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This study develops an improved Feldkamp–Davis–Kress (FDK) reconstruction algorithm using non-local total variation (NLTV) denoising and a cubic B-spline interpolation-based backprojector to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The NLTV objective function is minimized on all log-transformed projections using steepest gradient descent optimization with an adaptive control of the step size to augment the difference between a real structure and noise. The proposed algorithm was evaluated using a phantom data set acquired from a low-dose protocol with lower milliampere-seconds (mAs).The combination of NLTV minimization and cubic B-spline interpolation rendered the enhanced reconstruction images with significantly reduced noise compared to conventional FDK and local total variation with anisotropic penalty. The artifacts were remarkably suppressed in the reconstructed images. Quantitative analysis of reconstruction images using low-dose projections acquired from low mAs showed a contrast-to-noise ratio with spatial resolution comparable to images reconstructed using projections acquired from high mAs. The proposed approach produced the lowest RMSE and the highest correlation. These results indicate that the proposed algorithm enables application of the conventional FDK algorithm for low mAs image reconstruction in low-dose CBCT imaging, thereby eliminating the need for more computationally demanding algorithms. The substantial reductions in radiation exposure associated with the low mAs projection acquisition may facilitate wider practical applications of daily online CBCT imaging.

Accurate multi-leaf collimator (MLC) motion prediction is a prerequisite for precise dose delivery in advanced techniques such as IMRT and VMAT. Traditional patient-specific quality assurance (QA) methods remain resource-intensive and prone to physical measurement uncertainties. This study aimed to develop machine learning (ML) models to predict delivered MLC positions using kinematic parameters extracted from DICOM-RT plans for the Elekta Versa HD system. A dataset comprising 200 patient plans was constructed by pairing planned MLC positions, velocities, and accelerations with corresponding delivered values parsed from unstructured trajectory logs. Four regression models, including linear regression (LR), were trained to evaluate the deterministic nature of the Elekta servo-mechanism. LR demonstrated superior prediction accuracy, achieving the lowest mean absolute error (MAE) of 0.145 mm, empirically confirming the fundamentally linear relationship between planned and delivered trajectories. Subsequent dosimetric validation using ArcCHECK measurements on 17 clinical plans revealed that LR-corrected plans achieved statistically significant improvements in gamma passing rates, with a mean increase of 2.24% under the stringent 1%/1 mm criterion (p < 0.001). These results indicate that the LR model successfully captures systematic mechanical signatures, such as inertial effects. This study demonstrates that a computationally efficient LR model can accurately predict Elekta MLC performance, providing a robust foundation for implementing ML-based virtual QA. This approach is particularly valuable for time-sensitive workflows like adaptive radiotherapy (ART), as it significantly reduces reliance on physical QA resources.

The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.

To the best of our knowledge there have been no randomized controlled trials comparing lobectomy—a standard treatment for patients with early-stage non-small cell lung cancer (NSCLC)—and particle beam therapy (PBT), the best performing existing radiotherapy. We conducted a virtual randomized trial in medically operable patients with stage IA NSCLC to compare lobectomy and PBT effectiveness. A Markov model was developed to predict life expectancy after lobectomy and PBT in a cohort of patients with stage IA NSCLC. Ten thousand virtual patients were randomly assigned to each group. Sensitivity analyses were performed as model variables and scenarios changed to determine which treatment strategy was best for improving life expectancy. All estimated model parameters were determined using variables extracted from a systematic literature review of previously published articles. The preferred strategy differed depending on patient age. In young patients, lobectomy showed better life expectancy than that of PBT. The difference in life expectancy between lobectomy and PBT was statistically insignificant in older patients. Our model predicted lobectomy as the preferred strategy when operative mortality was under 5%. However, the preferred strategy changed to PBT if operative mortality post lobectomy was over 5%. For medically operable patients with stage IA NSCLC, our Markov model revealed the preferred strategy of lobectomy or PBT regarding operative mortality changed with varying age and comorbidity. Until randomized controlled trial results become available, we hope the current results will provide a rationale background for clinicians to decide treatment modalities for patients with stage IA NSCLC.

