Feasibility of a GATE Monte Carlo platform in a clinical pretreatment QA system for VMAT treatment plans using TrueBeam with an HD120 multileaf collimator.

PURPOSE: To evaluate the quality of patient-specific complicated treatment plans, including commercialized treatment planning systems (TPS) and commissioned beam data, we developed a process of quality assurance (QA) using a Monte Carlo (MC) platform. Specifically, we constructed an interface system that automatically converts treatment plan and dose matrix data in digital imaging and communications in medicine to an MC dose-calculation engine. The clinical feasibility of the system was evaluated. MATERIALS AND METHODS: A dose-calculation engine based on GATE v8.1 was embedded in our QA system and in a parallel computing system to significantly reduce the computation time. The QA system automatically converts parameters in volumetric-modulated arc therapy (VMAT) plans to files for dose calculation using GATE. The system then calculates dose maps. Energies of 6 MV, 10 MV, 6 MV flattening filter free (FFF), and 10 MV FFF from a TrueBeam with HD120 were modeled and commissioned. To evaluate the beam models, percentage depth dose (PDD) values, MC calculation profiles, and measured beam data were compared at various depths (Dmax , 5 cm, 10 cm, and 20 cm), field sizes, and energies. To evaluate the feasibility of the QA system for clinical use, doses measured for clinical VMAT plans using films were compared to dose maps calculated using our MC-based QA system. RESULTS: A LINAC QA system was analyzed by PDD and profile according to the secondary collimator and multileaf collimator (MLC). Values for MC calculations and TPS beam data obtained using CC13 ion chamber (IBA Dosimetry, Germany) were consistent within 1.0%. Clinical validation using a gamma index was performed for VMAT treatment plans using a solid water phantom and arbitrary patient data. The gamma evaluation results (with criteria of 3%/3 mm) were 98.1%, 99.1%, 99.2%, and 97.1% for energies of 6 MV, 10 MV, 6 MV FFF, and 10 MV FFF, respectively. CONCLUSIONS: We constructed an MC-based QA system for evaluating patient treatment plans and evaluated its feasibility in clinical practice. We observed robust agreement between dose calculations from our QA system and measurements for VMAT plans. Our QA system could be useful in other clinical settings, such as small-field SRS procedures or analyses of secondary cancer risk, for which dose calculations using TPS are difficult to verify.

Secondary cancer-incidence risk estimates for external radiotherapy and high-dose-rate brachytherapy in cervical cancer: phantom study.

This study was designed to estimate radiation-induced secondary cancer risks from high-dose-rate (HDR) brachytherapy and external radiotherapy for patients with cervical cancer based on measurements of doses absorbed by various organs. Organ doses from HDR brachytherapy and external radiotherapy were measured using glass rod dosimeters. Doses to out-of-field organs were measured at various loca-tions inside an anthropomorphic phantom. Brachytherapy-associated organ doses were measured using a specialized phantom that enabled applicator insertion, with the pelvis portion of the existing anthropomorphic phantom replaced by this new phantom. Measured organ doses were used to calculate secondary cancer risk based on Biological Effects of Ionizing Radiation (BEIR) VII models. In both treatment modalities, organ doses per prescribed dose (PD) mostly depended on the distance between organs. The locations showing the highest and lowest doses were the right kidney (external radiotherapy: 215.2 mGy; brachytherapy: 655.17 mGy) and the brain (external radiotherapy: 15.82 mGy; brachytherapy: 2.49 mGy), respectively. Organ doses to nearby regions were higher for brachytherapy than for external beam therapy, whereas organ doses to distant regions were higher for external beam therapy. Organ doses to distant treatment regions in external radiotherapy were due primarily to out-of-field radiation resulting from scattering and leakage in the gantry head. For brachytherapy, the highest estimated lifetime attributable risk per 100,000 population was to the stomach (88.6), whereas the lowest risks were to the brain (0.4) and eye (0.4); for external radiotherapy, the highest and lowest risks were to the thyroid (305.1) and brain (2.4). These results may help provide a database on the impact of radiotherapy-induced secondary cancer incidence dur-ing cervical cancer treatment, as well as suggest further research on strategies to counteract the risks of radiotherapy-associated secondary malignancies.

