Pathogenicity of genotype 2.1 classical swine fever virus isolated from Japan in 2019 in pigs.

Classical swine fever (CSF) re-emerged in Japan in 2018 for the first time in 26 years. The disease has been known to be caused by a moderately pathogenic virus, rather than the highly pathogenic virus that had occurred in the past. However, the underlying pathophysiology remains unknown. This study conducted an experimental challenge on specific pathogen-free (SPF) pigs in a naïve state for 2, 4, and 6 weeks and confirmed the disease state during each period by clinical observation, virus detection, and pathological necropsy. We revealed the pathological changes and distribution of pathogens and virus-specific antibodies at each period after virus challenge. These results were comprehensively analyzed and approximately 70% of the pigs recovered, especially at 4- and 6-week post-virus challenge. This study provides useful information for future countermeasures against CSF by clarifying the pathogenicity outcomes in unvaccinated pigs with moderately pathogenic genotype 2.1 virus.

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.

Evaluation of Local Retinal Function in Light-Damaged Rats Using Multifocal Electroretinograms and Multifocal Visual Evoked Potentials.

Electroretinograms (ERGs) are often used to evaluate retinal function. However, assessing local retinal function can be challenging; therefore, photopic and scotopic ERGs are used to record whole-retinal function. This study evaluated focal retinal function in rats exposed to continuous light using a multifocal ERG (mfERG) system. The rats were exposed to 1000 lux of fluorescent light for 24 h to induce photoreceptor degeneration. After light exposure, the rats were reared under cyclic light conditions (12 h: 5 lux, 12 h: dark). Photopic and multifocal ERGs and single-flash and multifocal visual evoked potentials (mfVEPs) were recorded 7 days after light exposure. Fourteen days following light exposure, paraffin-embedded sections were prepared from the eyes for histological evaluation. The ERG and VEP responses dramatically decreased after 24 h of light exposure, and retinal area-dependent decreases were observed in mfERGs and mfVEPs. Histological assessment revealed severe damage to the superior retina and less damage to the inferior retina. Considering the recorded visual angles of mfERGs and mfVEPs, the degenerated area shown on the histological examinations correlates well with the responses from multifocal recordings.

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.

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.

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.

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.

Effect of secondary electron generation on dose enhancement in Lipiodol with and without a flattening filter.

PURPOSE: Lipiodol, which was used in transcatheter arterial chemoembolization before liver stereotactic body radiation therapy (SBRT), remains in SBRT. Previous we reported the dose enhancement in Lipiodol using 10 MV (10×) FFF beam. In this study, we compared the dose enhancement in Lipiodol and evaluated the probability of electron generation (PEG) for the dose enhancement using flattening filter (FF) and flattening filter free (FFF) beams. METHODS: FF and FFF for 6 MV (6×) and 10× beams were delivered by TrueBeam. The dose enhancement factor (DEF), energy spectrum, and PEG was calculated using Monte Carlo (MC) code BEAMnrc and heavy ion transport code system (PHITS). RESULTS: DEFs for FF and FFF 6× beams were 7.0% and 17.0% at the center of Lipiodol (depth, 6.5 cm). DEFs for FF and FFF 10× beams were 8.2% and 10.5% at the center of Lipiodol. Spectral analysis revealed that the FFF beams contained more low-energy (0-0.3 MeV) electrons than the FF beams, and the FF beams contained more high-energy (>0.3 MeV) electrons than the FFF beams in Lipiodol. The difference between FFF and FF beam DEFs was larger for 6× than for 10×. This occurred because the 10× beams contained more high-energy electrons. The PEGs for photoelectric absorption and Compton scattering for the FFF beams were higher than those for the FF beams. The PEG for the photoelectric absorption was higher than that for Compton scattering. CONCLUSIONS: FFF beam contained more low-energy photons and it contributed to the dose enhancement. Energy spectra and PEGs are useful for analyzing the mechanisms of dose enhancement.

Tolerance levels of CT number to electron density table for photon beam in radiotherapy treatment planning system.

The accuracy of computed tomography number to electron density (CT-ED) calibration is a key component for dose calculations in an inhomogeneous medium. In a previous work, it was shown that the tolerance levels of CT-ED calibration became stricter with an increase in tissue thickness and decrease in the effective energy of a photon beam. For the last decade, a low effective energy photon beam (e.g., flattening-filter-free (FFF)) has been used in clinical sites. However, its tolerance level has not been established yet. We established a relative electron density (ED) tolerance level for each tissue type with an FFF beam. The tolerance levels were calculated using the tissue maximum ratio (TMR) and each corresponding maximum tissue thickness. To determine the relative ED tolerance level, TMR data from a Varian accelerator and the adult reference computational phantom data in the International Commission on Radiological Protection publication 110 (ICRP-110 phantom) were used in this study. The 52 tissue components of the ICRP-110 phantom were classified by mass density as five tissues groups including lung, adipose/muscle, cartilage/spongy-bone, cortical bone, and tooth tissue. In addition, the relative ED tolerance level of each tissue group was calculated when the relative dose error to local dose reached 2%. The relative ED tolerances of a 6 MVFFF beam for lung, adipose/muscle, and cartilage/spongy-bone were ±0.044, ±0.022, and ±0.044, respectively. The thicknesses of the cortical bone and tooth groups were too small to define the tolerance levels. Because the tolerance levels of CT-ED calibration are stricter with a decrease in the effective energy of the photon beam, the tolerance levels are determined by the lowest effective energy in useable beams for radiotherapy treatment planning systems.

