Evaluation of radiation detectors for the determination of field output factors in Leksell Gamma Knife dosimetry using 3D printed phantom inserts.

The Leksell Gamma Knife® Perfexion™ and Icon™ have a unique geometry, containing 192 60Co sources with collimation for field sizes of 4 mm, 8 mm, and 16 mm. 4 mm and 8 mm collimated fields lack lateral charged particle equilibrium, so accurate field output factors are essential. This study performs field output factor measurements for the microDiamond, microSilicon, and RAZOR™ Nano detectors. 3D printed inserts for the spherical Solid Water® Phantom were fabricated for microDiamond detector, the microSilicon unshielded diode and the RAZOR™ Nano micro-ionisation chamber. Detectors were moved iteratively to identify the peak detector signal for each collimator, representing the effective point of measurement of the chamber. In addition, field output correction factors were calculated for each detector relative to vendor supplied Monte Carlo simulated field output factors and field output factors measured with a W2 scintillator. All field output factors where within 1.1 % for the 4 mm collimator and within 2.3 % for the 8 mm collimator. The 3D printed phantom inserts were suitable for routine measurements if the user identifies the effective point of measurement, and ensures a reproducible setup by marking the rotational alignment of the cylindrical print. Measurements with the microDiamond and microSilicon can be performed faster compared to the RAZOR™ Nano due to differences in the signal to noise ratio. All detectors are suitable for field output factor measurements for the Leksell Gamma Knife® Perfexion™ and Icon™.

Assessment of Optimizers and their Performance in Autosegmenting Lung Tumors.

Purpose: Optimizers are widely utilized across various domains to enhance desired outcomes by either maximizing or minimizing objective functions. In the context of deep learning, they help to minimize the loss function and improve model's performance. This study aims to evaluate the accuracy of different optimizers employed for autosegmentation of non-small cell lung cancer (NSCLC) target volumes on thoracic computed tomography images utilized in oncology. Materials and Methods: The study utilized 112 patients, comprising 92 patients from "The Cancer Imaging Archive" (TCIA) and 20 of our local clinical patients, to evaluate the efficacy of various optimizers. The gross tumor volume was selected as the foreground mask for training and testing the models. Of the 92 TCIA patients, 57 were used for training and validation, and the remaining 35 for testing using nnU-Net. The performance of the final model was further evaluated on the 20 local clinical patient datasets. Six different optimizers, namely AdaDelta, AdaGrad, Adam, NAdam, RMSprop, and stochastic gradient descent (SGD), were investigated. To assess the agreement between the predicted volume and the ground truth, several metrics including Dice similarity coefficient (DSC), Jaccard index, sensitivity, precision, Hausdorff distance (HD), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were utilized. Results: The DSC values for AdaDelta, AdaGrad, Adam, NAdam, RMSprop, and SGD were 0.75, 0.84, 0.85, 0.84, 0.83, and 0.81, respectively, for the TCIA test data. However, when the model trained on TCIA datasets was applied to the clinical datasets, the DSC, HD, HD95, and ASSD metrics showed a statistically significant decrease in performance compared to the TCIA test datasets, indicating the presence of image and/or mask heterogeneity between the data sources. Conclusion: The choice of optimizer in deep learning is a critical factor that can significantly impact the performance of autosegmentation models. However, it is worth noting that the behavior of optimizers may vary when applied to new clinical datasets, which can lead to changes in models' performance. Therefore, selecting the appropriate optimizer for a specific task is essential to ensure optimal performance and generalizability of the model to different datasets.

Design and Development of a Low-cost Integrated Dosimeter for External Beam Dosimetry in Radiation Oncology.

Radiation dosimeters play a crucial role in radiation oncology by accurately measuring radiation dose, ensuring precise and safe radiation therapy. This study presents the design and development of a low-cost printed circuit board (PCB) dosimeter and an integrated electrometer with sensitivity optimized for dose rates intended for use in megavoltage radiation therapy. The PCB dosimeter was designed in KiCad, and it uses a low-cost S5MC-13F general-purpose 1 kV 5A power diode as a radiation detector. The dosimeter is calibrated against a known dose derived from an ionization chamber and tested for dose linearity, dose rate dependence, field size dependence, and detector orientation dependence. The observed average dose differences between the delivered and measured doses for most measurements were found to be < 1.1%; the dose rate linearity between 100 MU/min and 1400 MU/min was found to be within 1.3%. This low-cost architecture could successfully be adapted further for a scalable, cost-effective dosimetry solution through firmware or circuit design.

Dose assessment for daily cone-beam CT in lung radiotherapy patients and its combination with treatment planning.