Evaluation of radiation dose to organs during kilovoltage cone-beam computed tomography using Monte Carlo simulation.

Image-guided techniques for radiation therapy have improved the precision of radiation delivery by sparing normal tissues. Cone-beam computed tomography (CBCT) has emerged as a key technique for patient positioning and target localization in radiotherapy. Here, we investigated the imaging radiation dose delivered to radiosensitive organs of a patient during CBCT scan. The 4D extended cardiac-torso (XCAT) phantom and Geant4 Application for Tomographic Emission (GATE) Monte Carlo (MC) simulation tool were used for the study. A computed tomography dose index (CTDI) standard polymethyl methacrylate (PMMA) phantom was used to validate the MC-based dosimetric evaluation. We implemented an MC model of a clinical on-board imager integrated with the Trilogy accelerator. The MC model's accuracy was validated by comparing its weighted CTDI (CTDIw) values with those of previous studies, which revealed good agreement. We calculated the absorbed doses of various human organs at different treatment sites such as the head-and-neck, chest, abdomen, and pelvis regions, in both standard CBCT scan mode (125 kVp, 80 mA, and 25 ms) and low-dose scan mode (125 kVp, 40 mA, and 10 ms). In the former mode, the average absorbed doses of the organs in the head and neck and chest regions ranged 4.09-8.28 cGy, whereas those of the organs in the abdomen and pelvis regions were 4.30-7.48 cGy. In the latter mode, the absorbed doses of the organs in the head and neck and chest regions ranged 1.61-1.89 cGy, whereas those of the organs in the abdomen and pelvis region ranged between 0.79-1.85 cGy. The reduction in the radiation dose in the low-dose mode compared to the standard mode was about 20%, which is in good agreement with previous reports. We opine that the findings of this study would significantly facilitate decisions regarding the administration of extra imaging doses to radiosensitive organs.

The Prediction of Stress in Radiation Therapy: Integrating Artificial Intelligence with Biological Signals.

This study aimed to predict stress in patients using artificial intelligence (AI) from biological signals and verify the effect of stress on respiratory irregularity. We measured 123 cases in 41 patients and calculated stress scores with seven stress-related features derived from heart-rate variability. The distribution and trends of stress scores across the treatment period were analyzed. Before-treatment information was used to predict the stress features during treatment. AI models included both non-pretrained (decision tree, random forest, support vector machine, long short-term memory (LSTM), and transformer) and pretrained (ChatGPT) models. Performance was evaluated using 10-fold cross-validation, exact match ratio, accuracy, recall, precision, and F1 score. Respiratory irregularities were calculated in phase and amplitude and analyzed for correlation with stress score. Over 90% of the patients experienced stress during radiation therapy. LSTM and prompt engineering GPT4.0 had the highest accuracy (feature classification, LSTM: 0.703, GPT4.0: 0.659; stress classification, LSTM: 0.846, GPT4.0: 0.769). A 10% increase in stress score was associated with a 0.286 higher phase irregularity (p < 0.025). Our research pioneers the use of AI and biological signals for stress prediction in patients undergoing radiation therapy, potentially identifying those needing psychological support and suggesting methods to improve radiotherapy effectiveness through stress management.

Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.

PURPOSE: Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer. METHODS: Two computed tomography (CT) datasets with well-defined labels, including contoured prostate, bladder, and rectum, were obtained from 18 patients. The labels of the CT images captured during radiation therapy (CT2nd) were predicted using CT images scanned before radiation therapy (CT1st). From the deformable vector fields (DVFs) created by using the VoxelMorph method, 10 DVFs were extracted when each of the modified CT and CT2nd images were deformed and registered to the fixed CT1st image. Augmented images were acquired by utilizing 110 extracted DVFs and spatially transforming the CT1st images and labels. An nnU-net autosegmentation network was trained by using the augmented images, and the CT2nd label was predicted. A patient-specific model was created for 18 patients, and the performances of the individual models were evaluated. The results were evaluated by employing the Dice similarity coefficient (DSC), average Hausdorff distance, and mean surface distance. The accuracy of the proposed model was compared with those of models trained with large datasets. RESULTS: Patient-specific models were developed successfully. For the proposed method, the DSC values of the actual and predicted labels for the bladder, prostate, and rectum were 0.94 ± 0.03, 0.84 ± 0.07, and 0.83 ± 0.04, respectively. CONCLUSION: We demonstrated the feasibility of automatic segmentation by employing individual patient datasets and image augmentation techniques. The proposed method has potential for clinical application in automatic prostate segmentation for ART.

