Dose Calculation Method Research Articles

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region.

In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated. 41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( ) and a population-based dose calculation method ( ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs. The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60HU for MAE. Using the 2%/2mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0 0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0 0.8% and 99.1 0.8% for the and , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4 1.6% and 99.2 0.6% for and , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the approach in an MR only workflow.

Estimation of radiation absorbed dose in man from 166Ho-EDTMP based on biodistribution data in Wistar rats

Bone marrow malignancies including multiple myeloma are treated by the myeloablative radiotherapy and subsequent stem cell transplantation. Radiopharmaceuticals are used for the selective delivery of radiation absorbed dose to bone marrow. In this work, the radiation absorbed dose of various organs of the adult man from 166Ho-EDTMP radiopharmaceutical was estimated based on biodistribution data in Wistar rats. The MIRD dose calculation method and the Sparks and Aydogan methodology for extrapolation of %IA of organs from animal to human were applied. The results shows that, more than 61% of injected activity is cumulated on the surface of the trabecular and cortical bones. Red marrow and osteogenic cells radiation absorbed doses were estimated to about 1.4 and 3.1 mGy/MBq, respectively. Compared to other aminophosphonate radiopharmaceuticals, 166Ho-EDTMP requires a minimum activity to deliver a therapeutic dose of 25 Gy to the red bone marrow. The result were compared with available data from literature.

Deep dose plugin: towards real-time Monte Carlo dose calculation through a deep learning-based denoising algorithm

Monte Carlo (MC) simulation is considered the gold standard method for radiotherapy dose calculation. However, achieving high precision requires a large number of simulation histories, which is time-consuming. The use of computer graphics processing units (GPUs) has greatly accelerated MC simulation and allows dose calculation within a few minutes for a typical radiotherapy treatment plan. However, some clinical applications demand real-time efficiency for MC dose calculation. To tackle this problem, we have developed a real-time, deep learning (DL)-based dose denoiser that can be plugged into a current GPU-based MC dose engine to enable real-time MC dose calculation. We used two different acceleration strategies to achieve this goal: (1) we applied voxel unshuffle and voxel shuffle operators to decrease the input and output sizes without any information loss, and (2) we decoupled the 3D volumetric convolution into a 2D axial convolution and a 1D slice convolution. In addition, we used a weakly supervised learning framework to train the network, which greatly reduces the size of the required training dataset and thus enables fast fine-tuning-based adaptation of the trained model to different radiation beams. Experimental results show that the proposed denoiser can run in as little as 39 ms, which is 11.6 times faster than the baseline model. As a result, the whole MC dose calculation pipeline can be finished within ∼ 0.15 s, including both GPU MC dose calculation and DL-based denoising, achieving the real-time efficiency needed for some radiotherapy applications, such as online adaptive radiotherapy.

Development of local-scale high-resolution atmospheric dispersion model using large-eddy simulation part 6: introduction of detailed dose calculation method

ABSTRACT We developed a LOcal-scale High-resolution atmospheric DIspersion Model using Large-Eddy Simulation (LOHDIM-LES) for the safety assessment of nuclear facilities and emergency responses to accidental or deliberate releases of radioactive materials. In parts 1–5 of this series, the basic performance was examined by comparing with wind tunnel or field experimental results on plume dispersion over various complex surface geometries under ideal and realistic atmospheric conditions. In this study, we introduced a dose calculation method that can simulate air dose rate distributions by considering shielding effects and inhomogeneous distribution of radioactive materials in the air and on the ground and building surfaces. First, we conducted LESs of radioactive plume dispersion and dry deposition in building array and investigated the basic performance of the dose calculation method by comparison with that of Monte-Carlo based three-dimensional radiation transport simulations by Particle and Heavy-Ion Transport code System (PHITS). Then, we compared the LOHDIM-LES results with actual air dose rate obtained from the monitoring posts in a nuclear facility. The observed data were reasonably simulated. Thus, we completed LOHDIM-LES, which can simulate plume dispersion, dry deposition, and air dose rates from complex distributions of radionuclides considering the influence of individual buildings under realistic meteorological conditions.

