Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity.

Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hippocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. Therefore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its environment, from only the high-dimensional neural activity is important in deciphering the information represented in the brain. Most previous studies have estimated the low-dimensional dynamics (i.e., latent variables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian processes. Recently, persistent cohomology has been used to estimate latent variables from the phase information (i.e., circular coordinates) of manifolds created by neural activity. However, the advantages of persistent cohomology over Bayesian population decoding are not well understood. We compared persistent cohomology and Bayesian population decoding in estimating the animal location from simulated and actual grid cell population activity. We found that persistent cohomology can estimate the animal location with fewer neurons than Bayesian population decoding and robustly estimate the animal location from actual noisy data.

Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model.

OBJECTIVES: To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features. METHODS: The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers. RESULTS: A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers. CONCLUSION: The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients. CLINICAL RELEVANCE STATEMENT: The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed. KEY POINTS: • Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.

Influence of different air CT numbers for IVDT on the dose distribution in TomoTherapy MVCT.

This study aims to evaluate the effect of different air computed tomography (CT) numbers of the image value density table (IVDT) on the retrospective dose calculation of head-and-neck (HN) radiotherapy using TomoTherapy megavoltage CT (MVCT) images. The CT numbers of the inside and outside air and each tissue-equivalent plug of the "Cheese" phantom were obtained from TomoTherapy MVCT. Two IVDTs with different air CT numbers were created and applied to MVCT images of the HN anthropomorphic phantom and recalculated by Planned Adaptive to verify dose distribution. We defined the recalculation dose with MVCT images using both inside and outside air of the IVDT as IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ , respectively. Treatment planning doses calculated on kVCT images were compared with those calculated on MVCT images using two different IVDT tables, namely, IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ . The difference between average MVCT numbers ±1 standard deviation on inside and outside air of the calibration phantom was 65 ± 36 HU. This difference in MVCT number of air exceeded the recommendation lung tolerance for dose calculation error of 2%. The dose differences between the planning target volume (PTV): D98% , D50% , D2% and the organ at risk (OAR): Dmax , Dmean recalculated by IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ using MVCT images were a maximum of 0.7% and 1.2%, respectively. Recalculated doses to the PTV and OAR with MVCT showed that IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ was 0.5%-0.7% closer to the kVCT treatment planning dose than IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ . This study showed that IVDT MVCT outair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ was more accurate than IVDT MVCT inair ${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ in recalculating the dose HN cases of MVCT using TomoTherapy.

Biological dosimetric impact of dose-delivery time for hypoxic tumour with modified microdosimetric kinetic model.

Background: An improved microdosimetric kinetic model (MKM) can address radiobiological effects with prolonged delivery times. However, these do not consider the effects of oxygen. The current study aimed to evaluate the biological dosimetric effects associated with the dose delivery time in hypoxic tumours with improved MKM for photon radiation therapy. Materials and methods: Cell survival was measured under anoxic, hypoxic, and oxic conditions using the Monte Carlo code PHITS. The effect of the dose rate of 0.5-24 Gy/min for the biological dose (Dbio) was estimated using the microdosimetric kinetic model. The dose per fraction and pressure of O2 (pO2) in the tumour varied from 2 to 20 Gy and from 0.01 to 5.0% pO2, respectively. Results: The ratio of the Dbio at 1.0-24 Gy/min to that at 0.5 Gy/min (RDR) was higher at higher doses. The maximum RDR was 1.09 at 1.0 Gy/min, 1.12 at 12 Gy/min, and 1.13 at 24 Gy/min. The ratio of the Dbio at 0.01-2.0% of pO2 to that at 5.0% of pO2 (Roxy) was within 0.1 for 2-20 Gy of physical dose. The maximum Roxy was 0.42 at 0.01% pO2, 0.76 at 0.4% pO2, 0.89 at 1% pO2, and 0.96 at 2% pO2. Conclusion: Our proposed model can estimate the cell killing and biological dose under hypoxia in a clinical and realistic patient. A shorter dose-delivery time with a higher oxygen distribution increased the radiobiological effect. It was more effective at higher doses per fraction than at lower doses.

Dose uncertainty due to energy dependence in dual-energy computed tomography.