Evaluating psychological anxiety in patients receiving radiation therapy using smartwatch.

PURPOSE: Patients undergoing radiation therapy (RT) often experience psychological anxiety that manifests as muscle contraction. Our study explored psychological anxiety in these patients by using biological signals recorded using a smartwatch. MATERIALS AND METHODS: Informed consent was obtained from participating patients prior to the initiation of RT. The patients wore a smartwatch from the waiting room until the conclusion of the treatment. The smartwatch acquired data related to heart rate features (average, minimum, and maximum) and stress score features (average, minimum, and maximum). On the first day of treatment, we analyzed the participants' heart rates and stress scores before and during the treatment. The acquired data were categorized according to sex and age. For patients with more than three days of data, we observed trends in heart rate during treatment relative to heart rate before treatment (HRtb) over the course of treatment. Statistical analyses were performed using the Wilcoxon signed-rank test and paired t-test. RESULTS: Twenty-nine individuals participated in the study, of which 17 had more than 3 days of data. During treatment, all patients exhibited elevated heart rates and stress scores, particularly those in the younger groups. The HRtb levels decreased as treatment progresses. CONCLUSION: Patients undergoing RT experience notable psychological anxiety, which tends to diminish as the treatment progresses. Early stage interventions are crucial to alleviate patient anxiety during RT.

Study of a plastic scintillating plate-based quality assurance system for pencil beam scanning proton beams.

INTRODUCTION: The purpose of this study was to evaluate a plastic scintillating plate-based beam monitoring system to perform quality assurance (QA) measurements in pencil beam scanning proton beam. METHODS: Single spots and scanned fields were measured with the high-resolution dosimetry system, consisting of a plastic scintillation plate coupled to a camera in a dark box at the isocenter. The measurements were taken at 110-190 MeV beam energies with 30° gantry angle intervals at each energy. Spot positions were determined using the plastic scintillating plate-based dosimetry system at the isocenter for 70-230 MeV beam energies with 30° gantry angle intervals. The effect of gantry angle on dose distribution was also assessed by determining the scanning pattern for daily QA and 25 fields treated with intensity-modulated proton therapy. RESULTS: Spot size, field flatness, and field symmetry of plastic scintillating plate-based dosimetry system were consistent with EBT3 at all investigated energies and angles. In all investigated energies and angles, the spot size measured was ±10% of the average size of each energy, the spot position measured was within ±2 mm, field flatness was within ±2%, and field symmetry was within ±1%. The mean gamma passing rates with the 3%/3 mm gamma criterion of the scanning pattern and 25 fields were 99.2% and 99.8%, respectively. CONCLUSIONS: This system can be effective for QA determinations of spot size, spot position, field flatness, and field symmetry over 360° of gantry rotation in a time- and cost-effective manner, with spatial resolution comparable to that of EBT3 film.

Monte Carlo simulation-based patient-specific QA using machine log files for line-scanning proton radiation therapy.

