CT number calibration audit in photon radiation therapy.

BACKGROUND: Inadequate computed tomography (CT) number calibration curves affect dose calculation accuracy. Although CT number calibration curves registered in treatment planning systems (TPSs) should be consistent with human tissues, it is unclear whether adequate CT number calibration is performed because CT number calibration curves have not been assessed for various types of CT number calibration phantoms and TPSs. PURPOSE: The purpose of this study was to investigate CT number calibration curves for mass density (ρ) and relative electron density (ρe ). METHODS: A CT number calibration audit phantom was sent to 24 Japanese photon therapy institutes from the evaluating institute and scanned using their individual clinical CT scan protocols. The CT images of the audit phantom and institute-specific CT number calibration curves were submitted to the evaluating institute for analyzing the calibration curves registered in the TPSs at the participating institutes. The institute-specific CT number calibration curves were created using commercial phantom (Gammex, Gammex Inc., Middleton, WI, USA) or CIRS phantom (Computerized Imaging Reference Systems, Inc., Norfolk, VA, USA)). At the evaluating institute, theoretical CT number calibration curves were created using a stoichiometric CT number calibration method based on the CT image, and the institute-specific CT number calibration curves were compared with the theoretical calibration curve. Differences in ρ and ρe over the multiple points on the curve (Δρm and Δρe,m , respectively) were calculated for each CT number, categorized for each phantom vendor and TPS, and evaluated for three tissue types: lung, soft tissues, and bones. In particular, the CT-ρ calibration curves for Tomotherapy TPSs (ACCURAY, Sunnyvale, CA, USA) were categorized separately from the Gammex CT-ρ calibration curves because the available tissue-equivalent materials (TEMs) were limited by the manufacturer recommendations. In addition, the differences in ρ and ρe for the specific TEMs (ΔρTEM and Δρe,TEM , respectively) were calculated by subtracting the ρ or ρe of the TEMs from the theoretical CT-ρ or CT-ρe calibration curve. RESULTS: The mean ± standard deviation (SD) of Δρm and Δρe,m for the Gammex phantom were -1.1 ± 1.2 g/cm3 and -0.2 ± 1.1, -0.3 ± 0.9 g/cm3 and 0.8 ± 1.3, and -0.9 ± 1.3 g/cm3 and 1.0 ± 1.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm and Δρe,m for the CIRS phantom were 0.3 ± 0.8 g/cm3 and 0.9 ± 0.9, 0.6 ± 0.6 g/cm3 and 1.4 ± 0.8, and 0.2 ± 0.5 g/cm3 and 1.6 ± 0.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm for Tomotherapy TPSs was 2.1 ± 1.4 g/cm3 for soft tissues, which is larger than those for other TPSs. The mean ± SD of Δρe,TEM for the Gammex brain phantom (BRN-SR2) was -1.8 ± 0.4, implying that the tissue equivalency of the BRN-SR2 plug was slightly inferior to that of other plugs. CONCLUSIONS: Latent deviations between human tissues and TEMs were found by comparing the CT number calibration curves of the various institutes.

Delivery accuracy of VMAT on two beam-matched linacs provided by accelerated go live service.

INTRODUCTION: Dosimetric accuracy is critical when a patient treated with volumetric modulated arc therapy (VMAT) is transferred to another beam-matched linac. To evaluate the performance of Accelerated Go Live (AGL) service, the measured beam characteristics and patient specific quality assurance (QA) results between two AGL-matched linacs were compared. MATERIALS AND METHODS: Two VersaHD linacs were installed using the AGL service. After the installation, the beam data such as percentage depth dose (PDD), lateral profiles and output factors for all photon beams were measured. Relative doses were also measured as a function of the multi-leaf collimator (MLC) leaf gap width. Subsequently, VMAT plans were created for prostate, pelvis, head and neck, liver, lung cancers and multiple brain metastases. Dose distributions and point doses were measured by multi-dimensional detectors and ionization chambers for patient specific quality assurance, and comparisons were made between the two linacs. RESULTS: Dose differences in PDDs were all within ± 1% except the entrance region, and the averaged gamma indices of the lateral profiles were within 0.3. The differences in doses as a function of the MLC leaf gap width between the two linacs were within ±0.5%. For all the plans, gamma passing rates were all higher than 95% with criteria of 2%/2 mm. The average and the SD of dose differences on the multi-dimensional detector between both measurements was 0.06 ± 2.12%, and the average of point dose differences was -0.03 ± 0.33%. CONCLUSION: We have evaluated the AGL performance in the context of beam characteristics and patient specific QA. It was demonstrated that the AGL service provides an accurate VMAT treatment reproducibility for many tumor sites with gamma pass rates greater than 95% under criteria of 2%/2 mm.

