Search Results (15)

This study proposes an image-enhancement method to address the challenges of low visibility and color distortion in images captured during yellow sandstorms for an image sensor based outdoor surveillance system. The technique combines traditional image processing with deep learning to improve image quality while preserving color consistency during transformation. Conventional methods can partially improve color representation and reduce blurriness in sand–dust environments. However, they are limited in their ability to restore fine details and sharp object boundaries effectively. In contrast, the proposed method incorporates Retinex-based processing into the training phase, enabling enhanced clarity and sharpness in the restored images. The proposed framework comprises three main steps. First, a cycle-consistent generative adversarial network (CycleGAN) is trained with unpaired images to generate synthetically paired data. Second, CycleGAN is retrained using these generated images along with clear images obtained through multiscale image decomposition, allowing the model to transform dust-interfered images into clear ones. Finally, color preservation is achieved by selecting the A and B chrominance channels from the small-scale model to maintain the original color characteristics. The experimental results confirmed that the proposed method effectively restores image color and removes sand–dust-related interference, thereby providing enhanced visual quality under sandstorm conditions. Specifically, it outperformed algorithm-based dust removal methods such as Sand-Dust Image Enhancement (SDIE), Chromatic Variance Consistency Gamma and Correction-Based Dehazing (CVCGCBD), and Rank-One Prior (ROP+), as well as machine learning-based methods including Fusion strategy and Two-in-One Low-Visibility Enhancement Network (TOENet), achieving a Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score of 17.238, which demonstrates improved perceptual quality, and an Local Phase Coherence-Sharpness Index (LPC-SI) value of 0.973, indicating enhanced sharpness. Both metrics showed superior performance compared to conventional methods. When applied to Closed-Circuit Television (CCTV) systems, the proposed method is expected to mitigate the adverse effects of color distortion and image blurring caused by sand–dust, thereby effectively improving visual clarity in practical surveillance applications. Full article

This study investigates the generation of synthetic CT (sCT) images from zero echo time (ZTE) MRI to support MR-only radiotherapy, which can reduce image registration errors and lower treatment planning costs. Since MRI lacks the electron density data required for accurate dose calculations, generating reliable sCTs is essential. ZTE MRI, offering high bone contrast, was used with two deep learning models: attention deep residual U-Net (ADR-Unet) and derived conditional generative adversarial network (cGAN). Data from 17 head and neck cancer patients were used to train and evaluate the models. ADR-Unet was enhanced with deep residual blocks and attention mechanisms to improve learning and reconstruction quality. Both models were implemented in-house and compared to standard U-Net and Unet++ architectures using image quality metrics, visual inspection, and dosimetric analysis. Volumetric modulated arc therapy (VMAT) planning was performed on both planning CT and generated sCTs. ADR-Unet achieved a mean absolute error of 55.49 HU and a Dice score of 0.86 for bone structures. All the models demonstrated Gamma pass rates above 99.4% and dose deviations within 2–3%, confirming clinical acceptability. These results highlight ADR-Unet and cGAN as promising solutions for accurate sCT generation, enabling effective MR-only radiotherapy. Full article

GaN High-Electron-Mobility Transistors have gained some foothold in the power-electronics industry. This is due to wide frequency bandwidth and power handling. Gallium Nitride offers a wide bandgap and higher critical field strength compared to most wide-bandgap semiconductors, resulting in better radiation resistance. Theoretically, it supports higher speeds as the device dimensions could be reduced without suffering voltage breakdown. The simulation and experimental results illustrate the superior performance of the Gallium Nitride High-Electron-Mobility Transistors in an amplifying circuit. Using a spice model for commercially available Gallium Nitride High-Electron-Mobility Transistors, non-distorted output to an input signal of 200 ps was displayed. Real-world measurements underscore the fast response of the Gallium Nitride High-Electron-Mobility Transistors with its measured slew rate at approximately 3000 V/μs, a result only 17% lower than the result obtained from the simulation. This fast response, coupled with the amplifier radiation resistance, shows promise for designing improved detection and imaging circuits with long Mean Time Between Failure required, for example, by next-generation industrial-process gamma transmission-computed tomography. Full article

Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow. Full article

In this paper, we propose a new model for conditional video generation (GammaGAN). Generally, it is challenging to generate a plausible video from a single image with a class label as a condition. Traditional methods based on conditional generative adversarial networks (cGANs) often encounter difficulties in effectively utilizing a class label, typically by concatenating a class label to the input or hidden layer. In contrast, the proposed GammaGAN adopts the projection method to effectively utilize a class label and proposes scaling class embeddings and normalizing outputs. Concretely, our proposed architecture consists of two streams: a class embedding stream and a data stream. In the class embedding stream, class embeddings are scaled to effectively emphasize class-specific differences. Meanwhile, the outputs in the data stream are normalized. Our normalization technique balances the outputs of both streams, ensuring a balance between the importance of feature vectors and class embeddings during training. This results in enhanced video quality. We evaluated the proposed method using the MUG facial expression dataset, which consists of six facial expressions. Compared with the prior conditional video generation model, ImaGINator, our model yielded relative improvements of 1.61%, 1.66%, and 0.36% in terms of PSNR, SSIM, and LPIPS, respectively. These results suggest potential for further advancements in conditional video generation. Full article

Oil-Gan, also known as emblica, is the fruit of the genus Phyllanthus emblica L. The fruits are high in nutrients and display excellent health care functions and development values. The primary aim of this study was to investigate the activities of ethyl acetate extract from Phyllanthus emblica L. (EPE) on type 1 diabetes mellitus (T1D) and immunoregulatory activities in non-obese diabetes (NOD) mice with spontaneous and cyclophosphamide (Cyp)-accelerated diabetes. EPE was vehicle-administered to spontaneous NOD (S-NOD) mice or Cyp-accelerated NOD (Cyp-NOD) mice once daily at a dose of 400 mg/kg body weight for 15 or 4 weeks, respectively. At the end, blood samples were collected for biological analyses, organ tissues were dissected for analyses of histology and immunofluorescence (IF) staining (including expressions of Bcl and Bax), the expression levels of targeted genes by Western blotting and forkhead box P3 (Foxp3), and helper T lymphocyte 1 (Th1)/Th2/Th17/Treg regulatory T cell (Treg) cell distribution by flow cytometry. Our results showed that EPE-treated NOD mice or Cyp-accelerated NOD mice display a decrease in levels of blood glucose and HbA1c, but an increase in blood insulin levels. EPE treatment decreased blood levels of IFN-γ and tumor necrosis α (TNF-α) by Th1 cells, and reduced interleukin (IL)-1β and IL-6 by Th17 cells, but increased IL-4, IL-10, and transforming growth factor-β1 (TGF-β1) by Th2 cells in both of the two mice models by enzyme-linked immunosorbent assay (ELISA) analysis. Flow cytometric data showed that EPE-treated Cyp-NOD mice had decreased the CD4⁺ subsets T cell distribution of CD4⁺IL-17 and CD4⁺ interferon gamma (IFN-γ), but increased the CD4⁺ subsets T cell distribution of CD4⁺IL-4 and CD4⁺Foxp3. Furthermore, EPE-treated Cyp-NOD mice had decreased the percentage per 10,000 cells of CD4⁺IL-17 and CD4⁺IFNγ, and increased CD4⁺IL-4 and CD4⁺Foxp3 compared with the Cyp-NOD Con group (p < 0.001, p < 0.05, p < 0.05, and p < 0.05, respectively). For target gene expression levels in the pancreas, EPE-treated mice had reduced expression levels of inflammatory cytokines, including IFN-γ and TNF-α by Th1 cells, but increased expression levels of IL-4, IL-10, and TGF-1β by Th2 cells in both two mice models. Histological examination of the pancreas revealed that EPE-treated mice had not only increased pancreatic insulin-expressing β cells (brown), and but also enhanced the percentage of Bcl-2 (green)/Bax (red) by IF staining analyses of islets compared with the S-NOD Con and the Cyp-NOD Con mice, implying that EPE displayed the protective effects of pancreas β cells. EPE-treated mice showed an increase in the average immunoreactive system (IRS) score on insulin within the pancreas, and an enhancement in the numbers of the pancreatic islets. EPE displayed an improvement in the pancreas IRS scores and a decrease in proinflammatory cytokines. Moreover, EPE exerted blood-glucose-lowering effects by regulating IL-17 expressions. Collectively, these results implied that EPE inhibits the development of autoimmune diabetes by regulating cytokine expression. Our results demonstrated that EPE has a therapeutic potential in the preventive effects of T1D and immunoregulation as a supplementary. Full article