BACKGROUND: Quality assurance (QA) is a prerequisite for safe and accurate pencil-beam proton therapy. Conventional measurement-based patient-specific QA (pQA) can only verify limited aspects of patient treatment and is labor-intensive. Thus, a better method is needed to ensure the integrity of the treatment plan. PURPOSE: Line scanning, which involves continuous and rapid delivery of pencil beams, is a state-of-the-art proton therapy technique. Machine performance in delivering scanning protons is dependent on the complexity of the beam modulations. Moreover, it contributes to patient treatment accuracy. A Monte Carlo (MC) simulation-based QA method that reflects the uncertainty related to the machine during scanning beam delivery was developed and verified for clinical applications to pQA. METHODS: Herein, a tool for particle simulation (TOPAS) for nozzle modeling was used, and the code was commissioned against the measurements. To acquire the beam delivery uncertainty for each plan, patient plans were delivered. Furthermore, log files recorded every 60 µs by the monitors downstream of the nozzle were exported from the treatment control system. The spot positions and monitor unit (MU) counts in the log files were converted to dipole magnet strengths and number of particles, respectively, and entered into the TOPAS. For the 68 clinical cases, MC simulations were performed in a solid water phantom, and two-dimensional (2D) absolute dose distributions at 20-mm depth were measured using an ionization chamber array (Octavius 1500, PTW, Freiburg, Germany). Consequently, the MC-simulated 2D dose distributions were compared with the measured data, and the dose distributions in the pre-treatment QA plan created with RayStation (RaySearch Laboratories, Stockholm, Sweden). Absolute dose comparisons were made using gamma analysis with 3%/3 mm and 2%/2 mm criteria for 47 clinical cases without considering daily machine output variation in the MC simulation and 21 cases with daily output variation, respectively. All cases were analyzed with 90% or 95% of passing rate thresholds. RESULTS: For 47 clinical cases not considering daily output variations, the absolute gamma passing rates compared with the pre-treatment QA plan were 99.71% and 96.97%, and the standard deviations (SD) were 0.70% and 3.78% with the 3%/3 mm or 2%/2 mm criteria, respectively. Compared with the measurements, the passing rate of 2%/2 mm gamma criterion was 96.76% with 3.99% of SD. For the 21 clinical cases compared with pre-treatment QA plan data and measurements considering daily output variations, the 2%/2 mm absolute gamma analysis result was 98.52% with 1.43% of SD and 97.67% with 2.72% of SD, respectively. With a 95% passing rate threshold of 2%/2 mm criterion, the false-positive and false-negative were 21.8% and 8.3% for without and with considering output variation, respectively. With a 90% threshold, the false-positive and false-negative reduced to 11.4% and 0% for without and with considering output variation, respectively. CONCLUSIONS: A log-file-based MC simulation method for patient QA of line-scanning proton therapy was successfully developed. The proposed method exhibited clinically acceptable accuracy, thereby exhibiting a potential to replace the measurement-based dosimetry QA method with a 90% gamma passing rate threshold when applying the 2%/2 mm criterion.

FLASH dose rate calculation based on log files in proton pencil beam scanning therapy.

BACKGROUND: In radiation therapy, irradiating healthy normal tissues in the beam trajectories is inevitable. This unnecessary dose means that patients undergoing treatment risk developing side effects. Recently, FLASH radiotherapy delivering ultra-high-dose-rate beams has been re-examined because of its normal-tissue-sparing effect. To confirm the mean and instantaneous dose rates of the FLASH beam, stable and accurate dosimetry is required. PURPOSE: Detailed verification of the FLASH effect requires dosimeters and a method to measure the average and instantaneous dose rate stably for 2- or 3-dimensional dose distributions. To verify the delivered FLASH beam, we utilized machine log files from the built-in monitor chamber to develop a dosimetry method to calculate the dose and average/instantaneous dose rate distributions in two or three dimensions in a phantom. METHODS: To create a spread-out Bragg peak (SOBP) and deliver a uniform dose in a target, a mini-ridge filter was created with a 3D printer. Proton pencil beam line scanning plans of 2 × 2 cm2 , 3 × 3 cm2 , 4 × 4 cm2 , and round shapes with 2.3 cm diameter patterns delivering 230 MeV energy protons were created. The absorbed dose in the solid water phantom of each plan was measured using a PPC05 ionization chamber (IBA Dosimetry, Virginia, USA) in the SOBP region, and the log files for each plan were exported from the treatment control system console. Using these log files, the delivered dose and average dose rate were calculated using two methods: a direct method and a Monte Carlo (MC) simulation method that uses log file information. The computed and average dose rates were compared with the ionization chamber measurements. Additionally, instantaneous dose rates in user-defined volumes were calculated using the MC simulation method with a temporal resolution of 5 ms. RESULTS: Compared to ionization chamber dosimetry, 10 of 12 cases using the direct calculation method and 9 of 11 cases using the MC method had a dose difference below ±3%. Nine of 12 cases using the direct calculation method and 8 of 11 cases using the MC method had dose rate differences below ±3%. The average and maximum dose differences for the direct calculation and MC method were-0.17, +0.72%, and -3.15, +3.32%, respectively. For the dose rate difference, the average and maximum for the direct calculation and MC method were +1.26, +1.12%, and +3.75, +3.15%, respectively. In the instantaneous dose rate calculation with the MC simulation, a large fluctuation with a maximum of 163 Gy/s and a minimum of 4.29 Gy/s instantaneous dose rate was observed in a specific position, whereas the mean dose rate was 62 Gy/s. CONCLUSIONS: We successfully developed methods in which machine log files are used to calculate the dose and the average and instantaneous dose rates for FLASH radiotherapy and demonstrated the feasibility of verifying the delivered FLASH beams.

