Proton spot dose estimation based on positron activity distributions with neural network.

BACKGROUND: Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning-based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. PURPOSE: This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U-Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. METHODS: Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one-dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. RESULTS: The U-Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5 mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5 mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3 mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. CONCLUSIONS: Both the U-Net and the Transformer have certain advantages in the proton dose estimation task. The U-Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose-guided proton therapy.

Quantitative analysis of radiosensitizing effect for magnetic hyperthermia-radiation combined therapy on prostate cancer cells.

BACKGROUND: Magnetic hyperthermia (MHT) has emerged as a promising therapeutic approach in the field of radiation oncology due to its superior precision in controlling temperature and managing the heating area compared to conventional hyperthermia. Recent studies have proposed solutions to address clinical safety concerns associated with MHT, which arise from the use of highly concentrated magnetic nanoparticles and the strong magnetic field needed to induce hyperthermic effects. Despite these efforts, challenges remain in quantifying therapeutic outcomes and developing treatment plan systems for combining MHT with radiation therapy (RT). PURPOSE: This study aims to quantitatively measure the therapeutic effect, including radiation dose enhancement (RDE) in the magnetic hyperthermia-radiation combined therapy (MHRT), using the equivalent radiation dose (EQD) estimation method. METHODS: To conduct EQD estimation for MHRT, we compared the therapeutic effects between the conventional hyperthermia-radiation combined therapy (HTRT) and MHRT in human prostate cancer cell lines, PC3 and LNCaP. We adopted a clonogenic assay to validate RDE and the radiosensitizing effect induced by MHT. The data on survival fractions were analyzed using both the linear-quadradic model and Arrhenius model to estimate the biological parameters describing RDE and radiosensitizing effect of MHRT for both cell lines through maximum likelihood estimation. Based on these parameters, a new survival fraction model was suggested for EQD estimation of MHRT. RESULTS: The newly designed model describing the MHRT effect, effectively captures the variations in thermal and radiation dose for both cell lines (R2 > 0.95), and its suitability was confirmed through the normality test of residuals. This model appropriately describes the survival fractions up to 10 Gy for PC3 cells and 8 Gy for LNCaP cells under RT-only conditions. Furthermore, using the newly defined parameter r, the RDE effect was calculated as 29% in PC3 cells and 23% in LNCaP cells. EQDMHRT calculated through this model was 9.47 Gy for PC3 and 4.71 Gy for LNCaP when given 2 Gy and MHT for 30 min. Compared to EQDHTRT, EQDMHRT showed a 26% increase for PC3 and a 20% increase for LNCaP. CONCLUSIONS: The proposed model effectively describes the changes of the survival fraction induced by MHRT in both cell lines and adequately represents actual data values through residual analysis. Newly suggested parameter r for RDE effect shows potential for quantitative comparisons between HTRT and MHRT, and optimizing therapeutic outcomes in MHRT for prostate cancer.