A new workflow of the on-line 1.5-T MR-guided adaptive radiation therapy.

PURPOSE: The aim of this study was to develop a new workflow for 1.5-T magnetic resonance (MR)-guided on-line adaptive radiation therapy (MRgART) and assess its feasibility in achieving dose constraints. MATERIALS AND METHODS: We retrospectively evaluated the clinical data of patients who underwent on-line adaptive radiation therapy using a 1.5-T MR linear accelerator (MR-Linac). The workflow in MRgART was established by reviewing the disease site, number of fractions, and re-planning procedures. Five cases of prostate cancer were selected to evaluate the feasibility of the new workflow with respect to achieving dose constraints. RESULTS: Between December 2021 and September 2022, 50 consecutive patients underwent MRgART using a 1.5-T MR-Linac. Of these, 20 had prostate cancer, 10 had hepatocellular carcinoma, 6 had pancreatic cancer, 5 had lymph node oligo-metastasis, 3 had renal cancer, 3 had bone metastasis, 2 had liver metastasis from colon cancer, and 1 had a mediastinal tumor. Among a total of 247 fractions, 235 (95%) were adapt-to-shape (ATS)-based re-planning. The median ATS re-planning time in all 50 cases was 17 min. In the feasibility study, all dose constraint sets were met in all 5 patients by ATS re-planning. Conversely, a total of 14 dose constraints in 5 patients could not be achieved by virtual plan without using adaptive re-planning. These dose constraints included the minimum dose received by the highest irradiated volume of 1 cc in the planning target volume and the maximum dose of the rectal/bladder wall. CONCLUSION: A new workflow of 1.5-T MRgART was established and found to be feasible. Our evaluation of the dose constraint achievement demonstrated the effectiveness of the workflow.

[A Survey of Dosimetry in the Magnetic Field of MR-Linacs].

In recent years, MR-Linac, a radiotherapy linear accelerator (linac) equipped with magnetic resonance (MR) imaging, has been deployed in clinical facilities across Japan. Because of the magnetic field of MR-Linac, which can affect the dose distributions and dose response of ionization chambers, conventional reference dosimetry for absorbed dose to water using an ionization chamber becomes impractical. Consequently, the magnetic field effect should be considered in the reference dosimetry for MR-Linac. Although numerous studies have delved into this matter and several magnetic field correction methods have been proposed to extend the conventional formalism, a practical protocol for reference dosimetry for MR-Linac remains elusive.The purpose of this review are as follows: (i) to summarize and evaluate literature and existing datasets as well as identify any gaps that highlight areas for the future research on this topic; (ii) to elucidate dosimetric challenges associated with ionization chamber dosimetry in magnetic fields; and (iii) to propose a formalism for reference dosimetry for MR-Linac based on available literature and datasets. This review focuses on studies based on commercially available MR-Linacs and datasets, specifically tailored for reference-class cylindrical ion chambers.

Practical guidelines of online MR-guided adaptive radiotherapy.