[Study of Dose Recalculation Method on Overriding Lung Density Using CBCT Image in the Thorax].

It is a useful method for the adaptive radiotherapy (ART) to calculate absorbed dose accurately on the image set taken by on-board cone beam computed tomography (CBCT) attached to linac for image-guided radiation therapy (IGRT). For this purpose, a simple and accurate calculation method is necessary. Several papers report that it is possible to calculate easily and accurately by using several methods of researches in the neck and prostate, but the lung density varies greatly depending on patient thorax condition. In this study, we propose a new dose recalculation method, which is a simple procedure and can achieve accurate dose calculation considered different lung densities in each patient. By using this method, it is possible to calculate exclusive of artifacts in CBCT because of overriding the lung density. The dose error between dose recalculation of the CBCT image and treatment plan agreed within±1%. Therefore, this method is expected to be a useful method for accurate dose calculation with CBCT image for ART.

Energy spectrum and dose enhancement due to the depth of the Lipiodol position using flattened and unflattened beams.

AIM: Lipiodol was used for stereotactic body radiotherapy combining trans arterial chemoembolization. Lipiodol used for tumour seeking in trans arterial chemoembolization remains in stereotactic body radiation therapy. In our previous study, we reported the dose enhancement effect in Lipiodol with 10× flattening-filter-free (FFF). The objective of our study was to evaluate the dose enhancement and energy spectrum of photons and electrons due to the Lipiodol depth with flattened (FF) and FFF beams. METHODS: FF and FFF for 6 MV beams from TrueBeam were used in this study. The Lipiodol (3 × 3 × 3 cm3) was located at depths of 1, 3, 5, 10, 20, and 30 cm in water. The dose enhancement factor (DEF) and the energy fluence were obtained by Monte Carlo calculations of the particle and heavy ion transport code system (PHITS). RESULTS: The DEFs at the centre of Lipiodol with the FF beam were 6.8, 7.3, 7.6, 7.2, 6.1, and 5.7% and those with the FFF beam were 20.6, 22.0, 21.9, 20.0, 12.3, and 12.1% at depths of 1, 3, 5, 10, 20, and 30 cm, respectively, where Lipiodol was located in water. Moreover, spectrum results showed that more low-energy photons and electrons were present at shallow depth where Lipiodol was located in water. The variation in the low-energy spectrum due to the depth of the Lipiodol position was more explicit with the FFF beam than that with the FF beam. CONCLUSIONS: The current study revealed variations in the DEF and energy spectrum due to the depth of the Lipiodol position with the FF and FFF beams. Although the FF beam could reduce the effect of energy dependence due to the depth of the Lipiodol position, the dose enhancement was overall small. To cause a large dose enhancement, the FFF beam with the distance of the patient surface to Lipiodol within 10 cm should be used.

Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.

Physical and biological dosimetric margin according to prescription method for stereotactic body radiation therapy.

This study aimed to expand the biological conversion factor (BCF) model, which converts the physical dosimetric margin (PDM) to the biological dosimetric margin (BDM) for point prescription with 3-dimensional conformal radiation therapy (3DCRT) and the marginal prescription method with volumetric-modulated arc radiotherapy (VMAT). The VMAT of the marginal prescription and the 3DCRT of the point prescription with lung stereotactic body radiation therapy (SBRT) by using RayStation were planned. The biological equivalent dose (BED) for a dose per fraction (DPF) of 3-20 Gy was calculated from these plans. The dose was perturbed with the calculation using a 1-mm step isocenter shift. The dose covering 95% of the target was greater than or equal to 90% of the prescribed physical dose, and the BED were defined as the PDM and BDM, respectively. The BCF was created as a function of the DPF. The PDM and BDM for all DPFs were larger with the point prescription method than with the marginal prescription method. The marginal prescription method with a 60% isodose line had a larger PDM and BDM. The BCF with the point prescription was smaller than that with the marginal prescription in the left-right (LR), anterior-posterior (AP) and cranio-caudal (CC) directions. In the marginal prescription method, the 60% isodose line had a higher BCF. In conclusion, the improved BCF method could be converted to BDM for point prescription with 3DCRT and marginal prescription method with VMAT, which is required for stereotactic radiation therapy in radiobiology-based treatment planning.

A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network.