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automated technologies face limitations in accurately identifying dicentric chromosomes (DCs), resulting in decreased precision for radiation dose estimation. Furthermore, in the process of identifying DCs through automatic or semi-automatic methods, the resulting distribution could demonstrate under-dispersion or over-dispersion, which results in significant deviations from the Poisson distribution. In response to these issues, we developed an algorithm that employs deep learning to automatically identify chromosomes and perform fully automatic and accurate estimation of diverse radiation doses, adhering to a Poisson distribution. MATERIALS AND METHODS: The dataset utilized for the dose estimation algorithm was generated from 30 healthy donors, with samples created across seven doses, ranging from 0 to 4 Gy. The procedure encompasses several steps: extracting images for dose estimation, counting chromosomes, and detecting DC and fragments. To accomplish these tasks, we utilize a diverse array of artificial neural networks (ANNs). The identification of DCs was accomplished using a detection mechanism that integrates both deep learning-based object detection and classification methods. Based on these detection results, dose-response curves were constructed. A dose estimation was carried out by combining a regression-based ANN with the Monte-Carlo method. RESULTS: In the process of extracting images for dose analysis and identifying DCs, an under-dispersion tendency was observed. To rectify the discrepancy, classification ANN was employed to identify the results of DC detection. This approach led to satisfaction of Poisson distribution criteria by 32 out of the initial pool of 35 data points. In the subsequent stage, dose-response curves were constructed using data from 25 donors. Data provided by the remaining five donors served in performing dose estimations, which were subsequently calibrated by incorporating a regression-based ANN. Of the 23 points, 22 fell within their respective confidence intervals at p < .05 (95%), except for those associated with doses at levels below 0.5 Gy, where accurate calculation was obstructed by numerical issues. The accuracy of dose estimation has been improved for all radiation levels, with the exception of 1 Gy. CONCLUSIONS: This study successfully demonstrates a high-precision dose estimation method across a general range up to 4 Gy through fully automated detection of DCs, adhering strictly to Poisson distribution. Incorporating multiple ANNs confirms the ability to perform fully automated radiation dose estimation. This approach is particularly advantageous in scenarios such as large-scale radiological incidents, improving operational efficiency and speeding up procedures while maintaining consistency in assessments. Moreover, it reduces potential human error and enhances the reliability of results.

Accurate gamma-ray dose measurement up to 10 MeV by glass dosimeter with a sensitivity control filter for BNCT.

Glass dosimeters are very useful and convenient detection elements in radiation dosimetry. In this study, this glass dosimeter was applied to a BNCT treatment field. Boron Neutron Capture Therapy (BNCT) is a next-generation radiation therapy that can selectively kill only cancer cells. In the BNCT treatment field, both neutrons and secondary gamma-rays are generated. In other words, it is a mixed radiation field of neutrons and gamma-rays. We thus proposed a novel method to measure only gamma-ray dose in the mixed field using two RPLGD (Radiophoto-luminescence Glass Dosimeter) and two sensitivity control filters in order to control the dose response of the filtered RPLGD to be proportional to the air kerma coefficients, even if the gamma-ray energy spectrum is unknown. As the filter material iron was selected, and it was finally confirmed that reproduction of the air kerma coefficients was excellent within an error of 5.3% in the entire energy range up to 10 MeV. In order to validate this method, irradiation experiments were carried out using standard gamma-ray sources. As the result, the measured doses were in acceptably good agreement with the theoretical calculation results by PHITS. In the irradiation experiment with a volume source in a nuclear fuel storage room, the measured dose rates showed larger compared with survey meter values. In conclusion, the results of the standard sources showed the feasibility of this method, however for the volume source the dependence of the gamma-ray incident angle on the dosimeter was found to be not neglected. In the next step, it will be necessary to design a thinner filter in order to suppress the effect of the incident angle.

Integrating evolutionarily conserved mechanism of response to radiation for exploring novel Caenorhabditis elegans radiation-responsive genes for estimation of radiation dose associated with spaceflight.

During space exploration, space radiation is widely recognized as an inescapable perilous stressor, owing to its capacity to induce genomic DNA damage and escalate the likelihood of detrimental health outcomes. Rapid and reliable estimation of space radiation dose holds paramount significance in accurately assessing the health risks associated with spaceflight. However, the identification of space radiation-responsive genes, with their potential to serve as early indicators for diagnosing radiation dose associated with spaceflight, continues to pose a significant challenge. In this study, based on the evolutionarily conserved mechanism of radiation response, an in silico analysis method of homologous comparison was performed to identify the Caenorhabditis elegans orthologues of human radiation-responsive genes with possible roles in the major processes of response to radiation, and thereby to explore the potential C. elegans radiation-responsive genes for evaluating the levels of space radiation exposure. The results showed that there were 60 known C. elegans radiation-responsive genes and 211 C. elegans orthologues of human radiation-responsive genes implicated in the major processes of response to radiation. Through an investigation of all available transcriptomic datasets obtained from space-flown C. elegans, it was observed that the expression levels of the majority of these putative C. elegans radiation-responsive genes identified in this study were notably changed across various spaceflight conditions. Furthermore, this study indicated that within the identified genes, 19 known C. elegans radiation-responsive genes and 40 newly identified C. elegans orthologues of human radiation-responsive genes exhibited a remarkable positive correlation with the duration of spaceflight. Moreover, a noteworthy presence of substantial multi-collinearity among the majority of these identified genes was observed. This observation lends support to the possibility of treating each identified gene as an independent indicator of radiation dose in space. Ultimately, a subset of 15 potential radiation-responsive genes was identified, presenting the most promising indicators for estimation of radiation dose associated with spaceflight in C. elegans.