Purpose: To evaluate the absolute dose uncertainty at 2 different energies and for the large and small bowtie filters in dual-energy computed tomography (DECT). Material and methods: Measurements were performed using DECT at 80 kV and 140 kilovoltage peak (kVp), and single-energy computed tomography (CT) at 120 kV. The absolute dose was calculated from the mass-energy absorption obtained from the half-value layer (HVL) of aluminium. Results: The difference in the water-to-air ratio of the mean mass energy-absorption coefficients at 80 kV and 140 kV was 2.0% for the small bow-tie filter and 3.0% for the large bow-tie filter. At lower tube voltages, the difference in the absorbed dose with the large and small bow-tie filters was larger. Conclusions: The absolute dose uncertainty due to energy dependence was 3.0%, which could be reduced with single-energy beams at 120 kV or by using the average effective energy measurement with dual-energy beams.

Robust optimization of VMAT for prostate cancer accounting for geometric uncertainty.

The aim of this study was to propose optimal robust planning by comparing the robustness with setup error with the robustness of a conventional planning target volume (PTV)-based plan and to compare the robust plan to the PTV-based plan for the target and organ at risk (OAR). Data from 13 patients with intermediate-to-high-risk localized prostate cancer who did not have T3b disease were analyzed. The dose distribution under multiple setup error scenarios was assessed using a conventional PTV-based plan. The clinical target volume (CTV) and OAR dose in moving coordinates were used for the dose constraint with the robust plan. The hybrid robust plan added the dose constraint of the PTV-rectum to the static coordinate system. When the isocenter was shifted by 10 mm in the superior-inferior direction and 8 mm in the right-left and anterior directions, the doses to the CTV, bladder, and rectum of the PTV-based plan, robust plan, and hybrid robust plan were compared. For the CTV D99% in the PTV-based plan and hybrid robust plan, over 95% of the prescribed dose was secured in all directions, except in the inferior direction. There was no significant difference between the PTV-based plan and the hybrid robust plan for rectum V70Gy , V60Gy , and V40Gy . This study proposed an optimization method for patients with prostate cancer. When the setup error occurred within the PTV margin, the dose robustness of the CTV for the hybrid robust plan was higher than that of the PTV-based plan, while maintaining the equivalent OAR dose.

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.

Image synthesis of effective atomic number images using a deep convolutional neural network-based generative adversarial network.

Background: The effective atomic numbers obtained from dual-energy computed tomography (DECT) can aid in characterization of materials. In this study, an effective atomic number image reconstructed from a DECT image was synthesized using an equivalent single-energy CT image with a deep convolutional neural network (CNN)-based generative adversarial network (GAN). Materials and methods: The image synthesis framework to obtain the effective atomic number images from a single-energy CT image at 120 kVp using a CNN-based GAN was developed. The evaluation metrics were the mean absolute error (MAE), relative root mean square error (RMSE), relative mean square error (MSE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mutual information (MI). Results: The difference between the reference and synthetic effective atomic numbers was within 9.7% in all regions of interest. The averages of MAE, RMSE, MSE, SSIM, PSNR, and MI of the reference and synthesized images in the test data were 0.09, 0.045, 0.0, 0.89, 54.97, and 1.03, respectively. Conclusions: In this study, an image synthesis framework using single-energy CT images was constructed to obtain atomic number images scanned by DECT. This image synthesis framework can aid in material decomposition without extra scans in DECT.

Improved biological dosimetric margin model for different PTV margins with stereotactic body radiation therapy in homogeneous and nonhomogeneous tumor regions.

Background: The purpose of this study was to improve the biological dosimetric margin (BDM) corresponding to different planning target volume (PTV) margins in homogeneous and nonhomogeneous tumor regions using an improved biological conversion factor (BCF) model for stereotactic body radiation therapy (SBRT). Materials and methods: The PTV margin was 5-20 mm from the clinical target volume. The biologically equivalent dose (BED) was calculated using the linear-quadratic model. The biological parameters were α/ß = 10 Gy, and the dose per fraction (DPF) was d = 3-20 Gy/fr. The isocenter was offset at intervals of 1 mm; 95% of the clinical target volume covered more than 90% of the prescribed physical dose, and BED was defined as biological and physical DMs. The BCF formula was defined as a function of the DPF. Results: The difference in the BCF caused by the DPF was within 0.05 for the homogeneous and nonhomogeneous phantoms. In the virtual nonhomogeneous phantom, the data with a PTV margin of 10-20 mm were not significantly different; thus, these were combined to fit the BCF. In the virtual homogeneous phantom, the BCF was fitted to each PTV margin. Conclusions: The current study improved a scheme to estimate the BDM considering the size of the PTV margin and homogeneous and nonhomogeneous regions. This technique is expected to enable BED-based treatment planning using treatment systems based on physical doses for SBRT.