The first magnetic resonance (MR)-guided radiotherapy system in Japan was installed in May 2017. Implementation of online MR-guided adaptive radiotherapy (MRgART) began in February 2018. Online MRgART offers greater treatment accuracy owing to the high soft-tissue contrast in MR-images (MRI), compared to that in X-ray imaging. The Japanese Society for Magnetic Resonance in Medicine (JSMRM), Japan Society of Medical Physics (JSMP), Japan Radiological Society (JRS), Japanese Society of Radiological Technology (JSRT), and Japanese Society for Radiation Oncology (JASTRO) jointly established the comprehensive practical guidelines for online MRgART. These guidelines propose the essential requirements for clinical implementation of online MRgART with respect to equipment, personnel, institutional environment, practice guidance, and quality assurance/quality control (QA/QC). The minimum requirements for related equipment and QA/QC tools, recommendations for safe operation of MRI system, and the implementation system are described. The accuracy of monitor chamber and detector in dose measurements should be confirmed because of the presence of magnetic field. The ionization chamber should be MR-compatible. Non-MR-compatible devices should be used in an area that is not affected by the static magnetic field (outside the five Gauss line), and their operation should be checked to ensure that they do not affect the MR image quality. Dose verification should be performed using an independent dose verification system that has been confirmed to be reliable through commissioning. This guideline proposes the checklists to ensure the safety of online MRgART. Successful clinical implementation of online MRgART requires close collaboration between physician, radiological technologist, nurse, and medical physicist.

[Introduction of Young Researchers' Association of Medical Physics and Questionnaire Survey on Medical Physics Education and Medical Physics Training System in Japan for Young Researchers in 2021].

Details of Young Researchers' Association of Medical Physics (YRAMP) was introduced. In addition, several questionnaire surveys on medical physics education (MPE) or medical physicist training system (MPTS) in Japan have been conducted, none have targeted the current status and issues of MPE and MPTS. The purpose of this study was to investigate those from the perspective of researchers and students under 35-year-old (y.o.). The questionnaire survey was conducted between 14th September to 14th October 2021, for 112 members of the Young Researchers' Association of Medical Physics via Google Forms. The questionnaire was in two parts: MPE (Part1) and MPTS (Part2). Three subparts were constructed in Part1: Classroom lecture, Clinical training, Education course accredited by Japanese Board of Medical Physicist Qualification. Out of a total of 126 questions, 38 were mandatory to be answered. No personal information was collected. Ninety-three members (83.0%) were answered. The age structure of the respondents was as follows: 18-21, 22-26, 27-30, and 31-35 y.o.=5.4%, 36.6%, 39.8%, and 18.2%. Of the respondents, 74.2% and 11.8% answered that they first heard of "medical physics" or "medical physicist" when they were undergraduate students and in high school or younger, respectively. In Classroom lecture, 61.3%, 17.2%, and 21.5% of the respondents answered that they were "satisfied" or "moderately satisfied", "dissatisfied" or "moderately dissatisfied", and "Not sure" with the current MPE, respectively. In Clinical training, Education course, and MPTS, 58.1%, 21.5%, and 20.4% of the respondents answered that they were "satisfied" or "moderately satisfied", "dissatisfied" or "moderately dissatisfied", and "Not sure", respectively. In both MPE and MPTS, approximately 88% and 51% of the respondents answered that "holding lectures and study sessions for high school and undergraduate students" and "utilizing YouTube" would be useful in promoting MPE and MPTS in Japan, respectively. The results of the questionnaire survey will provide useful data for MPE and MPTS in Japan.

Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

PURPOSE: Adaptive radiotherapy requires auto-segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto-segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN cancer for MR-guided radiotherapy (MRgRT). MATERIAL AND METHODS: In the current study, we used a dataset from the American Association of Physicists in Medicine MRI Auto-Contouring (RT-MAC) Grand Challenge 2019. Specifically, eight structures in the MR images of HN region, namely submandibular glands, lymph node level II and level III, and parotid glands, were segmented with the deep learning models using a GAN and a fully convolutional network with a U-net. These images were compared with the clinically used atlas-based segmentation. RESULTS: The mean Dice similarity coefficient (DSC) of the U-net and GAN models was significantly higher than that of the atlas-based method for all the structures (p < 0.05). Specifically, the maximum Hausdorff distance (HD) was significantly lower than that in the atlas method (p < 0.05). Comparing the 2.5D and 3D U-nets, the 3D U-net was superior in segmenting the organs at risk (OAR) for HN patients. The DSC was highest for 0.75-0.85, and the HD was lowest within 5.4 mm of the 2.5D GAN model in all the OARs. CONCLUSIONS: In the current study, we investigated the auto-segmentation of the OAR for HN patients using U-net and GAN models on MR images. Our proposed model is potentially valuable for improving the efficiency of HN RT treatment planning.