IS2aR, a computational tool to transform voxelized reference phantoms into patient-specific whole-body virtual CTs for peripheral dose estimation.

BACKGROUND: The risk of radiogenic cancer induction due to radiotherapy depends on the dose received by the patient's organs. Knowing the position of all organs is needed to assess this dose in a personalized way. However, radiotherapy planning computed tomography (pCT) scans contain truncated patient anatomy, limiting personalized dose evaluation. PURPOSE: To develop a simple and freely available computational tool that adapts an ICRP reference computational phantom to generate a patient-specific whole-body CT for peripheral dose computations. METHODS: Various bone-segmentation methods were explored onto fifteen pCTs, and the one with the highest Sørensen-Dice coefficient was implemented. The reference phantom is registered to the pCT, obtaining a registration transform matrix, which is then applied to create the whole-body virtual CT. Additional validation involved a comparison of absorbed doses to organs delineated on both the pCT and the virtual CT. RESULTS: A dedicated graphical user interface was designed and implemented to house the developed functions for i) selecting a registration region on which automatic bone segmentation and rigid registration will occur, ii) displaying the results of these processes under selectable views, and iii) exporting the final patient-specific whole-body CT. This software was termed IS2aR. The tested whole-body virtual CT generated by IS2aR fulfilled our requirements. CONCLUSIONS: IS2aR is a user-friendly computational software to create a personalized whole-body CT containing the original structures in the reference phantom. The personalized dose deposited in peripheral organs can be estimated further to assess second cancer induction risk in epidemiological studies.

Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases.

PURPOSE: Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases. METHODS: A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a preclinical porcine model (n = 6 vertebrae). RESULTS: Dose analysis was successfully performed, with generation and display of thermal dose volumes, thermal damage, dose volume histograms and isodose contours. Usability testing showed an overall positive response to the TPS as beneficial to safe and effective RFA. The in vivo porcine study showed good agreement between the manually segmented thermally damaged volumes vs. the damage volumes identified from the TPS (Dice Similarity Coefficient = 0.71 ± 0.03, Hausdorff distance = 1.2 ± 0.1 mm). CONCLUSION: A TPS specifically dedicated to RFA in the bony spine could help account for tissue heterogeneities in both thermal and electrical properties. A TPS would enable visualization of damage volumes in 2D and 3D, assisting clinicians in decisions about potential safety and effectiveness prior to performing RFA in the metastatic spine.

Impact of the Frequency and Type of Procedures Performed in Nuclear Medicine Units on the Expected Radiological Hazard.