BACKGROUND: Artificial intelligence (AI)-based gamma passing rate (GPR) prediction has been proposed as a time-efficient virtual patient-specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy. PURPOSE: The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN). METHODS: The fluence maps of 270 VMAT beams for prostate cancer were measured using an electronic portal imaging device and analyzed using gamma evaluation with 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances. The 270 gamma distributions were divided into two datasets: 240 training datasets for creating a model and 30 test datasets for evaluation. The image prediction network for the fluence maps calculated by the treatment planning system (TPS) to the gamma distributions was created using a GAN. The sensitivity, specificity, and accuracy of detecting failing points were evaluated using measured and synthesized gamma distributions. In addition, the difference between measured GPR (mGPR) and predicted GPR (pGPR) values calculated from the synthesized gamma distributions was evaluated. RESULTS: The root mean squared errors between mGPR and pGPR were 1.0%, 2.1%, 3.5%, and 3.6% for the 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The accuracies for detecting failing points were 98.9%, 96.9%, 94.7%, and 93.7% for 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The sensitivity and specificity were the highest for 1%/0.5-mm and 3%/2-mm tolerances, which were 82.7% and 99.6%, respectively. CONCLUSIONS: We developed a novel system using a GAN to generate a synthesized gamma distribution-based patient-specific VMAT QA. The system is promising from the point of view of quality assurance in radiotherapy because it shows high performance and can detect failing points.

MRI image synthesis for fluid-attenuated inversion recovery and diffusion-weighted images with deep learning.

This study aims to synthesize fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted images (DWI) with a deep conditional adversarial network from T1- and T2-weighted magnetic resonance imaging (MRI) images. A total of 1980 images of 102 patients were split into two datasets: 1470 (68 patients) in a training set and 510 (34 patients) in a test set. The prediction framework was based on a convolutional neural network with a generator and discriminator. T1-weighted, T2-weighted, and composite images were used as inputs. The digital imaging and communications in medicine (DICOM) images were converted to 8-bit red-green-blue images. The red and blue channels of the composite images were assigned to 8-bit grayscale pixel values in T1-weighted images, and the green channel was assigned to those in T2-weighted images. The prediction FLAIR and DWI images were of the same objects as the inputs. For the results, the prediction model with composite MRI input images in the DWI image showed the smallest relative mean absolute error (rMAE) and largest mutual information (MI), and that in the FLAIR image showed the largest relative mean-square error (rMSE), relative root-mean-square error (rRMSE), and peak signal-to-noise ratio (PSNR). For the FLAIR image, the prediction model with the T2-weighted MRI input images generated more accurate synthesis results than that with the T1-weighted inputs. The proposed image synthesis framework can improve the versatility and quality of multi-contrast MRI without extra scans. The composite input MRI image contributes to synthesizing the multi-contrast MRI image efficiently.

Detection of RD-114 virus by a reverse transcriptase assay based on product enhancement.

RD-114 virus is a feline endogenous retrovirus that exists in the genome of all cats. It can be assumed that feline and canine live vaccines manufactured by culturing cells of feline origin are contaminated with the virus. The current study is attempted to develop a product enhanced reverse transcriptase (PERT) assay to detect replication-competent RD-114 virus. Since culture supernatants of Crandell-Rees feline kidney (CRFK) cells do not have detectable reverse transcriptase activity in case of do not passage on other cell lines, these results raise the possibility that RD-114 virus grow efficiently in other retrovirus producing cell lines. For the PERT assay, RD-114 virus isolated from CRFK cells may need to be passaged more than four times on 293T cells to be expressed at appreciable levels. The PERT assay described here provides an accurate method for determining the presence of RD-114 in culture supernatants and will help monitor live vaccines produced in endogenous virus producing cell lines.

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.

Predictive gamma passing rate of 3D detector array-based volumetric modulated arc therapy quality assurance for prostate cancer via deep learning.

To predict the gamma passing rate (GPR) of the three-dimensional (3D) detector array-based volumetric modulated arc therapy (VMAT) quality assurance (QA) for prostate cancer using a convolutional neural network (CNN) with the 3D dose distribution. One hundred thirty-five VMAT plans for prostate cancer were selected: 110 plans were used for training and validation, and 25 plans were used for testing. Verification plans were measured using a helical 3D diode array (ArcCHECK). The dose distribution on the detector element plane of these verification plans was used as input data for the CNN model. The measured GPR (mGPR) values were used as the training data. The CNN model comprises eighteen layers and predicted GPR (pGPR) values. The mGPR and pGPR values were compared, and a cumulative frequency histogram of the prediction error was created to clarify the prediction error tendency. The correlation coefficients of pGPR and mGPR were 0.67, 0.69, 0.66, and 0.73 for 3%/3-mm, 3%/2-mm, 2%/3-mm, and 2%/2-mm gamma criteria, respectively. The respective mean±standard deviations of pGPR-mGPR were -0.87±2.18%, -0.65±2.93%, -0.44±2.53%, and -0.71±3.33%. The probabilities of false positive error cases (pGPR < mGPR) were 72%, 60%, 68%, and 56% for each gamma criterion. We developed a deep learning-based prediction model of the 3D detector array-based VMAT QA for prostate cancer, and evaluated the accuracy and tendency of prediction GPR. This model can provide a proactive estimation for the results of the patient-specific QA before the verification measurement.