Development of a video-guided real-time patient motion monitoring system.

PURPOSE: The authors developed a video image-guided real-time patient motion monitoring (VGRPM) system using PC-cams, and its clinical utility was evaluated using a motion phantom. METHODS: The VGRPM system has three components: (1) an image acquisition device consisting of two PC-cams, (2) a main control computer with a radiation signal controller and warning system, and (3) patient motion analysis software developed in-house. The intelligent patient motion monitoring system was designed for synchronization with a beam on/off trigger signal in order to limit operation to during treatment time only and to enable system automation. During each treatment session, an initial image of the patient is acquired as soon as radiation starts and is compared with subsequent live images, which can be acquired at up to 30 fps by the real-time frame difference-based analysis software. When the error range exceeds the set criteria (δ(movement)) due to patient movement, a warning message is generated in the form of light and sound. The described procedure repeats automatically for each patient. A motion phantom, which operates by moving a distance of 0.1, 0.2, 0.3, 0.5, and 1.0 cm for 1 and 2 s, respectively, was used to evaluate the system performance. The authors measured optimal δ(movement) for clinical use, the minimum distance that can be detected with this system, and the response time of the whole system using a video analysis technique. The stability of the system in a linear accelerator unit was evaluated for a period of 6 months. RESULTS: As a result of the moving phantom test, the δ(movement) for detection of all simulated phantom motion except the 0.1 cm movement was determined to be 0.2% of total number of pixels in the initial image. The system can detect phantom motion as small as 0.2 cm. The measured response time from the detection of phantom movement to generation of the warning signal was 0.1 s. No significant functional disorder of the system was observed during the testing period. CONCLUSIONS: The VGRPM system has a convenient design, which synchronizes initiation of the analysis with a beam on/off signal from the treatment machine and may contribute to a reduction in treatment error due to patient motion and increase the accuracy of treatment dose delivery.

Clinical applicability of deep learning-based respiratory signal prediction models for four-dimensional radiation therapy.

For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated. Among the 540 respiration signals, 60 signals are used as test data. Each of the remaining 480 signals was spilt into training and validation data in a 7:3 ratio. A total of 1000 ms of the signal sequence (Ts) is entered to the models, and the signal at 500 ms afterward (Pt) is predicted (standard training condition). The accuracy measures are: (1) root mean square error (RMSE) and Pearson correlation coefficient (CC), (2) accuracy dependency on Ts and Pt, (3) respiratory pattern dependency, and (4) error for 30% and 70% of the respiration gating for a 5 mm tumor motion for latencies of 300, 500, and 700 ms. Under standard conditions, the Transformer model exhibits the highest accuracy with an RMSE and CC of 0.1554 and 0.9768, respectively. An increase in Ts improves accuracy, whereas an increase in Pt decreases accuracy. An evaluation of the regularity of the respiratory signals reveals that the lowest predictive accuracy is achieved with irregular amplitude patterns. For 30% and 70% of the phases, the average error of the three models is <1.4 mm for a latency of 500 ms and >2.0 mm for a latency of 700 ms. The prediction accuracy of the Transformer is superior to LSTM and Bi-LSTM. Thus, the three models have clinically applicable accuracies for a latency <500 ms for 10 mm of regular tumor motion. The clinical acceptability of the deep learning models depends on the inherent latency and the strategy for reducing the irregularity of respiration.

Chronological Analysis of Acute Hematological Outcomes after Proton and Photon Beam Craniospinal Irradiation in Pediatric Brain Tumors.