Nuclear medicine procedures play an important role in medical diagnostics and therapy. They are related to the use of ionizing radiation, which affects the radiological exposure of all of the persons involved in their performance. The goal of the study was to estimate the doses associated with the performance of various nuclear medicine procedures in order to optimize workload management. The analysis was performed for 158 myocardial perfusion scintigraphy procedures, 24 bone scintigraphies, 9 thyroid scintigraphies (6 with use of 131I and 3 with 99mTc), 5 parathyroid glands and 5 renal scintigraphies. In this evaluation, two possible locations of thermoluminescent detectors, used for measurements, were taken into consideration: in the control room and directly next to the patient. It was shown how the radiological exposure varies depending on the performed procedure. For high activity procedures, ambient dose equivalent registered in the control room reached the level over 50% of allowed dose limit. For example, ambient dose equivalent obtained in control room when performing bone scintigraphy only was 1.13 ± 0.3 mSv. It is 68% of calculated dose limit in the examined time span. It has been shown that risk associated with nuclear medicine procedures is influenced not only by the type of procedure, but also by the frequency of their performance and compliance with the ALARA principle. Myocardial perfusion scintigraphy accounted for 79% of all evaluated procedures. The use of radiation shielding reduced the obtained doses from 14.7 ± 2.1 mSv in patient's vicinity to 1.47 ± 0.6 mSv behind the shielding. By comparing the results obtained for procedures and dose limits established by Polish Ministry of Health, it is possible to estimate what should be the optimal division of duties between staff, so that everyone receives similar doses.

A novel tool for predicting the dose distribution of non-sealed 188 Re (Rhenium) resin in non-melanoma skin cancers (NMSC) patients.

BACKGROUND: High-dose rate brachytherapy using a non-sealed 188 Rhenium resin (188 Re) is a recently approved treatment option for non-melanoma skin cancer (NMSC). The treatment goal is to deliver a personalized absorbed dose to the deepest point of neoplastic infiltration corresponding to the minimal target dose. The treatment consists of the application of a 188 Re-based resin over a plastic foil placed on the target skin surface. However, there is no treatment planning tool to assess the 188 Re activity needed for a personalized treatment. PURPOSE: The paper aims to present a novel Monte Carlo (MC)-based tool for 188 Re-based resin activity and dose calculation, experimentally validated using Gafchromic EBT3 films. METHODS: MC simulations were carried out using FLUKA modeling density and composition of 188 Re resin. The MC-based look up table (LUT) was incorporated in an ad hoc developed tool. The proposed tool allows the personalized calculation of treatment parameters (i.e., activity to be dispensed, the treatment duration, and dose volume histograms), according to the target dimension. The proposed tool was compared using Bland-Altman analysis to the previous calculation approaches conducted using VARSKIN in a retrospective cohort of 76 patients. The tool was validated in ad hoc experimental set ups using a stack of calibrated Gafchromic EBT3 films covered by a plastic film and exposed using a homogenous activity distribution of 188 Re eluate and a heterogeneous activity distribution of 188 Re resin mimic the patient treatment. RESULTS: The agreement between the proposed tool and VARSKIN was evaluated on the investigated cohort with median range of target area, target depth, and treatment time equal to 4.8 [1.0-60.1] cm2 , 1.1 [0.2-3.0] mm, and 70 [21-285] min, with a median range of target dose (Gy) of 23.5 [10-54.9]. The calculated minimal target doses, ranged from 1% to 10% for intermediate target depths (1.2 ± 0.7 mm), while showing significant differences in the estimation of superficial (maximal) target doses. The agreement between MC calculation and measurements at different plans in a stack of Gafchromic EBT3 films was within 10% for both the homogenous and heterogeneous activity distribution of 188 Re. Worst agreements were observed for absorbed doses lower than 0.3 Gy. CONCLUSIONS: Our results support the implementation of our MC-based tool in the practical routine for calculating the 188 Re resin activity and treatment parameters necessary for obtaining the prescribed minimal target dose.

Evaluation of dose accumulation methods and workflows utilising cone beam computed tomography images.