DNA strand breaks based on Monte Carlo simulation in and around the Lipiodol with flattening filter and flattening filter-free photon beams.

Background: The current study aims to investigate the DNA strand breaks based on the Monte Carlo simulation within and around the Lipiodol with flattening filter (FF) and flattening filter-free (FFF) photon beams. Materials and methods: The dose-mean lineal energy (yD) and DNA single- and double strand breaks (DSB/SSB) based on spatial patterns of inelastic interactions were calculated using the Monte Carlo code: particle and heavy ion transport system (PHITS). The ratios of dose using standard radiation (200 kVX) to the dose of test radiation (FF and FFF of 6 MV X-ray (6MVX) and 10 MVX beams) to produce the same biological effects was defined as RBEDSB. The RBEDSB within the Lipiodol and in the build-up and build-down regions was evaluated. Results: The RBEDSB values with the Lipiodol was larger than that without the Lipiodol at the depth of 4.9 cm by 4.2% and 2.5% for 6 MVX FFF and FF beams, and 3.3% and 2.5% for 10 MVX FFF and FF beams. The RBEDSB values with the Lipiodol was larger than that without the Lipiodol at the depth of 6.5 cm by 2.9% and 2.4% for 6 MVX FFF and FF beams, and 1.9% and 1.4% for 10 MVX FFF and FF beams. In the build-down region at the depth of 8.1 cm, the RBEDSB values with the Lipiodol was smaller than that without the Lipiodol by 4.2% and 2.9% for 6 MVX FFF and FF beams, and 1.4% and 0.1% for 10 MVX FFF and FF beams. Conclusions: The current study simulated the DNA strand break except for the physical dose difference. The lower and FFF beam occurred the higher biological effect.

Potential benefits of volumetric modulated arc therapy to reduce the incidence of ≥ grade 2 radiation pneumonitis in radiotherapy for locally advanced non-small cell lung cancer patients.

BACKGROUND: The use of volumetric modulated arc therapy is gradually widespread for locally advanced non-small cell lung cancer. The purpose of this study was to identify the factors that caused ≥ grade 2 radiation pneumonitis and evaluate the impact of using volumetric modulated arc therapy on the incidence of ≥ grade 2 radiation pneumonitis by comparing three-dimensional conformal radiation therapy. METHODS: We retrospectively evaluated 124 patients who underwent radical radiotherapy for locally advanced non-small cell lung cancer in our institution between 2008 and 2019. The following variables were analysed to detect the factors that affected ≥ grade 2 radiation pneumonitis; age, sex, the presence of interstitial lung disease, pulmonary emphysema, tumour location, stage, PTV/lung volume, lung V20Gy, total dose, concurrent chemoradiotherapy, adjuvant immune checkpoint inhibitor, radiotherapy method. Radiation pneumonitis was evaluated using the common terminology criteria for adverse events (version 5.0). RESULTS: A total of 84 patients underwent three-dimensional conformal radiation therapy (3D-CRT group) and 40 patients underwent volumetric modulated arc therapy (VMAT group). The cumulative incidence of ≥ grade 2 radiation pneumonitis at 12 months was significantly lower in the VMAT group than in the 3D-CRT group (25% vs. 49.1%). The use of volumetric modulated arc therapy was a significant factor for ≥ grade 2 radiation pneumonitis (HR:0.32, 95% CI: 0.15-0.65, P = 0.0017) in addition to lung V20Gy (≥ 24%, HR:5.72 (95% CI: 2.87-11.4), P < 0.0001) and total dose (≥ 70 Gy, HR:2.64 (95% CI: 1.39-5.03), P = 0.0031) even after adjustment by multivariate analysis. CONCLUSIONS: We identified factors associated with ≥ grade 2 radiation pneumonitis in radiotherapy for patients with locally advanced non-small cell lung cancer. Volumetric modulated arc therapy has potential benefits to reduce the risk of ≥ grade 2 radiation pneumonitis.

