be developed for each machine type