Comparison of linear energy transfer measurement for therapeutic carbon beam using CR-39 and TLD.

The measurement of linear energy transfer (LET) is crucial for the evaluation of the radiation effect in heavy ion therapy. As two detectors which are convenient to implant into the phantom, the performance of CR-39 and thermoluminescence detector (TLD) for LET measurement was compared by experiment and simulation in this study. The results confirmed the applicability of both detectors for LET measurements, but also revealed that the CR-39 detector would lead to potential overestimation of dose-averaged LET compared with the simulation by PHITS, while the TLD would have a large uncertainty measuring ions with LET larger than 20 keVµm-1. The results of this study were expected to improve the detection method of LET for therapeutic carbon beam and would finally be benefit to the quality assurance of heavy ion radiotherapy.

A feasibility study to predict 3D dose delivery accuracy for IMRT using DenseNet with log files.

OBJECTIVE: This study aims to explore the feasibility of DenseNet in the establishment of a three-dimensional (3D) gamma prediction model of IMRT based on the actual parameters recorded in the log files during delivery. METHODS: A total of 55 IMRT plans (including 367 fields) were randomly selected. The gamma analysis was performed using gamma criteria of 3% /3 mm (Dose Difference/Distance to Agreement), 3% /2 mm, 2% /3 mm, and 2% /2 mm with a 10% dose threshold. In addition, the log files that recorded the gantry angle, monitor units (MU), multi-leaf collimator (MLC), and jaws position during delivery were collected. These log files were then converted to MU-weighted fluence maps as the input of DenseNet, gamma passing rates (GPRs) under four different gamma criteria as the output, and mean square errors (MSEs) as the loss function of this model. RESULTS: Under different gamma criteria, the accuracy of a 3D GPR prediction model decreased with the implementation of stricter gamma criteria. In the test set, the mean absolute error (MAE) of the prediction model under the gamma criteria of 3% /3 mm, 2% /3 mm, 3% /2 mm, and 2% /2 mm was 1.41, 1.44, 3.29, and 3.54, respectively; the root mean square error (RMSE) was 1.91, 1.85, 4.27, and 4.40, respectively; the Sr was 0.487, 0.554, 0.573, and 0.506, respectively. There was a correlation between predicted and measured GPRs (P <  0.01). Additionally, there was no significant difference in the accuracy between the validation set and the test set. The accuracy in the high GPR group was high, and the MAE in the high GPR group was smaller than that in the low GPR group under four different gamma criteria. CONCLUSIONS: In this study, a 3D GPR prediction model of patient-specific QA using DenseNet was established based on log files. As an auxiliary tool for 3D dose verification in IMRT, this model is expected to improve the accuracy and efficiency of dose validation.

Radiation levels outside a patient undergoing177Lu-PSMA radioligand therapy.

Understanding the spatial distribution of radiation levels outside of a patient undergoing177Lu radioligand therapy is not only helpful for conducting correct tests for patient release, but also useful for estimation of its potential exposure to healthcare workers, caregivers, family members, and the general public. In this study, by mimicking the177Lu-labeled prostate-specific membrane antigen radioligand therapy for prostate cancers in an adult male, the spatial distribution of radiation levels outside of the phantom was simulated based on the Monte Carlo software of Particle and Heavy Ion Transport System, and verified by a series of measurements. Moreover, the normalized dose rates were further formulized on the three transverse planes representing the heights of pelvis, abdomen and chest. The results showed that the distributions of radiation levels were quite complex. Multi-directional and multi-height measurements are needed to ensure the external dose rate to meet the release criteria. In general, the radiation level was higher at the horizontal plane where the source was located, and the levels in front and behind of the body were higher than those of the left and right sides at the same height. The ratio of simulated dose rates to measured ones ranged from 0.82 to 1.19 within 1 m away from the body surface in all directions. Based on the established functions, the relative root mean square deviation between the calculated and simulated values were 0.21, 0.25 and 0.23 within a radius of 1 m on the pelvis, abdomen and chest transverse planes, respectively. It is expected that the results of this study would be helpful for guiding the test of extracorporeal radiation to determine the patient's release, and of benefit to estimate the radiation exposure to others.

Predicting the error magnitude in patient-specific QA during radiotherapy based on ResNet.

BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm.