The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scenario. The sCTs were evaluated by means of similarity metrics (e.g., mean absolute error, MAE) and geometrical criteria (e.g., dice coefficient). Recalculated CIRT plans were evaluated through dose volume histogram, gamma analysis and range shift analysis. The CT-based test set presented optimal MAE on bones (86.03 ± 10.76 HU), soft tissues (55.39 ± 3.41 HU) and air (54.42 ± 11.48 HU). Higher values were obtained from the MRI-only test set (MAE_BONE = 154.87 ± 22.90 HU). The global gamma pass rate reached 94.88 ± 4.9% with 3%/3 mm, while the range shift reached a median (IQR) of 0.98 (3.64) mm. The three-channel cGAN can generate acceptable abdominal sCTs and allow for CIRT dose recalculations comparable to the clinical plans. Full article

Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods. Full article

This paper studies the synergistic effect of total ionizing dose (TID) and displacement damage dose (DDD) in enhancement-mode GaN high electron mobility transistor (HEMT) based on the p-GaN gate and cascode structure using neutron and ⁶⁰Co gamma-ray irradiation. The results show that when the accumulated gamma-ray doses are up to 800k rad(Si), the leakage-current degradations of the two types of GaN HEMTs with 14 MeV neutron irradiation of 1.3 × 10¹² n/cm² and 3 × 10¹² n/cm² exhibit a lower degradation than the sum of the two separated effects. However, the threshold voltage shifts of the cascode structure GaN HEMT show a higher degradation when exposed to both TID and DDD effects. Moreover, the failure mechanisms of the synergistic effect in GaN HEMT are investigated using the scanning electron microscopy technique. It is shown that for the p-GaNHEMT, the increase in channel resistance and the degradation of two-dimensional electron gas mobility caused by neutron irradiation suppresses the increase in the TID leakage current. For the cascode structure HEMT, the neutron radiation-generated defects in the oxide layer of the metal–oxide–semiconductor field-effect transistor might capture holes induced by gamma-ray irradiation, resulting in a further increase in the number of trapped charges in the oxide layer. Full article

Third-generation semiconductor materials have a wide band gap, high thermal conductivity, high chemical stability and strong radiation resistance. These materials have broad application prospects in optoelectronics, high-temperature and high-power equipment and radiation detectors. In this work, thin-film solid state neutron detectors made of four third-generation semiconductor materials are studied. Geant4 10.7 was used to analyze and optimize detectors. The optimal thicknesses required to achieve the highest detection efficiency for the four materials are studied. The optimized materials include diamond, silicon carbide (SiC), gallium oxide (Ga${}_2$O${}_3$) and gallium nitride (GaN), and the converter layer materials are boron carbide (B${}_4$C) and lithium fluoride (LiF) with a natural enrichment of boron and lithium. With optimal thickness, the primary knock-on atom (PKA) energy spectrum and displacements per atom (DPA) are studied to provide an indication of the radiation hardness of the four materials. The gamma rejection capabilities and electron collection efficiency (ECE) of these materials have also been studied. This work will contribute to manufacturing radiation-resistant, high-temperature-resistant and fast response neutron detectors. It will facilitate reactor monitoring, high-energy physics experiments and nuclear fusion research. Full article

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution. Full article

High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose a GAN training optimization model for converting LDR images into HDR images. First, a gamma shift method is proposed for training the GAN model with an extended luminance range. Next, a weighted loss map trains the GAN model for tone compression in the local area of images. Then, a regional fusion training model is used to balance the training method with the regional weight map and the restoring speed of local tone training. Finally, because the generated module tends to show a good performance in bright images, mean gamma tuning is used to evaluate image luminance channels, which are then fed into modules. Tests are conducted on foggy, dark surrounding, bright surrounding, and high-contrast images. The proposed model outperforms conventional models in a comparison test. The proposed model complements the performance of an object detection model even in a real night environment. The model can be used in commercial closed-circuit television surveillance systems and the security industry. Full article