Long short‐term memory networks for proton dose calculation in highly heterogeneous tissues

To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANNs) in challenging three-dimensional (3D) anatomies. A novel proton dose calculation approach was designed based on the application of a long short-term memory (LSTM) network. It processes the 3D geometry as a sequence of two-dimensional (2D) computed tomography slices and outputs a corresponding sequence of 2D slices that forms the 3D dose distribution. The general accuracy of the approach is investigated in comparison to Monte Carlo reference simulations and pencil beam dose calculations. We consider both artificial phantom geometries and clinically realistic lung cases for three different pencil beam energies. For artificial phantom cases, the trained LSTM model achieved a 98.57% γ-index pass rate ([1%, 3mm]) in comparison to MC simulations for a pencil beam with initial energy 104.25MeV. For a lung patient case, we observe pass rates of 98.56%, 97.74%, and 94.51% for an initial energy of 67.85, 104.25, and 134.68MeV, respectively. Applying the LSTM dose calculation on patient cases that were fully excluded from the training process yields an average γ-index pass rate of 97.85%. LSTM networks are well suited for proton dose calculation tasks. Further research, especially regarding model generalization and computational performance in comparison to established dose calculation methods, is warranted.

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Lung shunt and lung dose calculation methods for radioembolization treatment planning.

Radioembolization, also known as selective internal radiation therapy (SIRT), is firmly established in the management of patients with unresectable liver cancers. Advances in normal and tumor liver dosimetry and new knowledge about tumor dose response relationships have helped promote the safe use of higher prescribed doses, consequently transitioning radioembolization from palliative to curative therapy. The lungs are considered a critical organ of risk for radioembolization treatment planning. Unfortunately, lung dosimetry has not achieved similar advances in dose calculation methodology as liver dosimetry. Current estimations of lung dose are dependent on a number of parameters associated with data acquisition and processing algorithms, leading to poor accuracy and precision. Therefore, the efficacy of curative radioembolization may be compromised in patients for whom the lung dose derived using currently available methods unnecessarily limits the desired administered activity to the liver. We present a systematic review of the various methods of determining the lung shunt fraction (LSF) and lung mean dose (LD). This review encompasses pretherapy estimations and post-therapy assessments of the LSF and LD using both 2D planar and 3D SPECT/CT based calculations. The advantages and limitations of each of these methods are deliberated with a focus on accuracy and practical considerations. We conclude the review by presenting a lexicon to precisely describe the methodology used for the estimation of LSF and LD; specifically, category, agent, modality, contour and algorithm, in order to aid in their interpretation and standardization in routine clinical practice.

Comparison of Accuracy and Speed in Computer-Assisted Versus Conventional Methods for Pediatric Drug Dose Calculation: A Scenario-Based Randomized Controlled Trial.

Pediatric emergency care is prone to medication errors in many aspects including prescriptions, administrations, and monitoring. This study was designed to assess the effects of computer-assisted calculation on reducing error rates and time to prescription of specific emergency drugs. We conducted a randomized crossover experimental study involving emergency medicine residents and paramedics in the Department of Emergency Medicine at Ramathibodi Hospital. Participants calculated and prescribed medications using both the conventional method and a computer-assisted method. Medication names, dosages, routes of administration, and time to prescription were collected and analyzed using logistic and quantile regression analysis. Of 562 prescriptions, we found significant differences between computer-assisted calculation and the conventional method in the calculation accuracy of overall medications, pediatric advanced life support (PALS) drugs, and sedative drugs (91.17% vs 67.26%, 86.54% vs 46.15%, and 89.29% vs 57.86%, respectively, P < .001). Moreover, there were significant differences in calculation time for overall medications, PALS drugs and sedative drugs (25 vs 47 seconds, P < .001), and computer-assisted calculation significantly decreased the gap in medication errors between doctors and paramedics (P < .001). We conclude that computer-assisted prescription calculation provides benefits over the conventional method in accuracy of all medication dosages and in time required for calculation, while enhancing the drug prescription ability of paramedics.

Determination of mononuclear cell count using peripheral smear and flow cytometry in peripheral blood stem cell products: A retrospective study from an Indian cancer center.