Objectives. This study aims to develop a method for predicting patient-specific head organ doses by training a support vector regression (SVR) model based on radiomics features and graphics processing unit (GPU)-calculated reference doses.Methods. In this study, 237 patients who underwent brain CT scans were selected, and their CT data were transferred to an autosegmentation software to segment head regions of interest (ROIs). Subsequently, radiomics features were extracted from the CT data and ROIs, and the benchmark organ doses were computed using fast GPU-accelerated Monte Carlo (MC) simulations. The SVR organ dose prediction model was then trained using the radiomics features and benchmark doses. For the predicted organ doses, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were evaluated. The robustness of organ dose prediction was verified by changing the patient samples on the training and test sets randomly.Results. For all head organs, the maximal difference between the reference and predicted dose was less than 1 mGy. For the brain, the organ dose was predicted with an absolute error of 1.3%, and theR2reached up to 0.88. For the eyes and lens, the organ doses predicted by SVR achieved an RRMSE of less than 13%, the MAPE ranged from 4.5% to 5.5%, and theR2values were more than 0.7.Conclusions. Patient-specific head organ doses from CT examinations can be predicted within one second with high accuracy, speed, and robustness by training an SVR using radiomics features.

Evolutionary game analysis of indoor radon mitigation with local government involvement.

Radon is the second leading risk factor for lung cancer after smoking. As a public policy, radon mitigation not only involves radon control technology or its cost-benefit analysis, but also includes the decision-making process of local governments. In this study, the evolutionary game theory was used to analyse the interaction between local governments and residents based on the subsidy of the central government. Considering the practical data in China, factors influencing the behaviour of local governments and residents were discussed using numerical simulations. The results indicated that radon mitigation is a fully government-promoted action; thus, its implementation largely depends on the subsidy of the central government and the share of radon control costs borne by the local government. The financial burden for both local governments and residents is a more important determinant than long-term health effects. The relatively poor local economic situation could limit the implementation of radon control. There would be a public policy paradox wherein cities or regions with higher radon risk would have lower willingness for radon control, mainly due to the significantly higher costs of radon control. This work provides reference data for decision-making to implement radon control and is expected to offer some suggestions for local governments.

Guided block matching and 4-D transform domain filter projection denoising method for dynamic PET image reconstruction.

PURPOSE: Dynamic PET is an essential tool in oncology due to its ability to visualize and quantify radiotracer uptake, which has the potential to improve imaging quality. However, image noise caused by a low photon count in dynamic PET is more significant than in static PET. This study aims to develop a novel denoising method, namely the Guided Block Matching and 4-D Transform Domain Filter (GBM4D) projection, to enhance dynamic PET image reconstruction. METHODS: The sinogram was first transformed using the Anscombe method, then denoised using a combination of hard thresholding and Wiener filtering. Each denoising step involved guided block matching and grouping, collaborative filtering, and weighted averaging. The guided block matching was performed on accumulated PET sinograms to prevent mismatching due to low photon counts. The performance of the proposed denoising method (GBM4D) was compared to other methods such as wavelet, total variation, non-local means, and BM3D using computer simulations on the Shepp-Logan and digital brain phantoms. The denoising methods were also applied to real patient data for evaluation. RESULTS: In all phantom studies, GBM4D outperformed other denoising methods in all time frames based on the structural similarity and peak signal-to-noise ratio. Moreover, GBM4D yielded the lowest root mean square error in the time-activity curve of all tissues and produced the highest image quality when applied to real patient data. CONCLUSION: GBM4D demonstrates excellent denoising and edge-preserving capabilities, as validated through qualitative and quantitative assessments of both temporal and spatial denoising performance.

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance.

OBJECTIVE: To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML). METHODS: In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose image prediction (PDIP). Besides, 14 image-based features were extracted from RDD and EDD of portal dose predictions to obtain the feature vectors. In addition, a random forest was adopted for the multiclass classification task, and regression prediction for error magnitude. RESULTS: The top five features extracted with the highest weight included 1) the relative displacement in the x direction, 2) the ratio of the absolute minimum residual error to the maximal RDD value, 3) the product of the maximum and minimum residuals, 4) the ratio of the absolute maximum residual error to the maximal RDD value, and 5) the ratio of the absolute mean residual value to the maximal RDD value. The relative displacement in the x direction had the highest weight. The overall accuracy of the five-class classification model was 99.85% for the validation set and 99.30% for the testing set. This model could be applied to the classification of the error-free plan, COLL, MU, MLCS, and MLCR with an accuracy of 100%, 98.4%, 99.9%, 98.0%, and 98.3%, respectively. MLCR had the worst performance in error magnitude prediction (70.1-96.6%), while others had better performance in error magnitude prediction (higher than 93%). In the error magnitude prediction, the mean absolute error (MAE) between predicted error magnitude and actual error ranged from 0.03 to 0.33, with the root mean squared error (RMSE) varying from 0.17 to 0.56 for the validation set. The MAE and RMSE ranged from 0.03 to 0.50 and 0.44 to 0.59 for the test set, respectively. CONCLUSION: It could be demonstrated in this study that the image-based features extracted from RDD and EDD can be employed to identify different types of delivery errors and accurately predict error magnitude with the assistance of ML techniques. They can be used to associate traditional gamma analysis with clinically based analysis for error classification and magnitude prediction in patient-specific IMRT quality assurance.