INTRODUCTION: Various adaptive radiation therapy (ART) methods have emerged, with little consensus amongst the literature as to which is most appropriate. This study aimed to compare dose mapping (DM) versus Monte Carlo recalculation (MCR), using cone beam computed tomography (CBCT) images when utilised in automated ART dose accumulation workflows in the MIM Maestro software package. METHODS: The treatment plans for 38 cancer patients (19 prostate and 19 head and neck cases) were used to perform DM or MCR retrospectively upon CBCTs acquired during treatment, which were then deformably registered to the planning CT (DR-pCT) to facilitate dose accumulation. Dose-volume and region-of-interest data were extracted for the planning target volumes and organs at risk. Intraclass correlation (ICC) values and Bland-Altman plots were utilised to compare DM versus MCR doses on the CBCT images as well as CBCT versus DR-pCT doses. RESULTS: When comparing DM and MCR on CBCTs, the differences across dose level mean dose differences were mostly within a ±5% level of agreement based on the Bland-Altman plots, with over 67% of ICC values over 0.9 and indicative of good correlation. When these distributions were deformed back to the planning CT, the agreement was reduced considerably, with larger differences (exceeding ±5%) resulting from workflow-related issues. CONCLUSION: The results emphasise the need to consider and make adaptations to minimise the effect of workflows on algorithm performance. Manual user intervention, refined departmental protocols and further developments to the MIM Maestro software will enhance the use of this tool.

An analysis of the investigation results of radionuclide content in food in Chongqing, China / 中国辐射卫生

Objective To investigate the radionuclide content in food in Chongqing, China by conducting a survey on the radioactivity in food. Methods A total of 114 samples of vegetables, grain, milk powder, and tea were collected in Chongqing. The samples were dried, pulverized into powder, added into Marinelli beakers, and then measured for radionuclides using a high-purity germanium gamma spectrometer (GEM40P4-765). Results The mean activity concentrations of natural radionuclides 238U, 232Th, 226Ra, and 40K in food in Chongqing were (0.396 ± 0.510), (0.199 ± 0.296), (0.140 ± 0.209), and (119.250 ± 105.470) Bq/kg, respectively. The contents of radionuclides in different foods were significantly different (P < 0.05). The mean activity concentration of the artificial radionuclide 137Cs was (0.091 ± 0.308) Bq/kg, and the mean activity concentration of 90Sr measured in nine tea samples was (1.243 ± 0.860) Bq/kg. Conclusion The contents of radionuclides in food in Chongqing are lower than the national standard limits, but the safety of radioactivity in food still needs to be taken seriously, and long-term surveillance of radioactivity in food is needed.

Dose estimation in etiology of occupational radiogenic neoplasms: A case analysis / 中国辐射卫生

Objective To analyze the problems and solutions in the diagnosis of a patient with occupational radiogenic neoplasms. Methods The dose conversion method was selected in dose estimation. Personal dose equivalent, skin absorbed dose, and reported detection data were converted into red bone marrow absorbed dose. The upper 95% confidence limit of the probability of causation (PC 95%) was calculated. Results The PC 95% of cancer due to radiation in the worker was 66.38%, which suggested occupational radiogenic neoplasms. Personal dose data were missing in dose estimation. The current dose estimation standard lacked bedside radiography and CT operation type, and the dose conversion formula was not perfect. Conclusion In the judgment of occupational radiogenic neoplasms, the estimated dose showed uncertainty. There is an urgent need to formulate and promulgate dose estimation standards that are operational and in line with the current development of radiological diagnosis and treatment technology and equipment.

Estimation of the effective radiation dose for tunnel construction workers / 中国职业医学

Objective To analyze and set up the effective dose of different ionizing radiation for tunnel construction workers. Methods A total of five tunnels constructed using drilling and blasting methods were selected as the research subjects using the convenient sampling method. The workplace γ radiation effective dose, radon concentrations, and radioactive activity concentrations were detected, and on-site surveys were conducted to estimate the internal and external irradiation doses and total effective doses for workers in different work sites. Results Radiological hazards in tunnels constructed using drilling and blasting methods included radon and its progeny, γ radiation, radioactive dust (uranium-238, radium-226, thorium-232, and potassium-40) and others. The average total effective dose of ionizing radiation exposure for tunnel construction workers was (6.730 1±1.541 1) mSv. The average dose of radon and its progeny was (6.163 0±1.512 8) mSv, radioactive dust was (0.014 6±0.009 1) mSv, γ radiation was (0.552 6±0.138 7) mSv. The dose of radioactive dust of radon and its progeny was 0.24%. Radon and its progeny contributed more to the radioactive dose than radioactive dust and γ radiation (all P<0.05). Among all the radioactive dusts, the dose contribution ranked from highest to lowest was thorium-232, uranium-238, and radium-226. Conclusion For tunnel construction workers, the largest contribution to the effective dose of ionizing radiation exposure is from radon and its progeny for internal irradiation, followed by γ radiation for external irradiation. The contribution of radioactive dust to internal irradiation dose can be considered negligible.