BACKGROUND:Mononuclear cells (MNCs) are considered equivalent to hematopoietic stem cells, and differential count using peripheral smear was routinely practiced to enumerate MNC. Flow cytometry plots used for CD34 enumeration assay can also be used in MNC enumeration as it counts more WBC events than manual methods. The aim was to determine the relationship and degree of agreement between peripheral smear and flow cytometry in MNC enumeration of peripheral blood stem cell (PBSC) products.METHODS:In 63 patients, 73 PBSC products were collected between January 2017 and September 2019. The differences in MNC count estimated by peripheral smear method and from flow cytometry plots used for CD34 enumeration were analyzed using Mann–Whitney test. Agreement between the two methods for MNC enumeration was determined by regression analysis. Receiver operating characteristic curve was performed to determine MNC threshold in peripheral blood and PBSC product for adequate mobilization and harvest.RESULTS:There was no difference in enumeration of median MNC count between peripheral smear and flow cytometry (52% vs. 59%, P = 0.185) in PBSC product. However, regression analysis indicated a constant and proportional difference between the methods with r = 0.52. Cumulative sum test for linearity showed deviation from linearity (P = 0.04). MNC counts in peripheral blood failed to achieve discrimination capacity in predicting adequate CD34+ yield/kg body weight in product.CONCLUSION:Peripheral smear estimated lower MNC counts than flow cytometry with weaker agreements between the two methods. Hence, MNC count derived from flow cytometry plot can substitute peripheral smear method for MNC dose calculations. MNC dose at 3.4 × 108/kg consistently predicted >2 × 106/kg CD34+ cells collected.

Cone Beam CT (CBCT) Based Synthetic CT Generation Using Deep Learning Methods for Dose Calculation of Nasopharyngeal Carcinoma Radiotherapy.

Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

Impact of Acquisition Protocols on Accuracy of Dose Calculation Based on XVI Cone Beam Computed Tomography.

Purpose:The objective of this work is to study the impact of acquisition protocols on the accuracy of cone beam computed tomography (CBCT)-based dose calculation and to determinate its limits from image characteristics such as image quality, Hounsfield numbers consistency, and restrictive sizes of volume acquisition, compared to the CT imaging for the different anatomy localizations: head and neck (H&N), thorax, and pelvis.Materials and Methods:In this work, we used a routine on-board imaging CBCT of the XVI system (Elekta, Stockholm, Sweden). Dosimetric calculations performed on CT images require the knowledge of the Hounsfield unit-relative electron density (HU-ReD) calibration curve, which is determined for each imaging technology and must be adapted to the imaging acquisition parameters (filter/field of view). The accuracy of the dose calculation from CBCT images strongly depends on the quality of these images and also on the appropriate correspondence to the electronic densities, which will be used by the treatment planning system to simulate the dose distribution. In this study, we evaluated the accuracy of the dose calculation for each protocol, as already pointed in many studies.Results:As a result, the protocols that give better results in terms of dose calculation are F0S20 for the H&N region and F1M20 for the thoracic and pelvic regions, with an error <2% compared to results obtained with CT images. In addition, the dose distributions obtained with CT and CBCT imaging modalities were compared by two different methods. The first comparison was done by gamma index in three planes (sagittal, coronal, and transverse) with 2%; 2 mm criteria. The results showed good correspondence, with more than 95% of points passed the criteria. We also compared the target volume, the organs at risk (OARs), and the maximum and minimum doses for the three localizations (H&N, thorax, and pelvis) in CT and CBCT imaging modalities using a Rando phantom.Conclusions:The choice of the adequate CBCT acquisition protocol and the appropriate phantom to determine the HU-ReD calibration curve provides a better precision in the calculation of dose on CBCT images. This allows improving the results obtained when using the HU-ReD calibration method for dose calculation in adaptive radiotherapy.