Development of a voxel S-value database for patient internal radiation dosimetry.

PURPOSE: Personalized dosimetry with high accuracy drew great attention in clinical practices. Voxel S-value (VSV) convolution has been proposed to speed up absorbed dose calculations. However, the VSV method is efficient for personalized internal radiation dosimetry only when there are pre-calculated VSVs of the radioisotope. In this work, we propose a new method for VSV calculation based on the developed mono-energetic particle VSV database of γ, ß, α, and X-ray for any radioisotopes. METHODS: Mono-energetic VSV database for γ, ß, α, and X-ray was calculated using Monte Carlo methods. Radiation dose was first calculated based on mono-energetic VSVs for [F-18]-FDG in 10 patients. The estimated doses were compared with the values obtained from direct Monte Carlo simulation for validation of the proposed method. The number of VSVs used in calculation was optimized based on the estimated dose accuracy and computation time. RESULTS: The generated VSVs showed a great consistency with the results calculated using direct Monte Carlo simulation. For [F-18]-FDG, the proposed VSV method with number of VSV of 9 shows the best relative average organ absorbed dose uncertainty of 3.25% while the calculation time was reduced by 99% and 97% compared to the Monte Carlo simulation and traditional multiple VSV methods, respectively. CONCLUSIONS: In this work, we provided a method to generate the VSV kernels for any radioisotope based on the pre-calculated mono-energetic VSV database and significantly reduced the time cost for the multiple VSVs dosimetry approach. A software was developed to generate VSV kernels for any radioisotope in 19 mediums.

Automatic organ completion with image stitching for personalized radiation dosimetry in CT examinations.

PURPOSE: Computed tomography (CT) image-based patient-specific voxel-based dosimetry has difficulties complementing missing tissues for organs located partially inside or completely outside the image volume. Previous studies constructed patient-specific whole-body models by rescaling reference phantoms or extending regional CT images with manually adjusted phantoms. This study proposes a methodology for automatic organ completion of regional CT images for CT dosimetry using a stitching approach. METHODS: Virtual clinical trials were performed by truncating whole-body CT images to generate virtual clinical chest and abdominopelvic CT images. Corresponding anchor images for each patient were selected according to sex and similarity of the axial length and water equivalent diameter of the virtual regional CT images. Automatic image stitching was performed by transformation initialization and iteration, while the stitched CT images and organ atlas were used in GPU-based Geant4 Monte Carlo simulations to generate a radiation dose map and absorbed organ dose. To evaluate the performance of the stitching model in radiation dosimetry, organ mass differences and Jaccard's coefficient of stitched and rescaled anchor images were calculated, and the radiation doses were compared among the corresponding values from the VirtualDose®, original whole-body CT, stitching model, regional CT, registration-based rescaling method, and WED-based rescaling method. RESULTS: The anatomical accuracy of stitched images was significantly improved. For organs partially inside the image volume, organ dose estimation from the stitching model could be more accurate than that reported in previous studies. The absolute differences in effective dose from the stitched images were 6.55% and 4.81% for chest and abdominopelvic CT scans, respectively. CONCLUSION: The proposed automatic stitching model partially complements organs inside or outside the CT scan range and provides more accurate anatomical representations for radiation dosimetry than traditional phantom rescaling methods.

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study.

PURPOSE: The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. METHODS: Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich's theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. RESULTS: Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20-33, 33-47, 47-58, 58-69, 69-80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. CONCLUSIONS: A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features.