Stepwise deep neural network (stepwise-net) for head and neck auto-segmentation on CT images.

OBJECTIVE: The current study aims to propose the auto-segmentation model on CT images of head and neck cancer using a stepwise deep neural network (stepwise-net). MATERIAL AND METHODS: Six normal tissue structures in the head and neck region of 3D CT images: Brainstem, optic nerve, parotid glands (left and right), and submandibular glands (left and right) were segmented with deep learning. In addition to a conventional convolutional neural network (CNN) on U-net, a stepwise neural network (stepwise-network) was developed. The stepwise-network was based on 3D FCN. We designed two networks in the stepwise-network. One is identifying the target region for the segmentation with the low-resolution images. Then, the target region is cropped, which used for the input image for the prediction of the segmentation. These were compared with a clinical used atlas-based segmentation. RESULTS: The DSCs of the stepwise-net was significantly higher than the atlas-based method for all organ at risk structures. Similarly, the JSCs of the stepwise-net was significantly higher than the atlas-based methods for all organ at risk structures. The Hausdorff distance (HD) was significantly smaller than the atlas-based method for all organ at-risk structures. For the comparison of the stepwise-net and U-net, the stepwise-net had a higher DSC and JSC and a smaller HD than the conventional U-net. CONCLUSIONS: We found that the stepwise-network plays a role is superior to conventional U-net-based and atlas-based segmentation. Our proposed model that is a potentially valuable method for improving the efficiency of head and neck radiotherapy treatment planning.

Baseline drift vector of multiple points on body surface using a near-infrared camera.

The purpose of this study was to extract the three-dimensional (3D) vector of the baseline drift baseline drift vector (BDV) of the specific points on the body surface and to demonstrate the importance of the 3D tracking of the body surface. Our system consisted of a near-infrared camera (NIC: Kinect V2) and software that recognized and tracked blue stickers as markers. We acquired 3D coordinates of 30 markers stuck on the body surface for 30 min for eight healthy volunteers and developed a simple technique to extract the BDV. The BDV on the sternum, rib, and abdomen was extracted from the measured data. BDV per min. was analyzed to estimate the frequency to exceed a given tolerance. Also, the correlation among BDVs for multiple body sites was analyzed. The longitudinal baseline drift was observed in the BDV of healthy volunteers. Among the eight volunteers, the maximum probability that the BDV per min. exceeded the tolerance of 1 mm and 2 mm was 30% and 15%, respectively. The correlation among BDVs of multiple body sites suggested a potential feasibility to distinguish the translational movement of the whole area and the respiratory movement. In conclusion, we constructed the 3D tracking system of multiple points on the body surface using a noninvasive NIC at a low cost and established the method to extract the BDV. The existence of the longitudinal baseline drift showed the importance of the 3D tracking in the body surface.

Study of feasible and safe condition for total body irradiation using cardiac implantable electronic devices.