PURPOSE: This study aimed to compare the early hematological dynamics and acute toxicities between proton beam craniospinal irradiation (PrCSI) and photon beam craniospinal irradiation (PhCSI) for pediatric brain tumors. MATERIALS AND METHODS: We retrospectively reviewed patients with pediatric brain tumors who received craniospinal irradiation (CSI). The average change in hemoglobin levels (ΔHbavg), absolute lymphocyte counts (ΔALCavg), and platelet counts (ΔPLTavg) from baseline values was evaluated and compared between the PrCSI and PhCSI groups at 1 and 2 weeks after the initiation of CSI, 1 week before and at the end of radiotherapy, and 3-4 weeks after the completion of radiotherapy using t test and mixed-model analysis. RESULTS: The PrCSI and PhCSI groups consisted of 36 and 30 patients, respectively. There were no significant differences in ΔHbavg between the two groups at any timepoint. However, ΔALCavg and ΔPLTavg were significantly lower in the PhCSI group than in PrCSI group at every timepoint, demonstrating that PrCSI resulted in a significantly lower rate of decline and better recovery of absolute lymphocyte and platelet counts. The rate of grade 3 acute anemia was significantly lower in the PrCSI group than in in the PhCSI group. CONCLUSION: PrCSI showed a lower rate of decline and better recovery of absolute lymphocyte and platelet counts than PhCSI in the CSI for pediatric brain tumors. Grade 3 acute anemia was significantly less frequent in the PrCSI group than in the PhCSI group. Further large-scale studies are warranted to confirm these results.

Monitor unit prediction model for wobbling proton therapy with ridge filters.

PURPOSE: We introduced an output factor (cGy/MU) prediction model for wobbling proton beams over the full range of proton energy, scatterer thickness, and the width of spread-out Bragg peak (SOBP). MATERIALS AND METHODS: From December 2015 to August 2020, 1990 wobbling proton fields were used to treat patients, where 1714 fields had a diameter smaller than 11 cm and 276 had a diameter between 11 and 16 cm, which were designated as small and middle wobbling radius cases, respectively. The output factor is defined as the ratio of proton absorbed dose at mid-depth of SOBP to monitor unit (MU). It depends dominantly on proton energy, scatterer thickness, and the width of SOBP. We established the prediction model using the polynomial fitting function and determined its coefficients for the small and middle wobbling radius cases. We evaluated the accuracy of our prediction model by calculating the difference between predicted and measured output factors. RESULTS: For the small wobbling radius cases, the mean value of the output factor difference was 0.22% with a standard deviation of 1.3%. For the middle wobbling radius cases, the mean value was 0.20% and with a standard deviation of 0.79%. The large deviation was especially observed for wobbling proton beams having small field size and small width of SOBP. CONCLUSIONS: We made a prediction model of output factor for wobbling proton beams, thereby determining MU of each beam. This included the dependency of the output factor on the proton energy between 70 and 230 MeV, scatterer thickness, and the width of SOBP. For 93.6% of the small and 95.5% of the middle wobbling radius cases, the deviation between predicted and measured output factor was below 3%. The cases with deviations of predicted and measured output factor above 3% had small field size and small width of SOBP. The accuracy of our prediction model would be improved by adopting the field size effect and measuring more cases of small field size and small SOBP width in the future.

Improvement of megavoltage computed tomography image quality for adaptive helical tomotherapy using cycleGAN-based image synthesis with small datasets.

PURPOSE: Megavoltage computed tomography (MVCT) offers an opportunity for adaptive helical tomotherapy. However, high noise and reduced contrast in the MVCT images due to a decrease in the imaging dose to patients limits its usability. Therefore, we propose an algorithm to improve the image quality of MVCT. METHODS: The proposed algorithm generates kilovoltage CT (kVCT)-like images from MVCT images using a cycle-consistency generative adversarial network (cycleGAN)-based image synthesis model. Data augmentation using an affine transformation was applied to the training data to overcome the lack of data diversity in the network training. The mean absolute error (MAE), root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were used to quantify the correction accuracy of the images generated by the proposed algorithm. The proposed method was validated by comparing the images generated with those obtained from conventional and deep learning-based image processing method through non-augmented datasets. RESULTS: The average MAE, RMSE, PSNR, and SSIM values were 18.91 HU, 69.35 HU, 32.73 dB, and 95.48 using the proposed method, respectively, whereas cycleGAN with non-augmented data showed inferior results (19.88 HU, 70.55 HU, 32.62 dB, 95.19, respectively). The voxel values of the image obtained by the proposed method also indicated similar distributions to those of the kVCT image. The dose-volume histogram of the proposed method was also similar to that of electron density corrected MVCT. CONCLUSIONS: The proposed algorithm generates synthetic kVCT images from MVCT images using cycleGAN with small patient datasets. The image quality achieved by the proposed method was correspondingly improved to the level of a kVCT image while maintaining the anatomical structure of an MVCT image. The evaluation of dosimetric effectiveness of the proposed method indicates the applicability of accurate treatment planning in adaptive radiation therapy.