BACKGROUND: This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DLR) features based on 3D dose distribution. METHODS: A total of 140 patients with non-small cell lung cancer who received stereotactic body radiation therapy (SBRT) were retrospectively included in this study. These patients were randomly divided into the training (n = 112) and test (n = 28) sets. Besides, 107 dosiomics features were extracted by Pyradiomics, and 1316 DLR features were extracted by ResNet50. Feature visualization was performed based on Spearman's correlation coefficients, and feature selection was performed based on the least absolute shrinkage and selection operator. Three different models were constructed based on random forest, including (1) a dosiomics model (a model constructed based on dosiomics features), (2) a DLR model (a model constructed based on DLR features), and (3) a hybrid model (a model constructed based on dosiomics and DLR features). Subsequently, the performance of these three models was compared with receiver operating characteristic curves. Finally, these dosiomics and DLR features were analyzed with Spearman's correlation coefficients. RESULTS: In the training set, the area under the curve (AUC) of the dosiomics, DLR, and hybrid models was 0.9986, 0.9992, and 0.9993, respectively; the accuracy of these three models was 0.9643, 0.9464, and 0.9642, respectively. In the test set, the AUC of these three models was 0.8462, 0.8750, and 0.9000, respectively; the accuracy of these three models was 0.8214, 0.7857, and 0.8571, respectively. The hybrid model based on dosiomics and DLR features outperformed other two models. Correlation analysis between dosiomics features and DLR features showed weak correlations. The dosiomics features that correlated DLR features with the Spearman's rho |ρ| ≥ 0.8 were all first-order features. CONCLUSION: The hybrid features based on dosiomics and DLR features from 3D dose distribution could improve the performance of RP prediction after SBRT.

Development and calibration of a modifiable passive sampler for monitoring atmospheric tritiated water vapor in different environments.

Anthropogenic release of tritium from nuclear facilities is expected to increase significantly in the coming decades, which may cause radiation exposure to humans through the contamination of water and food chains. It is necessary and urgent to acquire detailed information about tritium in various environments for studying its behavior and assessing the potential radiation risk. In the atmosphere, although the passive sampling technique provides a low-cost and convenient way to characterize the dynamics of tritiated water vapor (HTO), a single, simple sampler configuration makes it difficult to collect sufficient and representative samples within the expected period from different environments. In this study, we systematically studied the impacts of sampler configurations on sampling performance and proposed a modifiable sampler design by scaling sampler geometry and adjusting absorbent to achieve different monitoring demands. The samplers were subsequently deployed at five sites in China and Germany for the field calibration and the measured results exhibited a good agreement between the adsorption process obtained in sites corrected with diffusion coefficient and the one calibrated in Shanghai. This suggests the feasibility of predicting sampling performance in the field based on known data. Finally, we developed a strategy for sampler modification and selection in different environments and demonstrated that using easily obtainable environmental data, our sampler can be optimized for any area without any time-consuming preliminary experiments. This work provides a scientific basis for establishing high-resolution atmospheric HTO database and expands the conventional empirical sampler design paradigm by demonstrating the feasibility of using quantitative indices for sampler performance customization.

Accounting for radiation exposure from previous CT exams while deciding on the next exam: What do referring clinicians think?

PURPOSE: To obtain clinicians' views of the need to account for radiation exposure from previous CT scans and the advisability of a regulatory mechanism to control the number of CT scans for an individual patient. METHODS: A convenience survey was conducted by emailing a link to a three-question electronic survey to clinicians in many countries, mostly through radiology and radiation protection contacts. RESULTS: 505 responses were received from 24 countries. 293 respondents (58%) understand that current regulations do not limit the number of CT scans that can be prescribed for a single patient in a year. When asked whether there should be a regulation to limit the number of CT scans that can be prescribed for a single patient in one year, only a small fraction (143, 28%) answered 'No', 182 (36%) answered 'Maybe' and 166 (33%) answered 'Yes'. Most respondents (337; 67%) think that radiation risk should form part of the consideration when deciding whether to request a CT exam. A minority (138; 27%) think the decision should be based only on the medical indication for the CT exam. Comparison among the 4 countries (South Korea, Hungary, USA and Canada) with the largest number of respondents indicated wide variations in responses. CONCLUSIONS: A majority of the surveyed clinicians consider radiation risk, in addition to clinical factors, when prescribing CT exams. Most respondents are in favor of, or would consider, regulation to control the number of CT scans that could be performed on a patient annually.

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.