Cardiac implantable electronic devices (CIEDs) were believed to have a tolerance dose and that direct irradiation has to be avoided. Thus, no clinical guidelines have mentioned the feasibility of total body irradiation (TBI) with a CIED directly. The purpose of this work was to study a feasible and safe condition for TBI using a CIED. Eighteen CIEDs were directly irradiated by a 6-MV X-ray beam, where a non-neutron producible beam was employed for the removal of any neutron contribution to CIED malfunction. Irradiation up to 10 Gy in accumulated dose was conducted with a 100-cGy/min dose rate, followed by up to 20 Gy at 200 cGy/min. An irradiation test of whether inappropriate ventricular shock therapy was triggered or not was also performed by using a 6-MV beam of 5, 10, 20 and 40 cGy/min to two CIEDs. No malfunction was observed during irradiation up to 20 Gy at 100 and 200 cGy/min without activation of shock therapy. These results were compared with typical TBI, suggesting that a CIED in TBI will not encounter malfunction because the prescribed dose and the dose rate required for TBI are much safer than those used in this experiment. Several inappropriate shock therapies were, however, observed even at 10 cGy/min if activated. The present result suggested that TBI was feasible and safe if a non-neutron producible beam was employed at low dose-rate without activation of shock therapy, where it was not inconsistent with clinical and non-clinical data in the literature. The feasibility of TBI while using a CIED was discussed for the first time.

Plastic scintillation dosimeter with a conical mirror for measuring 3D dose distribution.

PURPOSE: To test the measurement technique of the three-dimensional (3D) dose distribution measured image by capturing the scintillation light generated using a plastic scintillator and a scintillating screen. METHODS: Our imaging system constituted a column shaped plastic scintillator covered by a Gd2 O2 S:Tb scintillating screen, a conical mirror and a cooled CCD camera. The scintillator was irradiated with 6 MV photon beams. Meanwhile, the irradiated plan was prepared for the static field plans, two-field plan (2F plan) and the conformal arc plan (CA plan). The 2F plan contained 16 mm2 and 10 mm2 fields irradiated from gantry angles of 0° and 25°, respectively. The gantry was rotated counterclockwise from 45° to 315° for the CA plan. The field size was then obtained as 10 mm2 . A Monte Carlo simulation was performed in the experimental geometry to obtain the calculated 3D dose distribution as the reference data. Dose response was acquired by comparing between the reference and the measurement. The dose rate dependence was verified by irradiating the same MU value at different dose rates ranging from 100 to 600 MU/min. Deconvolution processing was applied to the measured images for the correction of light blurring. The measured 3D dose distribution was reconstructed from each measured image. Gamma analysis was performed to these 3D dose distributions. The gamma criteria were 3% for the dose difference, 2 mm for the distance-to-agreement and 10% for the threshold. RESULTS: Dose response for the scintillation light was linear. The variation in the light intensity for the dose rate ranging from 100 to 600 MU/min was less than 0.5%, while our system presents dose rate independence. For the 3D dose measurement, blurring of light through deconvolution processing worked well. The 3D gamma passing rate (3D GPR) for the 10 × 10 mm2 , 16 × 16 mm2 , and 20 × 20 mm2 fields were observed to be 99.3%, 98.8%, and 97.8%, respectively. Reproducibility of measurement was verified. The 3D GPR results for the 2F plan and the CA plan were 99.7% and 100%, respectively. CONCLUSIONS: We developed a plastic scintillation dosimeter and demonstrated that our system concept can act as a suitable technique for measuring the 3D dose distribution from the gamma results. In the future, we will attempt to measure the 4D dose distribution for clinical volumetric modulated arc radiation therapy (VMAT)-SBRTplans.

Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC). METHODS: In total, 236 patients who underwent brain-perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U-Net (U-NetAC), respectively. ChangAC, AutoencoderAC, and U-NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed-rank sum test and Bland-Altman analysis. RESULTS: U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P < 0.001). Bland-Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30-40% in all brain regions. AutoencoderAC and U-NetAC produced mean errors of <1% and maximum errors of 3%, respectively. CONCLUSION: New deep learning-based AC methods for AutoencoderAC and U-NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U-NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo-CT images. To verify our models' generalizability, external validation is required.

Three-dimensional dose-distribution measurement of therapeutic carbon-ion beams using a ZnS scintillator sheet.