Research progress of imaging flow cytometry in automatic analysis of radiation biodosimetric indicators / 中国辐射卫生

@#A large number of people would be exposed to irradiation in large-scale nuclear and radiation accidents or nuclear terrorist attacks. Therefore, it is urgent to establish rapid and high-throughput biodosimetry for in triage, providing a basis for emergency management. Imaging flow cytometry (IFC) possesses the high through put advantages of traditional flow cytometry and the sensitivity and specificity of microscope, and has a good application prospect in the research and development of rapid, automated, and high-throughput biological dose estimation technology. This article reviews the application progress of IFC in biodosimetry, and provides a reference for the development of biological dose estimation and detection equipment for large-scale nuclear and radiation accidents.

Oxidative Modification Status of Human Serum Albumin Caused by Chronic Low-Dose Radiation Exposure in Mamuju, Sulawesi, Indonesia.

The recently discovered high-level natural background radiation area (HBRA) of Mamuju in Indonesia provides a unique opportunity to study the biological effects of chronic low-dose radiation exposure on a human population. The mean total effective dose in the HBRA was approximately 69.6 mSv y-1 (range: 47.1 to 115.2 mSv y-1), based on a re-evaluation of the individual radiation exposure dose; therefore, proteomic analyses of serum components and oxidative modification profiling of residents living in the HBRA were reconducted using liquid chromatography-tandem mass spectrometry. The analysis of the oxidative modification sequences of human serum albumin revealed significant moderate correlations between the radiation dose and the modification of 12 sequences, especially the 111th methionine, 162nd tyrosine, 356th tyrosine, and 470th methionine residues. In addition, a dose-dependent variation in 15 proteins of the serum components was detected in the serum of residents exposed to chronic low-dose radiation. These findings suggest that the alterations in the expression of specific proteins and the oxidative modification responses of serum albumin found in exposed humans may be important indicators for considering the effects of chronic low-dose radiation exposure on living organisms, implying their potential utility as biomarkers of radiation dose estimation.

Estimation of radiation dose and establishment of local diagnostic reference levels for computed tomography of head in pediatric population.

BACKGROUND: Pediatric population is more sensitive to the effects of radiation than adults. Establishing diagnostic reference level (DRL) is an efficient dose optimization technique implemented by many countries for reducing radiation dose during Computed Tomography (CT) examinations. OBJECTIVES: To estimate radiation dose and establish a new local diagnostic reference level for CT head examination in the pediatric population. MATERIALS AND METHODS: We prospectively recruited 143 pediatric patients referred for CT head examination with age ranging from 0-5 years old. All patients had undergone CT head examination using the standard pediatric head protocol. Volumetric CT dose index (CTDIvol) and dose length product (DLP) were recorded. The effective dose was first calculated. Then, 75th percentile of dose indices was calculated to establish DRLs. RESULTS: DRLs in terms of CTDIvol and DLP are 23.84 mGy, 555.99 mGy.cm for patients <1 years old and 28.65 mGy, 794.99 mGy.cm for patients from 1-5 years old, respectively. Mean effective doses for <1 years old patients and 1-5 years old patients are 2.91 mSv and 2.78 mSv respectively. CONCLUSION: The study concludes that DRL in terms of CTDIvol is lower but DRL in terms of DLP and the effective dose is higher compared to a few other studies which necessitate the need for dose optimization.