Image synthesis of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks.

PURPOSE: To synthesize a dual-energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV-CT) image using a deep convolutional adversarial network. METHODS: A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kV-CT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV-CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI). RESULTS: The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image. CONCLUSIONS: The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single-energy CT images.

Formulation of objective indices to quantify machine failure risk analysis for interruptions in radiotherapy.

OBJECTIVES: To evaluate the effect of interruption in radiotherapy due to machine failure in patients and medical institutions using machine failure risk analysis (MFRA). MATERIAL AND METHODS: The risk of machine failure during treatment is assigned to three scores (biological effect, B; occurrence, O; and cost of labor and repair parts, C) for each type of machine failure. The biological patient risk (BPR) and the economic institution risk (EIR) are calculated as the product of B and O ( B × O ) and C and O ( C × O ), respectively. The MFRA is performed in two linear accelerators (linacs). RESULT: The multileaf collimator (MLC) fault has the highest BPR and second highest EIR. In particular, TrueBeam has a higher BPR and EIR for MLC failures. The total EIR in TrueBeam was significantly higher than that in Clinac iX. The minor interlock had the second highest BPR, whereas a smaller EIR. Meanwhile, the EIR for the LaserGuard fault was the highest, and that for the monitor chamber fault was the second highest. These machine failures occurred in TrueBeam. The BPR and EIR should be evaluated for each linac. Further, the sensitivity of the BPR, it decreased with higher T 1 / 2 and α/ß values. No relative difference is observed in the BPR for each machine failure when T 1 / 2 and α/ß were varied. CONCLUSION: The risk faced by patients and institutions in machine failure may be reduced using MFRA. ADVANCES IN KNOWLEDGE: For clinical radiotherapy, interruption can occur from unscheduled downtime with machine failures. Interruption causes sublethal damage repair. The current study evaluated the effect of interruption in radiotherapy owing to machine failure on patients and medical institutions using a new method, that is, machine failure risk analysis.

T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks.

BACKGROUND: The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images. MATERIALS AND METHODS: A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256. RESULTS: The relative mean absolute error (rMAE ) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size. CONCLUSIONS: Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations.

Assessment of biological dosimetric margin for stereotactic body radiation therapy.

PURPOSE: To develop a novel biological dosimetric margin (BDM) and to create a biological conversion factor (BCF) that compensates for the difference between physical dosimetric margin (PDM) and BDM, which provides a novel scheme of a direct estimation of the BDM from the physical dose (PD) distribution. METHODS: The offset to isocenter was applied in 1-mm steps along left-right (LR), anterior-posterior (AP), and cranio-caudal (CC) directions for 10 treatment plans of lung stereotactic body radiation therapy (SBRT) with a prescribed dose of 48 Gy. These plans were recalculated to biological equivalent dose (BED) by the linear-quadratic model for the dose per fraction (DPF) of d = 3-20 Gy/fr and α / ß = 3 - 10 . BDM and PDM were defined so that the region that satisfied that the dose covering 95% (or 98%) of the clinical target volume was greater than or equal to the 90% of the prescribed PD and BED, respectively. An empirical formula of the BCF was created as a function of the DPF. RESULTS: There was no significant difference between LR and AP directions for neither the PDM nor BDM. On the other hand, BDM and PDM in the CC direction were significantly larger than in the other directions. BCFs of D95% and D98% were derived for the transverse (LR and AP) and longitudinal (CC) directions. CONCLUSIONS: A novel scheme to directly estimate the BDM using the BCF was developed. This technique is expected to enable the BED-based SBRT treatment planning using PD-based treatment planning systems.

Radiobiological effects of the interruption time with Monte Carlo Simulation on multiple fields in photon beams.