Development of a time-resolved mirrorless scintillation detector.

PURPOSE: We developed a compact and lightweight time-resolved mirrorless scintillation detector (TRMLSD) employing image processing techniques and a convolutional neural network (CNN) for high-resolution two-dimensional (2D) dosimetry. METHODS: The TRMLSD comprises a camera and an inorganic scintillator plate without a mirror. The camera was installed at a certain angle from the horizontal plane to collect scintillation from the scintillator plate. The geometric distortion due to the absence of a mirror and camera lens was corrected using a projective transform. Variations in brightness due to the distance between the image sensor and each point on the scintillator plate and the inhomogeneity of the material constituting the scintillator were corrected using a 20.0 × 20.0 cm2 radiation field. Hot pixels were removed using a frame-based noise-reduction technique. Finally, a CNN-based 2D dose distribution deconvolution model was applied to compensate for the dose error in the penumbra region and a lack of backscatter. The linearity, reproducibility, dose rate dependency, and dose profile were tested for a 6 MV X-ray beam to verify dosimeter characteristics. Gamma analysis was performed for two simple and 10 clinical intensity-modulated radiation therapy (IMRT) plans. RESULTS: The dose linearity with brightness ranging from 0.0 cGy to 200.0 cGy was 0.9998 (R-squared value), and the root-mean-square error value was 1.010. For five consecutive measurements, the reproducibility was within 3% error, and the dose rate dependency was within 1%. The depth dose distribution and lateral dose profile coincided with the ionization chamber data with a 1% mean error. In 2D dosimetry for IMRT plans, the mean gamma passing rates with a 3%/3 mm gamma criterion for the two simple and ten clinical IMRT plans were 96.77% and 95.75%, respectively. CONCLUSION: The verified accuracy and time-resolved characteristics of the dosimeter may be useful for the quality assurance of machines and patient-specific quality assurance for clinical step-and-shoot IMRT plans.

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer.

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D98% and D95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D98% and D95% than sCTs from other methodologies.

Comparison of film dosimetry techniques used for quality assurance of intensity modulated radiation therapy.

PURPOSE: Accurate dosimetry is essential to ensure the quality of advanced radiation treatments, such as intensity modulated radiation therapy (IMRT). Therefore, a comparison study was conducted to assess the accuracy of various film dosimetry techniques that are widely used in clinics. METHODS: A simulated IMRT plan that produced an inverse pyramid dose distribution in a perpendicular plane of the beam axis was designed with 6 MV x rays to characterize the large contribution of scattered photons to low dose regions. Three film dosimetry techniques, EDR2, EDR2 with low-energy photon absorption lead filters (EDR2 WF), and GafChromic EBT, were compared to ionization chamber measurements as well as Monte Carlo (MC) simulations. The accuracy of these techniques was evaluated against the ionization chamber data. Two-dimensional comparisons with MC simulation results were made by computing the gamma index (gamma) with criteria ranging from 2% of dose difference or 2 mm of distance to agreement (2%/2 mm) to 4%/4 mm on the central vertical plane (20 x 20 cm2) of a square solid water phantom. Depth doses and lateral profiles at depths of 5, 10, and 15 cm were examined to characterize the deviation of film measurements and MC predictions from ionization chamber measurements. RESULTS: In depth dose comparisons, the deviation between the EDR2 films was 9% in the low dose region and 5% in high dose region, on average. With lead filters, the average deviation was reduced to -1.3% and -0.3% in the low dose and high dose regions, respectively. EBT film results agreed within 1.5% difference on average with ionization chamber measurements in low and high dose regions. In two-dimensional comparisons with MC simulation, EDR2 films passed gamma tests with a 2%/2 mm criterion only in the high dose region (gamma < or = 1, total of 63.06% of the tested region). In the low dose region, EDR2 films passed gamma tests with 3%/3 mm criterion (gamma < or = 1, total of 98.4% of the tested region). For EDR2 WF and GafChromic EBT films, gamma tests with a 2% /2 mm criterion (gamma < or = 1) in the tested area was 97.3% and 96.8% of the tested region, respectively. CONCLUSIONS: The EDR2 film WF and GafChromic EBT film achieved an average accuracy level of 1.5% against an ionization chamber. These two techniques agreed with the MC prediction in 2%/2mm criteria evaluated by the gamma index, whereas EDR2 without filters achieved an accuracy level of 3%/3 mm with the decision criteria of agreement greater than 95% of the tested region. The overall results will provide a useful quantitative reference for IMRT verifications.

