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.

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.

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.

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.

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.

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.

Feasibility of two-dimensional dose distribution deconvolution using convolution neural networks.

PURPOSE: The purpose of this study was to investigate the feasibility of two-dimensional (2D) dose distribution deconvolution using convolutional neural networks (CNNs) instead of an analytical approach for an in-house scintillation detector that has a detector-interface artifact in the penumbra region. METHODS: Datasets of 2D dose distributions were acquired from a medical linear accelerator of Novalis Tx. The datasets comprise two different sizes of square radiation fields and 13 clinical intensity-modulated radiation treatment (IMRT) plans. These datasets were divided into two datasets (training and test) to train and validate the developed network, called PenumbraNet, which is a shallow linear CNN. The PenumbraNet was trained to transform the measured dose distribution [M(x, y)] to calculated distribution [D(x, y)] by the treatment planning system. After training of the PenumbraNet was completed, the performance was evaluated using test data, which were 10 × 10 cm2 open field and ten clinical IMRT cases. The corrected dose distribution [C(x, y)] was evaluated against D(x, y) with 2%/2 mm and 3%/3 mm criteria of the gamma index for each field. The M(x, y) and deconvolved dose distribution with the analytically obtained kernel using Wiener filtering [A(x, y)] were also evaluated for comparison. In addition, we compared the performance of the shallow depth of linear PenumbraNet with that of nonlinear PenumbraNet and a deep nonlinear PenumbraNet within the same training epoch. RESULTS: The mean gamma passing rates were 84.77% and 95.81% with 3%/3 mm gamma criteria for A(x, y) and C(x, y) of the PenumbraNet, respectively. The mean gamma pass rates of nonlinear PenumbraNet and the deep depth of nonlinear PenumbraNet were 96.62%, 93.42% with 3%/3 mm gamma criteria, respectively. CONCLUSIONS: We demonstrated the feasibility of the PenumbraNets for 2D dose distribution deconvolution. The nonlinear PenumbraNet which has the best performance improved the gamma passing rate by 11.85% from the M(x, y) at 3%/3 mm gamma criteria.

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.

Current status of proton therapy techniques for lung cancer.

Proton beams have been used for cancer treatment for more than 28 years, and several technological advancements have been made to achieve improved clinical outcomes by delivering more accurate and conformal doses to the target cancer cells while minimizing the dose to normal tissues. The state-of-the-art intensity modulated proton therapy is now prevailing as a major treatment technique in proton facilities worldwide, but still faces many challenges in being applied to the lung. Thus, in this article, the current status of proton therapy technique is reviewed and issues regarding the relevant uncertainty in proton therapy in the lung are summarized.

Investigations of line scanning proton therapy with dynamic multi-leaf collimator.

PURPOSE: Scanning proton therapy has dosimetric advantage over passive treatment, but has a large penumbra in low-energy region. This study investigates the penumbra reduction when multi-leaf collimators (MLCs) are used for line scanning proton beams and secondary neutron production from MLCs. METHODS: Scanning beam plans with and without MLC shaping were devised. Line scanning proton plan of 36 energy layers between 71.2 and 155.2 MeV was generated. The MLCs were shaped according to the cross-sectional target shape for each energy layer. The two-dimensional doses were measured through an ion-chamber array, depending on the presence of MLC field, and Monte Carlo (MC) simulations were performed. The plan, measurement, and MC data, with and without MLC, were compared at each depth. The secondary neutron dose was simulated with MC. Ambient neutron dose equivalents were computed for the line scanning with 10 × 10 × 5 cm3 volume and maximum proton energy of 150 MeV, with and without MLCs, at lateral distances of 25-200 cm from the isocenter. The neutron dose for a wobbling plan with 10 × 10 × 5 cm3 volume was also evaluated. RESULTS: The lateral penumbra width using MLC was reduced by 23.2% on average, up to a maximum of 32.2%, over the four depths evaluated. The ambient neutron dose equivalent was 18.52% of that of the wobbling beam but was 353.1% larger than the scanning open field. CONCLUSIONS: MLC field shaping with line scanning reduced the lateral penumbra and should be effective in sparing normal tissue. However, it is important to investigate the increase in neutron dose.

High-precision quality assurance of robotic couches with six degrees of freedom.

A robotic couch capable of six degrees of freedom (6-DoF) of motion was introduced for state-of-the-art radiation therapy. Patient treatment requires precise quality assurance (QA) of 6-DoF. Unfortunately, conventional methods do not provide the requisite accuracy and precision. Therefore, we developed a high-precision automated QA system using a visual tracking system (VTS). The VTS comprises four motion-sensing cameras, a cube with infrared reflective markers. To acquire data in treatment room coordinates, a transformation matrix from VTS coordinates to treatment room coordinates was determined. The mean error and standard deviation of linear and rotational motions, as well as couch sagging were analyzed from continuously acquired images in the moving couch. The accuracy of VTS was 0.024 mm deviation for the sinusoidal motion, and the accuracy of the transformation matrix was 0.02 mm. In a cross-comparison, the difference between Laser Tracker (FARO) measurements was 0.14 ±â€¯0.12 mm for translation and 0.032 ±â€¯0.026° on average for yaw rotation. The new system provides QA of yaw, pitch and roll motion as well as sagging of the couch and sub-millimeter/degree accuracy together with precision.

Feasibility study of Fe3O4/TaO x nanoparticles as a radiosensitizer for proton therapy.

We investigated the feasibility of using multifunctional Fe3O4/TaO x (core/shell) nanoparticles, developed for use in contrast agents for computed tomography (CT) and magnetic resonance imaging (MRI), as dose-enhancing radiosensitizers. First, to verify the detectability of Fe3O4/TaO x nanoparticles in imaging, in vivo tests were conducted. Approximately 600 mg kg-1 of 19 nm-diameter Fe3O4/TaO x nanoparticles dispersed in phosphate-buffered saline was injected into the tail vein of six Balb/c mice used as tumour (4T1 mammary carcinoma cell) models. Three mice underwent MRI (BioSpec 70/20 USR, Bruker, Billerica, MA, USA) and micro-CT (Inveon, Siemens Preclinical, Knoxville, TN, USA) before and after the injection. The difference between the pre- and post-injection images was quantified by finding the correlation coefficient. The aorta, blood vessel, and liver were clearly seen in the MRI and micro-CT images 60 min after intravenous injection of Fe3O4/TaO x nanoparticles, but the tumour region was not visible in the CT images until after 24 h. There were large differences between the pre- and post-injection images. Second, the therapeutic enhancement dose of nanomaterials was computed via Monte Carlo simulation. Monoenergetic 70- and 150 MeV proton beams irradiated x-ray contrast agent (iodine, BaSO4), MRI contrast agent (gadolinium, Fe3O4), Au, Fe3O4/TaO x (core/shell) nanoparticles and water located at the centre of a 4 × 4 × 4 µm3 water phantom, upon which the dose enhancement ratio (DER) (dose with/without nanoparticles) was computed. When 70 MeV protons irradiated the Au, gadolinium, Fe3O4/TaO x , Fe3O4, iodine, and BaSO4 nanoparticles, the DERs at 1 nm were 15.76, 7.68, 7.82, 6.17, 4.85, and 5.51, respectively. Fe3O4/TaO x nanoparticles have the potential to be used as a multifunctional agent that enhances tumour detection and increases the dose. Dose enhancement with Fe3O4/TaO x was half that with Au. However, Fe3O4/TaO x is much cheaper than Au, and it is expected that tumour targeting combined with magnetic field could overcome the low DER.