The accurate measurement of the 3D dose distribution of carbon-ion beams is essential for safe carbon-ion therapy. Although ionization chambers scanned in a water tank or air are conventionally used for this purpose, these measurement methods are time-consuming. We thus developed a rapid 3D dose-measurement tool that employs a silver-activated zinc sulfide (ZnS) scintillator with lower linear energy transfer (LET) dependence than gadolinium-based (Gd) scintillators; this tool enables the measurement of carbon-ion beams with small corrections. A ZnS scintillator sheet was placed vertical to the beam axis and installed in a shaded box. Scintillation images produced by incident carbon-ions were reflected with a mirror and captured with a charge-coupled device (CCD) camera. A 290 MeV/nucleon mono-energetic beam and spread-out Bragg peak (SOBP) carbon-ion passive beams were delivered at the Gunma University Heavy Ion Medical Center. A water tank was installed above the scintillator with the water level remotely adjusted to the measurement depth. Images were recorded at various water depths and stacked in the depth direction to create 3D scintillation images. Depth and lateral profiles were analyzed from the images. The ZnS-scintillator-measured depth profile agreed with the depth dose measured using an ionization chamber, outperforming the conventional Gd-based scintillator. Measurements were realized with smaller corrections for a carbon-ion beam with a higher LET than a proton. Lateral profiles at the entrance and the Bragg peak depths could be measured with this tool. The proposed method would make it possible to rapidly perform 3D dose-distribution measurements of carbon-ion beams with smaller quenching corrections.

Verification system for intensity-modulated radiation therapy with scintillator.

In the preparation of intensity-modulated radiation therapy (IMRT), patient-specific verification is widely employed to optimize the treatment. To accurately estimate the accumulated dose and obtain the field-by-field or segment-by-segment verification, an original IMRT verification tool using scintillator light and an analysis workflow was developed in this study. The raw light distribution was calibrated with respect to the irradiated field size dependency and light diffusion in the water. The calibrated distribution was converted to dose quantity and subsequently compared with the results of the clinically employed plan. A criterion of 2-mm dose-to-agreement and 3% dose difference was specified in the gamma analysis with a 10% dose threshold. By applying the light diffusion calibration, the maximum dose difference was corrected from 7.7 cGy to 3.9 cGy around the field edge for a 60 cGy dose, 7 × 7 cm2 irradiation field, and 10 MV beam energy. Equivalent performance was confirmed in the chromodynamic film. The average dose difference and gamma pass rate of the accumulated dose distributions in six patients were 0.8 ± 4.5 cGy and 97.4%, respectively. In the field-by-field analysis, the average dose difference and gamma pass rate in seven fields of Patient 1 were 0.2 ± 1.2 cGy and 93.9%, respectively. In the segment-by-segment analysis, the average dose difference and gamma pass rate in nine segments of Patient 1 and a 305° gantry angle were - 0.03 ± 0.2 cGy and 93.9%, respectively. This system allowed the simultaneous and independent analysis of each field or segment in the accumulated dose analysis.

Feasibility of image quality improvement for high-speed CBCT imaging using deep convolutional neural network for image-guided radiotherapy in prostate cancer.

PURPOSE: High-speed cone-beam computed tomography (CBCT) scan for image-guided radiotherapy (IGRT) can reduce both the scan time and the exposure dose. However, it causes noise and artifacts in the reconstructed images due to the lower number of acquired projection data. The purpose of this study is to improve the image quality of high-speed CBCT using a deep convolutional neural network (DCNN). METHODS: CBCT images of 36 prostate cancer patients were selected. The CBCT images acquired at normal scan speed were defined as CBCT100%. Simulated high-speed CBCT images acquired at twofold and fourfold scan speed were created, which were defined as CBCT50% and CBCT25%, respectively. The image quality of the CBCT50% was treated as the requirement for IGRT in this study because previous studies reported that its image is sufficient with respect to IGRT. The DCNN model was trained to learn direct mapping from CBCT25% to the corresponding CBCT100%. The performance of the DCNN model was evaluated using the sixfold cross-validation method. CBCT images generated by DCNN (CBCT25%+DCNN) were evaluated for voxel value accuracy and image quality. RESULTS: The DCNN model can process CBCT25% of a new patient within 0.06 s/slice. The CBCT25%+DCNN was comparable to the CBCT50% in terms of both voxel value accuracy and image quality. CONCLUSIONS: We developed a DCNN model to remove noise and artifacts from high-speed CBCT. We emphasize that it is possible to reduce exposure to one quarter and to increase the CBCT scan speed by a factor of four.

