Evaluating the sensitivity of Halcyon's automatic transit image acquisition for treatment error detection: A phantom study using static IMRT

Comparison of deep learning models for building two-dimensional non-transit EPID Dosimetry on Varian Halcyon

Background

This study compared the effectiveness of five deep learning models in constructing non-transit dosimetry with an a-Si electronic portal imaging device (EPID) on Varian Halcyon. Deep learning model is increasingly used to support prediction and decision-making in several fields including oncology and radiotherapy.

Materials and methods

Forty-seven unique plans of data obtained from breast cancer patients were calculated using Eclipse treatment planning system (TPS) and extracted from DICOM format as the ground truth. Varian Halcyon was then used to irradiate the a-Si 1200 EPID detector without an attenuator. The EPID and TPS images were augmented and divided randomly into two groups of equal sizes to distinguish the validation and training-test data. Five different deep learning models were then created and validated using a gamma index of 3%/3 mm.

Results

Four models successfully improved the similarity of the EPID images and the TPS-generated planned dose images. Meanwhile, the mismatch of the constituent components and number of parameters could cause the models to produce wrong results. The average gamma pass rates were 90.07 ± 4.96% for A-model, 77.42 ± 7.18% for B-model, 79.60 ± 6.56% for C-model, 80.21 ± 5.88% for D-model, and 80.47 ± 5.98% for E-model.

Conclusion

The deep learning model is proven to run fast and can increase the similarity of EPID images with TPS images to build non-transit dosimetry. However, more cases are needed to validate this model before being used in clinical activities.

Analysis of dose distribution reproducibility based on a fluence map of in vivo transit dose using an electronic portal imaging device

Morphological changes can affect distribution of dose in patients. Determination of the dose distribution changes for each fraction radiotherapy can be done by relativein vivodosimetry (IVD). This study analysed the distribution of doses per fraction based on the fluence map recorded by the electronic portal imaging device (EPID) of the patient's transit dose. This research examined cases involving the cervix, breast, and nasopharynx. Transit dose analysis was performed by calculating the gamma index (GI) with composite and field-by-field methods. The gamma passing rate (GPR) value was assessed for its correlation with the subject's body weight. In the case of the nasopharynx, breast, and cervix, the GPR value decreased as the fraction increased. In the case of the nasopharynx, the correlation between the GPR and fraction radiotherapy showed no difference when using either composite or field-by-field methods. However, in cases involving the cervix and breast, there was a difference in the correlation values between the composite and field-by-field methods, where the subject had a significant correlation (p< 0.05) when it was done using a field-by-field method. In addition, the nasopharynx had the highest number of subjects with significant correlation (p< 0.05) between GPR and body weight, followed by the cervix and breast. In the nasopharynx, breast, and cervix, the reproducibility of the dose distribution decreased. This decreased reproducibility was associated with changes in body weight.

Use ofin vivotransit portal images to detect gross inter-fraction patient geometry changes on an O-ring type linear accelerator for pelvis and head/neck patients

The purpose of this work was to investigate the use of the Varian Portal Dosimetry application in conjunction within vivomegavoltage portal images on a Varian Halcyon O-ring type linear accelerator as anin vivodosimetry constancy (IVDc) tool for pelvis and head/neck patients receiving VMAT treatments. Sensitivity testing was conducted on phantoms with varying thicknesses (0.2 cm-1.0 cm) using static and modulated fields. A cohort of 96 portal dose images across eight patients was then compared with PTV metrics derived from daily CBCT image based treatment plan re-calculations to determine whether the IVDc tool could detect gross inter-fraction anatomical changes. A final cohort of 315 portal dose images across 22 patients was then assessed to demonstrate the application of IVDc tool. The IVDc tool, using 2%/2 mm criteria, detected all phantom thickness changes of 1.0 cm, some phantom thickness changes of 0.5 cm, and no changes of 0.2 cm. For the cohort of 96 results, a IVDc passing criteria of 95% (2%, 2 mm) was able to identify all cases that had PTV metric changes of 2% or more. Using the IVDc tool on the cohort of 315 results, and the IVDc passing criteria of 95%, resulted in 74 IVDc failures. A simple, easy to implement, methodology has been presented that is capable of detecting gross inter-fraction changes in patient geometry on the Varian Halcyon O-ring linac linear accelerator.

Intelligent IoT-based large-scale inverse planning system considering postmodulation factors

The model and algorithm of intensity-modulated radiotherapy (IMRT) are updated increasingly quickly, but the hardware upgrade of primary hospitals often lags behind. The new generation of intelligent precise radiotherapy platforms provides users with intelligent medical consortium services using big data, artificial intelligence and industrial Internet of Things technology. This technology can ensure that under the real-time guidance of a professional medical consortium, primary hospitals can realize rapid large-scale reverse planning design and can more accurately consider many factors of postprocessing. Although large-scale healthcare systems, such as volumetric-modulated arc therapy and other accurate radiotherapy technologies, have developed rapidly, the development of step-and-shoot-mode IMRT technology is still very important for developing countries. For software, in addition to the conformity of the dose distribution, the modulation speed, convenience and stability of the later dose delivery should also be considered in inverse planning. Therefore, this paper analyzes the main problems in conventional IMRT inverse planning, including the smoothing of the fluence map, the selection of the gantry angle and the dose leakage of tongue–groove effects. To address these issues, a novel Intelligent IoT-based large-scale inverse planning strategy with the key factors of the postmodulation is developed, and a detailed flow chart is also provided. The scheme consists of two steps. The first step is to obtain a relatively optimal combination of gantry angles by considering the dose distribution requirements and constraints and the modulation requirements and constraints. The second step is to optimize the intensity map, to smooth the map based on prior knowledge according to the determined angles, and to obtain the final modulation scheme according to the relevant objectives and constraints of the map decomposition (leaf sequencing). In an experiment, we calculate and validate the clinical head and neck case. Because of the special gantry angle selection, the angle combination is optimized from the initial equivalent distribution to adapt to the target area and protect the nontarget area. The value of the objective function varies greatly after the optimization, especially in the target area, and the target value decreases by approximately 10%. On this basis, we smooth the fluence map by a partial differential equation with prior knowledge and a minimization of the total number of monitor units. It is also shown from the objective function value that the target value is essentially unchanged for the target area, while for the nontarget area, the value decreases by 16%, which is very impressive.