Data-driven dose calculation algorithm based on deep U-Net

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy and efficiency, which may result in unsatisfactory dose accuracy or highly intensive computation time in many clinical situations. The purpose of this work is to develop a novel dose calculation algorithm based on the deep learning method for radiation therapy. In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two-dimensional (2D) fluence map was first converted into a three-dimensional (3D) volume using ray traversal algorithm. 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. Two hundred patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%±2.28%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them. In this study we developed a new deep learning based dose calculation method. This approach was evaluated by the clinical cases with different sites. Our results demonstrated its feasibility and reliability and indicated its great potential to improve the efficiency and accuracy of radiation dose calculation for different treatment modalities.

GPU-accelerated Monte Carlo simulation of MV-CBCT

Monte Carlo simulation (MCS) is one of the most accurate computation methods for dose calculation and image formation in radiation therapy. However, the high computational complexity and long execution time of MCS limits its broad use. In this paper, we present a novel strategy to accelerate MCS using a graphic processing unit (GPU), and we demonstrate the application in mega-voltage (MV) cone-beam computed tomography (CBCT) simulation. A new framework that generates a series of MV projections from a single simulation run is designed specifically for MV-CBCT acquisition. A Geant4-based GPU code for photon simulation is incorporated into the framework for the simulation of photon transport through a phantom volume. The FastEPID method, which accelerates the simulation of MV images, is modified and integrated into the framework. The proposed GPU-based simulation strategy was tested for its accuracy and efficiency in a Catphan 604 phantom and an anthropomorphic pelvis phantom with beam energies at 2.5 MV, 6 MV, and 6 MV FFF. In all cases, the proposed GPU-based simulation demonstrated great simulation accuracy and excellent agreement with measurement and CPU-based simulation in terms of reconstructed image qualities. The MV-CBCT simulation was accelerated by factors of roughly 900–2300 using an NVIDIA Tesla V100 GPU card against a 2.5 GHz AMD Opteron™ Processor 6380.

Development of an extended Macro Monte Carlo method for efficient and accurate dose calculation in magnetic fields.

Progress in the field of magnetic resonance (MR)-guided radiotherapy has triggered the need for fast and accurate dose calculation in presence of magnetic fields. The aim of this work is to satisfy this need by extending the macro Monte Carlo (MMC) method to enable dose calculation for photon, electron, and proton beams in a magnetic field. The MMC method is based on the transport of particles in macroscopic steps through an absorber by sampling the relevant physical quantities from a precalculated database containing probability distribution functions. To enable MMC particle transport in a magnetic field, a transformation accounting for the Lorentz force is applied for each macro step by rotating the sampled position and direction around the magnetic field vector. The transformed position and direction distributions on local geometries are validated against full MC for electron and proton pencil beams. To enable photon dose calculation, an in-house MC algorithm is used for photon transport and interaction. Emerging secondary charged particles are passed to MMC for transport and energy deposition. The extended MMC dose calculation accuracy and efficiency is assessed by comparison with EGSnrc (photon and electron beams) and Geant4 (proton beam) calculated dose distributions of different energies and homogeneous magnetic fields for broad beams impinging on water phantoms with bone and lung inhomogeneities. The geometric transformation on the local geometries is able to reproduce the results of full MC for all investigated settings (difference in mean value and standard deviation <1%). Macro Monte Carlo calculated dose distributions in a homogeneous magnetic field are in agreement with EGSnrc and Geant4, respectively, with gamma passing rates >99.6% (global 2%, 2mm and 10% threshold criteria) for all situations. MMC achieves a substantial efficiency gain of up to a factor of 21 (photon beam), 66 (electron beam), and 356 (proton beam) compared to EGSnrc or Geant4. Efficient and accurate dose calculation in magnetic fields was successfully enabled by utilizing the developed extended MMC transport method for photon, electron, and proton beams.

Validation of Monte Carlo Model for Dose Evaluation outside the Treatment Field for Siemens 6MV Beam