Prediction of radiation-induced malfunction for cardiac implantable electronic devices (CIEDs).

PURPOSE: Cardiac implantable electronic devices (CIEDs) were believed to possess a tolerance dose to malfunction during radiotherapy. Although recent studies have qualitatively suggested neutrons as a cause of malfunction, numerical understanding has not been reached. The purpose of this work is to quantitatively clarify the contribution of secondary neutrons from out-of-field irradiation to the malfunction of CIEDs as well as to deduce the frequency of malfunctions until completion of prostate cancer treatment as a typical case. MATERIALS AND METHODS: Measured data were gathered from the literature and were re-analyzed. Firstly, linear relationship for a number of malfunctions to the neutron dose was suggested by theoretical consideration. Secondly, the accumulated number of malfunctions of CIEDs gathered from the literature was compared with the prescribed dose, scattered photon dose, and secondary neutron dose for analysis of their correlation. Thirdly, the number of malfunctions during a course of prostate treatment with high-energy X-ray, passive proton, and passive carbon-ion beams was calculated while assuming the same response to malfunctions, where X-rays consisted of 6-MV, 10-MV, 15-MV, and 18-MV beams. Monte Carlo simulation assuming simple geometry was performed for the distribution of neutron dose from X-ray beams, where normalization factors were applied to the distribution so as to reproduce the empirical values. RESULTS: Linearity between risk and neutron dose was clearly found from the measured data, as suggested by theoretical consideration. The predicted number of malfunctions until treatment completion was 0, 0.02 ± 0.01, 0.30 ± 0.08, 0.65 ± 0.17, 0.88 ± 0.50, and 0.14 ± 0.04 when 6-MV, 10-MV, 15-MV, 18-MV, passive proton, and passive carbon-ion beams, respectively, were employed, where the single model response to a malfunction of 8.6 ± 2.1 Sv- 1 was applied. CONCLUSIONS: Numerical understanding of the malfunction of CIEDs has been attained for the first time. It has been clarified that neutron dose is a good scale for the risk of CIEDs in radiotherapy. Prediction of the frequency of malfunction as well as discussion of the risk to CIEDs in radiotherapy among the multiple modalities have become possible. Because the present study quantitatively clarifies the neutron contribution to malfunction, revision of clinical guidelines is suggested.

Predictive gamma passing rate for three-dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model.

PURPOSE: We aim to develop a method to predict the gamma passing rate (GPR) of a three-dimensional (3D) dose distribution measured by the Delta4 detector system using the dose uncertainty potential (DUP) accumulation model. METHODS: Sixty head-and-neck intensity-modulated radiation therapy (IMRT) treatment plans were created in the XiO treatment planning system. All plans were created using nine step-and-shoot beams of the ONCOR linear accelerator. Verification plans were created and measured by the Delta4 system. The planar DUP (pDUP) manifesting on a field edge was generated from the segmental aperture shape with a Gaussian folding on the beam's-eye view. The DUP at each voxel ( u ) was calculated by projecting the pDUP on the Delta4 phantom with its attenuation considered. The learning model (LM), an average GPR as a function of the DUP, was approximated by an exponential function a GPR u = e - q u to compensate for the low statistics of the learning data due to a finite number of the detectors. The coefficient q was optimized to ensure that the difference between the measured and predicted GPRs ( d GPR ) was minimized. The standard deviation (SD) of the d GPR was evaluated for the optimized LM. RESULTS: It was confirmed that the coefficient q was larger for tighter tolerance. This result corresponds to the expectation that the attenuation of the a GPR u will be large for tighter tolerance. The p GPR and m GPR were observed to be proportional for all tolerances investigated. The SD of d GPR was 2.3, 4.1, and 6.7% for tolerances of 3%/3 mm, 3%/2 mm, 2%/2 mm, respectively. CONCLUSION: The DUP-based predicting method of the GPR was extended to 3D by introducing DUP attenuation and an optimized analytical LM to compensate for the low statistics of the learning data due to a finite number of detector elements. The precision of the predicted GPR is expected to be improved by improving the LM and by involving other metrics.