Knowledge-based three-dimensional dose prediction for tandem-and-ovoid brachytherapy.

PURPOSE: The purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator. METHODS: A 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20-130% of prescription, mean (SD) and standard deviation (σ), as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram (DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%; bladder, rectum, and sigmoid D2cc) with ΔDx=Dx,actual-Dx,predicted mean, standard deviation, and Pearson correlation coefficient further quantifying model performance. RESULTS: Ranges of voxel-wise dose difference accuracy (δD¯±σ) for 20-130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV ΔD¯90±σΔD = -0.19 ± 0.55Gy (-0.09 ± 0.67 Gy), bladder ΔD¯2cc±σΔD = -0.06 ± 0.54Gy (-0.17 ± 0.67 Gy), rectum ΔD¯2cc±σΔD= -0.03 ± 0.36Gy (-0.04 ± 0.46 Gy), and sigmoid ΔD¯2cc±σΔD = -0.01 ± 0.34Gy (0.00 ± 0.44 Gy). CONCLUSIONS: A 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance.

[Experiment of a Dedicated Lead Slit for X-ray Output Analyzer in X-ray CT Half-value Layer Measurements].

It is not easy to measure the half-value layer (HVL) of CT because it is necessary to place the X-ray tube position fixed. The aim of this study was to experiment the new methods of HVL measuring of CT using a custom-made lead slit. The custom-made lead slit method allowed the HVL measuring of CT while the rotation of the X-ray tube. The error of HVL value using the custom-made lead slit method compared to the conventional method was within 6%. The custom-made lead slit method can enable to measure the HVL of CT easily without the X-ray tube position fixed.

Real-time estimation of patient-specific dose distributions for medical CT using the deep dose estimation.

PURPOSE: With the rising number of computed tomography (CT) examinations and the trend toward personalized medicine, patient-specific dose estimates are becoming more and more important in CT imaging. However, current approaches are often too slow or too inaccurate to be applied routinely. Therefore, we propose the so-called deep dose estimation (DDE) to provide highly accurate patient dose distributions in real time METHODS: To combine accuracy and computational performance, the DDE algorithm uses a deep convolutional neural network to predict patient dose distributions. To do so, a U-net like architecture is trained to reproduce Monte Carlo simulations from a two-channel input consisting of a CT reconstruction and a first-order dose estimate. Here, the corresponding training data were generated using CT simulations based on 45 whole-body patient scans. For each patient, simulations were performed for different anatomies (pelvis, abdomen, thorax, head), different tube voltages (80 kV, 100 kV, 120 kV), different scan trajectories (circle, spiral), and with and without bowtie filtration and tube current modulation. Similar simulations were performed using a second set of eight whole-body CT scans from the Visual Concept Extraction Challenge in Radiology (Visceral) project to generate testing data. Finally, the DDE algorithm was evaluated with respect to the generalization to different scan parameters and the accuracy of organ dose and effective dose estimates based on an external organ segmentation. RESULTS: DDE dose distributions were quantified in terms of the mean absolute percentage error (MAPE) and a gamma analysis with respect to the ground truth Monte Carlo simulation. Both measures indicate that DDE generalizes well to different scan parameters and different anatomical regions with a maximum MAPE of 6.3% and a minimum gamma passing rate of 91%. Evaluating the organ dose values for all organs listed in the International Commission on Radiological Protection (ICRP) recommendation, shows an average error of 3.1% and maximum error of 7.2% (bone surface). CONCLUSIONS: The DDE algorithm provides an efficient approach to determine highly accurate dose distributions. Being able to process a whole-body CT scan in about 1.5 s, it provides a valuable alternative to Monte Carlo simulations on a graphics processing unit (GPU). Here, the main advantage of DDE is that it can be used on top of any existing Monte Carlo code such that real-time performance can be achieved without major adjustments. Thus, DDE opens up new options not only for dosimetry but also for scan and protocol optimization.