PURPOSE: The interruption time is the irradiation interruption that occurs at sites and operations such as the gantry, collimator, couch rotation, and patient setup within the field in radiotherapy. However, the radiobiological effect of prolonging the treatment time by the interruption time for tumor cells is little evaluated. We investigated the effect of the interruption time on the radiobiological effectiveness with photon beams based on a modified microdosimetric kinetic (mMK) model. METHODS: The dose-mean lineal energy yD (keV/µm) of 6-MV photon beams was calculated by the particle and heavy ion transport system (PHITS). We set the absorbed dose to 2 or 8 Gy, and the interruption time (τ) was set to 1, 3, 5, 10, 30, and 60 min. The biological parameters such as α0, ß0, and DNA repair constant rate (a + c) values were acquired from a human non-small-cell lung cancer cell line (NCI-H460) for the mMK model. We used two-field and four-field irradiation with a constant dose rate (3 Gy/min); the photon beams were paused for interruption time τ. We calculated the relative biological effectiveness (RBE) to evaluate the interruption time's effect compared with no interrupted as a reference. RESULTS: The yD of 6-MV photon beams was 2.32 (keV/µm), and there was little effect by changing the water depth (standard deviation was 0.01). The RBE with four-field irradiation for 8 Gy was decreased to 0.997, 0.975, 0.900, and 0.836 τ = 1, 10, 30, 60 min, respectively. In addition, the RBE was affected by the repair constant rate (a + c) value, the greater the decrease in RBE with the longer the interruption time when the (a + c) value was large. CONCLUSION: The ~10-min interruption of 6-MV photon beams did not significantly impact the radiobiological effectiveness, since the RBE decrease was <3%. Nevertheless, the RBE's effect on tumor cells was decreased about 30% by increasing the 60 min interruption time at 8 Gy with four-field irradiation. It is thus necessary to make the interruption time as short as possible.

Synthesized effective atomic numbers for commercially available dual-energy CT.

PURPOSE: The objective of this study was to assess synthesized effective atomic number (Zeff) values with a new developed tissue characteristic phantom and contrast material of varying iodine concentrations using single-source fast kilovoltage switching dual-energy CT (DECT) scanner. METHODS: A newly developed multi energy tissue characterisation CT phantom and an acrylic phantom with various iodine concentrations of were scanned using single-source fast kilovoltage switching DECT (GE-DECT) scanner. The difference between the measured and theoretical values of Zeff were evaluated. Additionally, the difference and coefficient of variation (CV) values of the theoretical and measured values were compared with values obtained with the Canon-DECT scanner that was analysed in our previous study. RESULTS: The average Zeff difference in the Multi-energy phantom was within 4.5%. The average difference of the theoretical and measured Zeff values for the acrylic phantom with variation of iodine concentration was within 3.3%. Compared to the results for the single-source Canon-DECT scanner used in our previous study, the average difference and CV of the theoretical and measured Zeff values obtained with the GE-DECT scanner were markedly smaller. CONCLUSIONS: The accuracy of the synthesized Zeff values with GE-DECT had a good agreement with the theoretical Zeff values for the Multi-Energy phantom. The GE-DECT could reduce the noise and the accuracy of the Zeff values than that with Canon-DECT for the varying iodine concentrations of contrast medium. ADVANCES IN KNOWLEDGE: The accuracy and precision of the Zeff values of the contrast medium with the GE-DECT could be sufficient with human equivalent materials.

Photon and electron backscatter dose and energy spectrum analysis around Lipiodol using flattened and unflattened beams.

PURPOSE: The aim of the current study was to evaluate the backscatter dose and energy spectrum from the Lipiodol with flattening filter (FF) and flattening filter-free (FFF) beams. Moreover, the backscatter range, that was defined as the backscatter distance (BD) are revealed. METHODS: 6 MVX FF and FFF beams were delivered by TrueBeam. Two dose calculation methods with Monte Carlo calculation were used with a virtual phantom in which the Lipiodol (3 × 3 × 3 cm3 ) was located at a depth of 5.0 cm in a water-equivalent phantom (20 × 20 × 20 cm3 ). The first dose calculation was an analysis of the dose and energy spectrum with the complete scattering of photons and electrons, and the other was a specified dose analysis only with scattering from a specified region. The specified dose analysis was divided into a scattering of primary photons and a scattering of electrons. RESULTS: The lower-energy photons contributed to the backscatter, while the high-energy photons contributed the difference of the backscatter dose between the FF and FFF beams. Although the difference in the dose from the scattered electrons between the FF and FFF beams was within 1%, the difference of the dose from the scattered photons between the FF and FFF beams was 5.4% at a depth of 4.98 cm. CONCLUSIONS: The backscatter range from the Lipiodol was within 3 mm and depended on the Compton scatter from the primary photons. The backscatter dose from the Lipiodol can be useful in clinical applications in cases where the backscatter region is located within a tumor.