A novel risk analysis of clinical reference dosimetry based on failure modes and effects analysis.

PURPOSE: The output of a linear accelerator (linac) is one of the most important quality assurance (QA) factors in radiotherapy. However, there is no quantitative rationale for frequency and tolerance. The purpose of this study is to develop a novel risk analysis of clinical reference dosimetry based on failure modes and effects analysis (FMEA). METHODS: Clinical reference dosimetry data and the daily output data of two linacs (Clinac iX and Clinac 6EX) at Hiroshima University Hospital were analyzed. The analysis involved the number of patients per year for five types of fractionations. Risk priority number (RPN) is defined as the product of occurrence (O), severity (S), and detectability (D) in standard FMEA. In addition, we introduced "severity due to output drifting" (mean output change per day) (S') and the number of patients per year for five types of fractionations (W). We calculated the RPN = O × S × D × S' × W and quantitatively evaluated the risk for clinical reference dosimetry. RESULTS: Fewer fractions and less output calibration frequency resulted in higher RPN. Since clinical reference dosimetry data has a drift effect, which is missing in human processes, it was essential to use S' in addition to standard FMEA. Moreover, the parameter W was important in evaluating interinstitutional QA for clinical reference dosimetry. The relative risk of Clinac 6EX to Clinac iX was different approximately by twofold. CONCLUSIONS: We developed a novel index that can quantitatively evaluate risk for clinical reference dosimetry of each facility and machines in common on the basis of FMEA.

Automatic calibration of an arbitrarily-set near-infrared camera for patient surface respiratory monitoring.

PURPOSE: A patient's respiratory monitoring is one of the key techniques in radiotherapy for a moving target. Generally, such monitoring systems are permanently set to a fixed geometry during the installation. This study aims to enable a temporary setup of such a monitoring system by developing a fast method to automatically calibrate the geometrical position by a quick measurement of calibration markers. METHODS: One calibration marker was placed on the isocenter and the other six markers were placed at positions 5-cm apart from the isocenter to the left, right, anterior, posterior, superior, and inferior directions. A near-infrared (NIR) camera (NIC) [Kinect v2 (Microsoft Corp.)] was arbitrarily set with ten different angles around the calibration phantom with a fixed tilting-down angle at approximately 45° in a linear accelerator treatment vault. The three-dimensional (3D) coordinates in the camera (Cam) coordinate system (CS; x and y are the horizontal and vertical coordinates of the image, respectively, and z is a coordinate along the NIR time-of-flight) were taken for 1 min with 30 frames per second. The data corresponding to the measurement times of 1, 3, 10, 30, and 60 s were created to mimic various measurement times. These data were used to calculate the initial matrix elements, which included six parameters of the pitching, yawing, and rolling angles; horizontal two-dimensional translation in the treatment room; and the source-to-axis distance of NIC, for a conversion from the Cam CS to the treatment room CS for which the origin was defined at the isocenter (Iso coordinate). The six parameters were then optimized to minimize the displacements of the calculated marker coordinates from the actual positions in the Iso CS. The 3D positional accuracy and angular accuracy of the conversion were evaluated. The random error of the Iso coordinates was analyzed through a relation with the angle of each measurement setup. RESULTS: Three angles of NIC and relative translation vectors were successfully calculated from the measurement data of the calibration markers. The achieved spatial and angular accuracies were 0.02 mm and 1.6°, respectively, after the optimization. Among the mimicked measurement times investigated in this study, both spatial and angular accuracies had no dependence on the measurement time. The average random error of a static marker was 0.46 mm after the optimization. CONCLUSION: We developed an automatic method to calibrate the 3D patient surface monitoring system. The procedure developed in this study enabled a quick calibration of NIC, which can be easily repeated multiple times for a frequent and quick setup of the monitoring system.