Objectives: In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. Methods: A total of 112 IMRT plans for chest cancers were planned and measured by portal dosimetry equipped on TrueBeam linac. The convolutional neural network (CNN) based learning model was trained using delivery fluence as inputs and gamma passing rates (GPRs) of 4 different criteria (3%/3 mm, 2%/3 mm, 3%/2 mm, and 2%/2 mm) as outputs. Model performance for both validation and test sets was assessed using mean absolute error (MAE), mean squared error (MSE), root MSE (RMSE), Spearman rank correlation coefficients (Sr), and Determination coefficient (R2) between the measured and predicted GPR values. Results: In the test set, the MAE of the prediction model were 0.402, 0.511, 1.724, and 2.530, the MSE were 0.640, 0.986, 6.654, and 9.508, the RMSE were 0.800, 0.993, 2.580, and 3.083, the Sr were 0.643, 0.684, 0.821, and 0.824 (P < .001) and the R2 were 0.4110, 0.4666, 0.6677, and 0.6769 for 3%/3 mm, 3%/2 mm, 2%/3 mm, and 2%/2 mm, respectively. The MAE and RMSE of the prediction model decreased with stricter gamma criteria while the Sr and R2 between measured and predicted GPR values increased. Conclusions: The CNN prediction model based on delivery fluence informed by log files could accurately predict IMRT QA passing rates for different gamma criteria. It could reduce QA workload and improve efficiency in pretreatment QA. Our results suggest that the CNN prediction model based on delivery fluence informed by log files may be a promising tool for the gamma evaluation of IMRT QA.

Measurement of three-dimensional track profiles on CR-39s based on the photometric stereo method.

To obtain more information about incident particles, a new method for measuring three-dimensional track profiles formed on CR-39s based on the photometric stereo method was developed. A new optical microscope system with 16 lasers and a complementary metal-oxide-semiconductor camera was built to automatically capture the reflecting track images illuminated by the laser beams from different angles, and the track profiles were three-dimensionally reconstructed using a self-developed software. To verify the reconstruction results of the track profiles, both the openings and depth were measured with an atomic force microscope. The results showed that the relative deviations between the two methods of the openings were about 5.5% and the deviations of the depth were about 8.0%. At present, the reconstruction speed of a three-dimensional track profile is a factor of 400 greater than that of the atomic force microscope. The new method shows great potential for rapid reconstruction of numerous track morphologies. It is expected to be helpful for further studies on the energy and angle discrimination of incident particles in the field of nuclear measurements.

Verification of combustion rate of recovery and counting efficiency in the analysis of organically bound tritium in biota samples.

The analysis procedure of five biota samples's organically bound tritium (OBT) based on oxidation combustion and liquid scintillation counter (LSC) measurement was established. The combustion experiment under one atmospheric pressure in the presence of Pt-Al2O3 catalyst were carried out. The experiment results shown that the combustion recovery of five samples ranged from 86.4 % to 91.1 %, the combustion recovery of glucose monohydrate is about 93.7 %, which indicate that combustion recovery of biota samples differed from one species to another. Meanwhile, The counting efficiency of quenching agents CH3NO2 and CCl4 decreases from 20.3 % to 0 and from 19.3 % to 0 respectively as the quench agent mass increases from 10 µL to 500 µL. The counting efficiency of quenching agent HNO3 decreases from 22.4 % to 14.6 % as the quench agent mass increases from 10 µL to 500 µL. The SQP (E) value of CH3NO2 and CCl4 decreases as the mass of quenching agents increases, while the SQP (E) value of HNO3 increases as the quench agent mass increases. The SQP(E) of three tested quench agents ranges from 401.8 to 738.4, which covers the SQP(E) range of all the monitored biota samples in recent years. Therefore, the mapped curves and fixed equations are applicable. In addition, comparison experiment of four biota samples between two laboratories shown a relative deviation from 1.2 % to 12.8 %.

Changes of the linear energy transfer (LET) and beam width of therapeutic carbon ion beam in density heterogeneous phantoms.

This work aims to investigate the changes in the linear energy transfer (LET) spectra distribution and the beam spot width of a therapeutic carbon ion beam in density heterogeneous phantoms. Three different heterogeneous phantoms were fabricated using a combination of solid water, lung, and bone tissue slabs and irradiated by a single energy carbon beam (276.5 MeV u-1). CR-39 detectors were used for experimental measurements and the Monte Carlo toolkit Geant4 was employed for theoretical simulations. The results demonstrated that the measured LET spectra agree well with the simulation results. The lung and bone tissues displayed no obvious effect on the spectral distribution of LET. The dose-average LET was invariant and showed no obvious difference in the different materials, while the track-average LET increased in the lung and decreased in the bone materials. Similarly, the beam spot size increased in the lung, and decreased in the bone materials. Additionally, the fluence of the secondary fragments varied in different tissues. These findings are expected to provide cross-validation data for the quality assurance of carbon ion therapy and to be beneficial for validating the base data in treatment planning systems.

Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction.

The dose verification in radiotherapy quality assurance (QA) is time-consuming and places a heavy workload on medical physicists. To provide a clinical tool to perform patient specific QA accurately, the UNet++ is investigated to classify failed or pass fields (the GPR lower than 85% is considered "failed" while the GPR higher than 85% is considered "pass"), predict gamma passing rates (GPR) for different gamma criteria, and predict dose difference from virtual patient-specific quality assurance in radiotherapy. UNet++ was trained and validated with 473 fields and tested with 95 fields. All plans used Portal Dosimetry for dose verification pre-treatment. Planar dose distribution of each field was used as the input for UNet++, with QA classification results, gamma passing rates of different gamma criteria, and dose difference were used as the output. In the test set, the accuracy of the classification model was 95.79%. The mean absolute error (MAE) were 0.82, 0.88, 2.11, 2.52, and the root mean squared error (RMSE) were 1.38, 1.57, 3.33, 3.72 for 3%/3mm, 3%/2 mm, 2%/3 mm, 2%/2 mm, respectively. The trend and position of the predicted dose difference were consistent with the measured dose difference. In conclusion, the Virtual QA based on UNet++ can be used to classify the field passed or not, predict gamma pass rate for different gamma criteria, and predict dose difference. The results show that UNet++ based Virtual QA is promising in quality assurance for radiotherapy.

Internal dosimetry in F-18 FDG PET examinations based on long-time-measured organ activities using total-body PET/CT: does it make any difference from a short-time measurement?

PURPOSE: A 2-m axial field-of-view, total-body PET/CT scanner (uEXPLORER) has been recently developed to provide total-body coverage and ultra-high sensitivity, which together, enables opportunities for in vivo time-activity curve (TAC) measurement of all investigated organs simultaneously with high temporal resolution. This study aims at quantifying the cumulated activity and patient dose of 2-[F-18]fluoro-2-deoxy-D-glucose (F-18 FDG ) imaging by using delayed time-activity curves (TACs), measured out to 8-h post-injection, for different organs so that the comparison between quantifying approaches using short-time method (up to 75 min post-injection) or long-time method (up to 8 h post-injection) could be performed. METHODS: Organ TACs of 10 healthy volunteers were collected using total-body PET/CT in 4 periods after the intravenous injection of F-18 FDG. The 8-h post-injection TACs of 6 source organs were fitted using a spline method (based on Origin (version 8.1)). To compare with cumulated activity estimated from spline-fitted curves, the cumulated activity estimated from multi-exponential curve was also calculated. Exponential curve was fitted with shorter series of data consistent with clinical procedure and previous dosimetry works. An 8-h dynamic bladder wall dose model considering 2 voiding were employed to illustrate the differences in bladder wall dose caused by the different measurement durations. Organ absorbed doses were further estimated using Medical Internal Radiation Dose (MIRD) method and voxel phantoms. RESULTS: A short-time measurement could lead to significant bias in estimated cumulated activity for liver compared with long-time-measured spline fitted method, and the differences of cumulated activity were 18.38% on average. For the myocardium, the estimated cumulated activity difference was not statistically significant due to large variation in metabolism among individuals. The average residence time differences of brain, heart, kidney, liver, and lungs were 8.38%, 15.13%, 25.02%, 23.94%, and 16.50% between short-time and long-time methods. Regarding effective dose, the maximum differences of residence time between long-time-measured spline fitted curve and short-time-measured multi-exponential fitted curve was 9.93%. When using spline method, the bladder revealed the most difference in the effective dose among all the investigated organs with a bias up to 21.18%. The bladder wall dose calculated using a long-time dynamic model was 13.79% larger than the two-voiding dynamic model, and at least 50.17% lower than previous studies based on fixed bladder content volume. CONCLUSIONS: Long-time measurement of multi-organ TACs with high temporal resolution enabled by a total-body PET/CT demonstrated that the clinical procedure with 20 min PET scan at 1 h after injection could be used for retrospective dosimetry analysis in most organs. As the bladder content contributed the most to the effective dose, a long-time dynamic model was recommended for the bladder wall dose estimation.