Tolerance levels of mass density for CT number calibration in photon radiation therapy.

Computed tomography (CT) data are required to calculate the dose distribution in a patient's body. Generally, there are two CT number calibration methods for commercial radiotherapy treatment planning system (RTPS), namely CT number-relative electron density calibration (CT-RED calibration) and CT number-mass density calibration (CT-MD calibration). In a previous study, the tolerance levels of CT-RED calibration were established for each tissue type. The tolerance levels were established when the relative dose error to local dose reached 2%. However, the tolerance levels of CT-MD calibration are not established yet. We established the tolerance levels of CT-MD calibration based on the tolerance levels of CT-RED calibration. In order to convert mass density (MD) to relative electron density (RED), the conversion factors were determined with adult reference computational phantom data available in the International Commission on Radiological Protection publication 110 (ICRP-110). In order to validate the practicability of the conversion factor, the relative dose error and the dose linearity were validated with multiple RTPSes and dose calculation algorithms for two groups, namely, CT-RED calibration and CT-MD calibration. The tolerance levels of CT-MD calibration were determined from the tolerance levels of CT-RED calibration with conversion factors. The converted RED from MD was compared with actual RED calculated from ICRP-110. The conversion error was within ±0.01 for most standard organs. It was assumed that the conversion error was sufficiently small. The relative dose error difference for two groups was less than 0.3% for each tissue type. Therefore, the tolerance levels for CT-MD calibration were determined from the tolerance levels of CT-RED calibration with the conversion factors. The MD tolerance levels for lung, adipose/muscle, and cartilage/spongy-bone corresponded to ±0.044, ±0.022, and ±0.045 g/cm3 , respectively. The tolerance levels were useful in terms of approving the CT-MD calibration table for clinical use.

Biological dose-enhancement analysis with Monte Carlo simulation for Lipiodol for photon beams.

BACKGROUND: Previously, the physical dose-enhancement factor (DphysEF) enhancement was introduced. However, the dose enhancement considering the biological effectiveness was not shown. PURPOSE: The aim of the current study was to evaluate the biological dose-enhancement factor (DbioEF) by the dose rate and to compare the DphysEF and the DbioEF in Lipiodol for liver Stereotactic Body Radiation Therapy (SBRT). MATERIALS AND METHODS: Flattening-filter-free (FFF) 6-MV (6MVX) and 10MVX beams were delivered by TrueBeam. A virtual inhomogeneity phantom and a liver SBRT patient-treatment plan were used. The DphysEF and lineal energy distribution ( y ) distribution was calculated from Monte Carlo simulations. Using a microdosimetric-kinetic (MK) model that is estimated based on the linear-quadratic formula for Lipiodol using human liver hepatocellular cells (HepG2), the biological dose and biological dose enhancement factor (DbioEF) were calculated. The dose rate in the simulation was changed from 0.1 to 24 Gy/min. RESULTS: The DbioEF (DR:2Gy/min) and DphysEF with 10MVX FFF beam were 23.2% and 19.1% at maximum and 12.8% and 11.1% on average in the Lipiodol. In the comparison of the DbioEF between 0.1-24 Gy/min, the DbioEF was 21.2% and 11.1% with 0.1 Gy/min for 6MVX and 10 MVX, respectively. The DbioEF was larger than DEF for the 6MVX and 10MVX FFF beams. In clinical cases with the 10MVX FFF beam, the DbioEF and DphysEF in the Lipiodol region can increase the in-tumor dose by approximately 11% and 10%, respectively, without increasing the dose to normal tissue. CONCLUSIONS: The lower-energy and higher-dose-rate beams were contributed to the biological dose. The Lipiodol caused the enhancement of the physical dose and biological effectiveness. ADVANCES IN KNOWLEDGE: The biological dose enhancement (DbioEF) should be considered in the high-density material such as the Lipiodol.