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Background: The objective was to enhance the biological compensation factor related to irradiation interruption in a short time (short irradiation interruption) in hypoxic tumors using a refined microdosimetric kinetic model (MKM) for photon radiation therapy. Materials and methods: The biological dose differences were calculated for CHO-K1 cells exposed to a photon beam, considering interruptions of (τ) of 0-120 min and pO2 at oxygen levels of 0.075-160 mm Hg. The interrupted dose fraction (IDF) was defined as the percentage ratio of the dose delivered before short irradiation interruption to the total dose, which ranged from 10-90%. The compensated dose was calculated based on an IDF of 10-90% for a dose of 2-8 Gy and oxygen levels of 0.075-160 mm Hg. Results: The Δ with and without short irradiation interruption was more pronounced with a higher dose and increased pO2. It exceeded 3% between IDF of 50% and either 10% or 90% and occurred more than τ = 50 min at 0.075 mm Hg, τ = 20 min at 3 mm Hg, τ = 20 min at 8 mm Hg, τ = 20 min at 15 mm Hg, τ = 20 min at 38 mm Hg, and τ = 20 min at 160 mm Hg. The dose compensation factor was greater at higher IDF rates. Conclusion: The biological dose decreased with longer interruption times and higher oxygen concentrations. The improved model can compensate for the biological doses at various oxygen concentrations. Advances in knowledge: The current study improved the dose compensation method for the decrease in the biological effect owing to short irradiation interruption by considering the oxygen concentration. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (PDNR), 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. [ABSTRACT FROM AUTHOR]

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 (SSRT). 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/β = 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]