Quality assurance of isocentres for passive proton beam nozzles using motion capture cameras.

PURPOSE: The objective of this work is to determine mechanical, radiation, and imaging isocentres in three-dimensional (3D) coordinates and verifying coincidence of isocentres of passively scattered proton beam using a visual tracking system (VTS) and an in-house developed phantom named the Eagle. METHODS: The Eagle phantom consists of two modules: The first, named Eagle-head, is used for determining 3D mechanical isocentre of gantry rotation. The second, named Eagle-body, is used for determining 3D radiation and imaging isocentres. The Eagle-body has four slots wherein radiochromic films were inserted for measuring the 3D radiation isocentre and a metal bead was embedded in the centre of one cube to determine the imaging isocentre; this was determined by analysing cone-beam computed tomography images of the cube. Infrared reflective markers that can be tracked by VTS were attached to the Eagle at predetermined locations. The tracked data were converted into 3D treatment room coordinates. The developed method was compared with other methods to assess accuracy. RESULTS: The isocentres were determined in mm with respect to the laser isocentre. The mechanical, radiation, and imaging isocentres were (-0.289, 0.189, 0.096), (-0.436, -0.217, 0.009), and (0.134, 0.142, 0.103), respectively. When compared with other methods, the difference in coordinates was (-0.033, -0.107, 0.014) and (0.003, 0.067, 0.039) for radiation and imaging isocentres, respectively. CONCLUSION: The developed system was found to be useful in providing fast and accurate measurements of the three isocentres in the 3D treatment room coordinate system.

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients.

This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network-based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.

Comparison of clinical outcomes between passive scattering versus pencil-beam scanning proton beam therapy for hepatocellular carcinoma.

BACKGROUND AND PURPOSE: Our study aimed to compare the oncologic outcomes and toxicities between passive scattering (PS) proton beam therapy (PBT) and pencil-beam scanning (PBS) PBT for primary hepatocellular carcinoma (HCC). MATERIALS AND METHODS: The multidisciplinary team for liver cancer identified the PBT candidates who were ineligible for resection or radiofrequency ablation. We retrospectively analyzed 172 patients who received PBT for primary HCC from January 2016 to December 2017. The PS with wobbling method was applied with both breath-hold and regular breathing techniques, while the PBS method was utilized only for regular breathing techniques covering the full amplitude of respiration. To maintain the balance of the variables between the PS and PBS groups, we performed propensity score matching. RESULTS: The median follow-up duration for the total cohort was 14 months (range, 1-31 months). After propensity score matching, a total of 103 patients (70 in the PS group and 33 in the PBS group) were included in analysis. There were no significant differences in the rates of overall survival (OS), in-field local control (IFLC), out-field intrahepatic control (OFIHC), extrahepatic progression-free survival (EHPFS), and complete response (CR) between the matched groups. In the subgroup analyses, no subgroup showed a significant difference in IFLC between the PS and PBS groups. There was also no significant difference in the toxicity profiles between the groups. CONCLUSION: There are no differences in oncologic outcomes, including OS, IFLC, OFIHC, EHPFS, and CR rates, or in the toxicity profiles between PS and PBS PBT for primary HCC.