Introduction: There has been a concern about the unintended doses to critical structures outside the treatment field due to the increased risk of radiation-induced second cancer following radiotherapy treatments. Today, Monte Carlo (MC) simulation is considered the most accurate method for dose calculations in different domains of medical physics. Material and Methods: The Geant4 Application for Tomographic Emission (GATE) code was used to create an MC model of 6MV Siemens Primus linac. Measurements were taken in a water phantom using an ion chamber to validate the MC model. Dose profiles outside the treatment field at 1.5 (dmax), 5.0 and10.0 cm depths for field sizes from 5×5 to 20×20 cm2 were measured in the present study. Out-of-field percentage depth dose (PDD) curves at 0.0, 5.0, and 7.5 cm off axis for field size 10×10 cm2 were investigated for both measurements and simulation. However out-of-field PDDs from 10 to 15 cm off axis for field size 10×10 cm2 were studied only by simulation. Results: The comparisons showed agreement between the measured and simulated doses for the out-of-field profiles along the in-plane direction for all considered field sizes and depths, as well as for the PDDs at 0.0 and 5.0 cm off axis, but with less agreement at 7.5 cm off axis. All the simulated out-of-field PDDs at distances ≥ 10 cm off axis had similar trend shapes. Conclusion: The developed MC model is considered a good representation of 6 MV Siemens Primus linac for the out-of-field dose calculation in lieu of measurements.

Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network

This study proposes a near-real-time spot-scanning proton dose calculation method with probabilistic uncertainty estimation using a three-dimensional convolutional neural network (3D-CNN). CT images and clinical target volume contours of 215 head and neck cancer patients were collected from a public database. 1484 and 488 plans were extracted for training and testing the 3D-CNN model, respectively. Spot beam data and single-field uniform dose (SFUD) labels were calculated for each plan using an open-source dose calculation toolkit. Variable spot data were converted into a fixed-size volume hereby called a ‘peak map’ (PM). 300 epochs of end-to-end training was implemented using sets of stopping power ratio and PM as input. Moreover, transfer learning techniques were used to adjust the trained model to SFUD doses calculated with different beam parameters and calculation algorithm using only 7.95% of training data used for the base model. Finally, accuracy of the 3D-CNN-calculated doses and model uncertainty was reviewed with several evaluation metrics. The 3D-CNN model calculates 3D proton dose distributions accurately with a mean absolute error of 0.778 cGyE. The predicted uncertainty is correlated with dose errors at high contrast edges. Averaged Sørensen-Dice similarity coefficients between binarized outputs and ground truths are mostly above 80%. Once the 3D-CNN model was well-trained, it can be efficiently fine-tuned for different proton doses by transfer learning techniques. Inference time for calculating one dose distribution is around 0.8 s for a plan using 1500 spot beams with a consumer grade GPU. A novel spot-scanning proton dose calculation method using 3D-CNN was developed. The 3D-CNN model is able to calculate 3D doses and uncertainty with any SFUD spot data and beam irradiation angles. Our proposed method should be readily extendable to other setups and plans and be useful for dose verification, image-guided proton therapy, or other applications.

An improved beam splitting method for intensity modulated proton therapy

The pencil beam algorithm (PBA) has become the predominant dose calculation method in proton therapy, due to its high level of efficiency. However, density heterogeneity decreases the accuracy of PBA. To improve PBA’s accuracy, a beam splitting method is used to divide the original scanning beam into multiple thinner beamlets. Beam splitting should ensure that the beamlets’ summed fluence is as close to the original beam fluence as possible, while keeping the spatial variance of beamlets small, and minimizing the number of beamlets. In this work, the generalized differential evolution (GDE) algorithm is utilized for the optimal scheme. Under reasonable constraints for the radius and weight of beamlets, several schemes are optimized via the GDE algorithm. In order to achieve a trade-off between accuracy and calculation speed, three hexagon-based schemes, which split the original beam into 7, 13, and 19 beamlets, respectively, are proposed and compared with the scheme of Raystation 4.5. The fluence distribution calculated by the schemes with 13 beamlets and 19 beamlets are demonstrated to be more accurate than the Raystation scheme, which has 19 beamlets, with a maximum absolute difference between the summed beamlets fluence and the original beam fluence of 2.12%, and 0.93%, respectively. Furthermore, beam splitting schemes are implemented into a proton dose calculation algorithm based on the KylinRay-IMPT TPS. These schemes, based on the dose algorithm, are compared with the Monte Carlo program TOPAS 3.2 in slab geometry with lateral heterogeneity. The dose, calculated by the dose algorithm using a scheme of 13 beamlets, shows a good agreement with the dose from TOPAS. In addition, an abdominal geometry is used for further verification. Gamma analysis passing rates greater than 99.7% are observed, with a 2%/2 mm criterion. Thus, the accuracy and effectiveness of the improved beam splitting method are preliminarily verified.