Prompt gamma imaging is one of the emerging techniques used in proton therapy for in-vivo range verification. Prompt gamma signals are generated during therapy due to the nuclear interaction between beam particles and nuclei of the tissue that is detected and processed in order to obtain the position and energy of the event so that the benefits of Bragg’s peak can be fully utilized. This work aims to develop a gallium nitride (GaN)-based readout system for position-sensitive detectors. An operational amplifier is the module most used in such a system to process the detector signal, and a GaN-based operational amplifier (OPA) is designed and simulated in LTSpice. The designed circuit had an open-loop gain of 70 dB and a unity gain frequency of 34 MHz. The slew rate of OPA was 20 V/μs and common mode rejection ratio was 84.2 dB. A simulation model of the readout circuit system using the GaN-based operational amplifier was also designed, and the result showed that the system can successfully process the prompt gamma signals. Due to the radiation hardness of GaN devices, the readout circuit system is expected to be more reliable than its silicon counterpart. Full article

Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local control rates and better sparing of organs at risk. An MRI-only workflow could reduce the risk of misalignment between magnetic resonance imaging (MRI) brain studies and computed tomography (CT) scanning for SRT planning, while shortening delays in planning. Given the absence of a calibrated electronic density in MRI, we aimed to assess the equivalence of synthetic CTs generated by a generative adversarial network (GAN) for planning in the brain SRT setting. Methods: All patients with available MRIs and treated with intra-cranial SRT for brain metastases from 2014 to 2018 in our institution were included. After co-registration between the diagnostic MRI and the planning CT, a synthetic CT was generated using a 2D-GAN (2D U-Net). Using the initial treatment plan (Pinnacle v9.10, Philips Healthcare), dosimetric comparison was performed using main dose-volume histogram (DVH) endpoints in respect to ICRU 91 guidelines (Dmax, Dmean, D2%, D50%, D98%) as well as local and global gamma analysis with 1%/1 mm, 2%/1 mm and 2%/2 mm criteria and a 10% threshold to the maximum dose. t-test analysis was used for comparison between the two cohorts (initial and synthetic dose maps). Results: 184 patients were included, with 290 treated brain metastases. The mean number of treated lesions per patient was 1 (range 1–6) and the median planning target volume (PTV) was 6.44 cc (range 0.12–45.41). Local and global gamma passing rates (2%/2 mm) were 99.1 CI95% (98.1–99.4) and 99.7 CI95% (99.6–99.7) respectively (CI: confidence interval). DVHs were comparable, with no significant statistical differences regarding ICRU 91′s endpoints. Conclusions: Our study is the first to compare GAN-generated CT scans from diagnostic brain MRIs with initial CT scans for the planning of brain stereotactic radiotherapy. We found high similarity between the planning CT and the synthetic CT for both the organs at risk and the target volumes. Prospective validation is under investigation at our institution. Full article

GaN high-electron-mobility transistors (HEMTs) are promising next-generation devices in the power electronics field which can coexist with silicon semiconductors, mainly in some radiation-intensive environments, such as power space converters, where high frequencies and voltages are also needed. Its wide band gap (WBG), large breakdown electric field, and thermal stability improve actual silicon performances. However, at the moment, GaN HEMT technology suffers from some reliability issues, one of the more relevant of which is the dynamic on-state resistance (R_{ON_dyn}) regarding power switching converter applications. In this study, we focused on the drain-to-source on-resistance (R_DSON) characteristics under ⁶⁰Co gamma radiation of two different commercial power GaN HEMT structures. Different bias conditions were applied to both structures during irradiation and some static measurements, such as threshold voltage and leakage currents, were performed. Additionally, dynamic resistance was measured to obtain practical information about device trapping under radiation during switching mode, and how trapping in the device is affected by gamma radiation. The experimental results showed a high dependence on the HEMT structure and the bias condition applied during irradiation. Specifically, a free current collapse structure showed great stability until 3.7 Mrad(Si), unlike the other structure tested, which showed high degradation of the parameters measured. The changes were demonstrated to be due to trapping effects generated or enhanced by gamma radiation. These new results obtained about R_{ON_dyn} will help elucidate trap behaviors in switching transistors. Full article