With the increased use of X-ray imaging for patient alignment in external beam radiation therapy, particularly with cone-beam computed tomography (CBCT), the additional dose received by patients has become of greater consideration. In this study, we analysed the radiation dose from CBCT for clinical lung radiotherapy and assessed its relative contribution when combined with radiation treatment planning for a variety of lung radiotherapy techniques. The Monte Carlo simulation program ImpactMC was used to calculate the 3D dose delivered by a Varian TrueBeam linear accelerator to patients undergoing thorax CBCT imaging. The concomitant dose was calculated by simulating the daily CBCT irradiation of ten lung cancer patients. Each case was planned with a total dose of 50-60 Gy to the target lesion in 25-30 fractions using the 3DCRT or IMRT plan and retrospectively planned using VMAT. For each clinical case, the calculated CBCT dose was summed with the planned dose, and the dose to lungs, heart, and spinal cord were analysed according to conventional dose conformity metrics. Our results indicate greater variations in dose to the heart, lungs, and spinal cord based on planning technique, (3DCRT, IMRT, VMAT) than from the inclusion of daily cone-beam imaging doses over 25-30 fractions. The average doses from CBCT imaging per fraction to the lungs, heart and spinal cord were 0.52 ± 0.10, 0.49 ± 0.15 and 0.39 ± 0.08 cGy, respectively. Lung dose variations were related to the patient's size and body composition. Over a treatment course, this may result in an additional mean absorbed dose of 0.15-0.2 Gy. For lung V5, the imaging dose resulted in an average increase of ~ 0.6% of the total volume receiving 5 Gy. The increase in V20 was more dependent on the planning technique, with 3DCRT increasing by 0.11 ± 0.09% with imaging and IMRT and VMAT increasing by 0.17 ± 0.05% and 0.2 ± 0.06%, respectively. In this study, we assessed the concomitant dose for daily CBCT lung cancer patients undergoing radiotherapy. The additional radiation dose to the normal lungs from daily CBCT was found to range from 0.15 to 0.2 Gy when the patient was treated with 25-30 fractions. Consideration of potential variation in relative biological effectiveness between kilovoltage imaging and megavoltage treatment dose was outside the scope of this study. Regardless of this, our results show that the assessment of imaging dose can be incorporated into the treatment planning process and the relative effect on overall dose distribution was small compared to the difference among planning techniques.

Comparison and Validation of Multiple Detectors against Monte Carlo Simulation for the Use of Small-Field Dosimetry.

Aim: The aim of this study was to compare the Exradin W2 scintillator, PTW microDiamond, IBA Razor Nano, and IBA Razor chamber detectors for small-field dose measurements and validate the measured data against the EGSnrc user code and observe the variation between daisy-chained and direct measurement methods for the above detectors. Materials and Methods: The W2 scintillator, microDiamond, Razor Nano, and Razor chamber detectors were used to measure the in-plane and cross-plane profiles and the output factors (OFs) at 10 cm depth, and 90 source-to-surface distance for 6MV X-rays (Elekta Versa HD). The field sizes ranged from 0.5 cm × 0.5 cm to 5 cm × 5 cm. The BEAMnrc/DOSXYZnrc user codes (EGSnrc) were used to simulate the reference profiles. Gamma analysis was performed to compare the measured and simulated dose distributions. Results: The OFs measured with the W2 scintillator, microDiamond, Razor Nano chamber, Razor chamber, and the calculated Monte Carlo (MC) showed agreement to within 1% for the 3 cm × 3 cm field size. The uncertainty in the MC simulation was observed to be 0.4%. The percent difference in OFs measured using daisy-chained and direct measurement methods was within 0.15%, 0.4%, 1.4%, and 2.4% for microDiamond, W2 scintillator, Nano, and Razor chamber detectors, respectively. Conclusion: The lateral beam profiles and OFs of W2 scintillator, microDiamond, Razor Nano, and Razor chambers exhibit good agreement with the MC simulation within the nominal field sizes. Our results demonstrate that we can achieve considerable time-saving by directly measuring small-field OFs without daisy-chained methods using microDiamond and W2 scintillator. In terms of ease of use, sensitivity, reproducibility, and from a practical standpoint, we recommend microDiamond for small-field dosimetry.

A feasibility study on the development and use of a deep learning model to automate real-time monitoring of tumor position and assessment of interfraction fiducial marker migration in prostate radiotherapy patients.

Purpose. The aim of this study was to assess the feasibility of the development and training of a deep learning object detection model for automating the assessment of fiducial marker migration and tracking of the prostate in radiotherapy patients.Methods and Materials. A fiducial marker detection model was trained on the YOLO v2 detection framework using approximately 20,000 pelvis kV projection images with fiducial markers labelled. The ability of the trained model to detect marker positions was validated by tracking the motion of markers in a respiratory phantom and comparing detection data with the expected displacement from a reference position. Marker migration was then assessed in 14 prostate radiotherapy patients using the detector for comparison with previously conducted studies. This was done by determining variations in intermarker distance between the first and subsequent fractions in each patient.Results. On completion of training, a detection model was developed that operated at a 96% detection efficacy and with a root mean square error of 0.3 pixels. By determining the displacement from a reference position in a respiratory phantom, experimentally and with the detector it was found that the detector was able to compute displacements with a mean accuracy of 97.8% when compared to the actual values. Interfraction marker migration was measured in 14 patients and the average and maximum±standard deviation marker migration were found to be2.0±0.9mmand2.3±0.9mm,respectively.Conclusion. This study demonstrates the benefits of pairing deep learning object detection, and image-guided radiotherapy and how a workflow to automate the assessment of organ motion and seed migration during prostate radiotherapy can be developed. The high detection efficacy and low error make evident the advantages of using a pre-trained model to automate the assessment of the target volume positional variation and the migration of fiducial markers between fractions.

Tungsten carbide and LMPA electron cutouts: comparison and validation using Monte Carlo modelling and measurement of dose.

Electron applicator cutouts for radiation therapy electron beam shaping are typically cast from Low Melting Point Alloys (LMPA), such as cerrobend. In this work, we describe the Monte Carlo modelling of novel 3D printed cutouts based on tungsten carbide powder and the resulting dose profiles subject to Elekta Agility electron beams. Cerrobend cutouts were also modelled using the Monte Carlo code EGSnrc. Cerrobend and tungsten carbide cutouts were found to have the same dose profiles within model variance. Computed profiles and percentage depth dose (PDDs) curves of the Elekta Agility accelerator model using standard cutouts in water were found to agree with water tank measurements using gamma criteria of 2%/2mm. The Monte Carlo computed dose profiles of the tungsten carbide cutouts in a polystyrene phantom were also found to agree with liquid-filled ionization chamber array measurements using gamma criteria of 2%/2mm. We conclude that the tungsten carbide cutouts are clinically equivalent to LMPA cutouts.