A dose voxel kernel method for rapid reconstruction of out-of-field neutron dose of patients in pencil beam scanning (PBS) proton therapy

Monte Carlo (MC) radiation transport methods are used for dose calculation as ‘gold standard.’ However, the method is computationally time-consuming and thus impractical for normal tissue dose reconstructions for the large number of proton therapy patients required for epidemiologic investigations of late health effects. In the present study, we developed a new dose calculation method for the rapid reconstruction of out-of-field neutron dose to patients undergoing pencil beam scanning (PBS) proton therapy. The new dose calculation method is based on neutron dose voxel kernels (DVKs) generated by MC simulations of a proton pencil beam irradiating a water phantom (60 × 60 × 300 cm3), which was conducted using a MC proton therapy simulation code, TOPAS. The DVKs were generated for 19 beam energies (from 70 to 250 MeV with the 10 MeV interval) and three range shifter thicknesses (1, 3, and 5 cm). An in-house program was written in C++ to superimpose the DVKs onto a patient CT images according to proton beam characteristics (energy, position, and direction) available in treatment plans. The DVK dose calculation method was tested by calculating organ/tissue-specific neutron doses of 1- and 5-year-old whole-body computational phantoms where intracranial and craniospinal irradiations were simulated. The DVK-based doses generally showed reasonable agreement with those calculated by direct MC simulations with a detailed PBS model that were previously published, with differences mostly less than 30% and 10% for the intracranial and craniospinal irradiations, respectively. The computation time of the DVK method for one patient ranged from 1 to 30 min on a single CPU core of a personal computer, demonstrating significant improvement over the direct MC dose calculation requiring several days on high-performance computing servers. Our DVK-based dose calculation method will be useful when dosimetry is needed for the large number of patients such as for epidemiologic or clinical research.

Evaluation of Drug Dose Calculation Ability of Nursing Students: An Interventional Study

The objective of the study was to evaluate the drug dose calculation ability of nursing students. A pre-post interventional study was conducted among 99 undergraduate nursing students studying in 2nd year, 3rd year and 4th year. Pre-test was conducted using a self-administered 20-item questionnaire on drug dose calculation test followed by a 30 minute lecture on the methods of drug dose calculation. Post-test was conducted using the same questionnaire after a washout period of six weeks. Mean score, standard deviation, frequency and percentage were calculated. Student’s t-test, one way ANOVA and McNemar Test were used for statistical analysis using SPSS version 11.5. P-value less than 0.05 was considered statistically significant. Pre-test and post-test mean score (± Standard deviation) were 10.59 ± 3.00 and 15.36 ± 2.46 respectively. There was 45.04% increase in mean score in the post-test after the intervention and it was statistically significant (P-value < 0.05). None of students scored more than 90% of mastery level in the pretest and 42 students (42.4%) scored more than 90% of mastery level in the post-test. The mean score was highest for third year students and lowest for 2nd year students in both pre-test and post-test; however, it was statistically not significant (P-value>0.05). Drug dose calculation ability of the students was poor in the pre-test and it significantly improved after the intervention. The study findings highlight the need for regular continuing mathematical and drug dose calculation practice among the students.

Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning.

Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&N cancer radiotherapy. Forty-four patients received VMAT for H&N cancer (70-63-56Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-air-bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CTref ). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CTref and pCT of each method. In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1HU, 266.6 and 208.9HU, 113.2 and 14.2HU, and 95.5 and -36.6HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P≤0.05). The DLM dose discrepancies were 7±8cGy (maximum=44cGy) for the ipsilateral parotid gland Dmean and 5±6cGy (max=26cGy) for the contralateral parotid gland mean dose (Dmean ). For the parotid gland Dmean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate±standard deviation was 98.1±1.2%, 91.0±5.3%, 97.9±1.6%, and 98.8±0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM. For H&N radiotherapy, DIR method and DLM appears as the most appealing CBCT-based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.