The Leksell Gamma Knife (LGK) Icon TM is used for mask‐based and frame‐based fixation. The mask fixation provides a noninvasive method. However, an optimal mask fixation method is yet to be established. We evaluated the characteristics of three mask fixation methods (Plain, Folded, and Wide) for the LGK Icon TM . Force‐sensitive resistor sensors were attached to the forehead, supraorbital, zygoma, mandible, and occipital bone of the phantom, and digital humidity and temperature sensors were attached to both temporal lobes. Cone‐beam computed tomography (CBCT) and high‐definition motion management (HDMM) for each mask fixation method were used to evaluate the phantom motion during the initial application. Subsequently, the mask was removed and reapplied on the second (1st reapplication) and third days (2nd reapplication). In the initial application, forces acting on most portions of the phantom were stabilized within 1.5 h. The largest force acted on the occipital bone for the Plain and Wide methods and on the mandible for the Folded method. The temperature rapidly approaches the initial temperature, whereas the humidity gradually approached the initial humidity in all fixation methods. The Folded method exhibited a significantly lower translation along the Y‐axis of the Leksell coordinate system, and rotations along all axes were under 0.5°. The HDMM values remained at 0.1 mm for all fixation methods. In the reapplications, the force acting on the occipital bone was significantly greater than that during the initial application for all mask fixation methods; the temperature and humidity remained unchanged. All mask fixation methods in the 1st reapplication were not significantly different from those in the 2nd reapplication. The Folded method is recommended as an optimal mask fixation for patients who require tight fixation; the Wide method can be considered if patient comfort is a priority.

This study aimed to investigate the feasibility of the manual expansion method with normal tissue complication probability (NTCP) for dose escalation. The dose-volume histogram (DVH) of the previously published data from proton and photon therapy plans was used to evaluate the effect of NTCP on dose escalation. The escalated DVH was manually obtained by increasing dose up to 1.8-times based on previously published data. Various NTCP calculation models have been applied to the DVH of patients with prostate cancer. The feasibility of the manual expansion plan method was verified by comparing it with an optimized plan using a treatment planning system (TPS). The risk ratio was investigated to determine the possible dose escalation ratio according to a rectal radiation therapy oncology group (RTOG) toxicity of ≥ grade 2. In addition, the relative biological effectiveness (RBE) uncertainty of NTCP was investigated using a linear-quadratic (LQ)-model-based equivalent dose of 2 Gy. Manual dose expansion, up to 1.8-times for rectum and bladder, showed acceptable DVH compared to the DVH optimized by TPS for both proton pencil-beam scanning (PBS) and photon volumetric modulated arc therapy (VMAT) plans. The risk ratio can provide information for toxicity-related dose escalation range for analysis of the risk ratio of proton therapy to photon therapy for the rectum even though the NTCP of proton therapy is higher than that of photon therapy. In the hypo-fractionated scheme, RBE uncertainty results in severe late toxicity for the rectum for the dose escalation ratio of up to 1.3-times. The manual expansion method is feasible with a specific treatment modality and can be a useful tool for dose escalation studies.

The Leksell Gamma Knife (LGK) IconTM is used for mask‐based and frame‐based fixation. The mask fixation provides a noninvasive method. However, an optimal mask fixation method is yet to be established. We evaluated the characteristics of three mask fixation methods (Plain, Folded, and Wide) for the LGK IconTM. Force‐sensitive resistor sensors were attached to the forehead, supraorbital, zygoma, mandible, and occipital bone of the phantom, and digital humidity and temperature sensors were attached to both temporal lobes. Cone‐beam computed tomography (CBCT) and high‐definition motion management (HDMM) for each mask fixation method were used to evaluate the phantom motion during the initial application. Subsequently, the mask was removed and reapplied on the second (1st reapplication) and third days (2nd reapplication). In the initial application, forces acting on most portions of the phantom were stabilized within 1.5 h. The largest force acted on the occipital bone for the Plain and Wide methods and on the mandible for the Folded method. The temperature rapidly approaches the initial temperature, whereas the humidity gradually approached the initial humidity in all fixation methods. The Folded method exhibited a significantly lower translation along the Y‐axis of the Leksell coordinate system, and rotations along all axes were under 0.5°. The HDMM values remained at 0.1 mm for all fixation methods. In the reapplications, the force acting on the occipital bone was significantly greater than that during the initial application for all mask fixation methods; the temperature and humidity remained unchanged. All mask fixation methods in the 1st reapplication were not significantly different from those in the 2nd reapplication. The Folded method is recommended as an optimal mask fixation for patients who require tight fixation; the Wide method can be considered if patient comfort is a priority.

Axons in central nervous system （CNS） do not regenerate spontaneously after injuries such as stroke and traumatic spinal cord iniury. Both intrinsic and extrinsic factors are responsible for the regeneration fail- ure, Although intensive research efforts have been invested on extrinsic regeneration inhibitors, the extent to which glial inhibitors contribute to the regeneration failure in viva still remains elusive. Recent exper- imental evidence has rekindled interests in intrinsic factors for the regulation of regeneration capacity in adult mammals. In this review, we propose that activating macrophages with pro-regenerative molecular signatures could be a novel approach for boosting intrinsic regenerative capacity of CNS neurons. Using a conditioning injury model in which regeneration of central branches of dorsal root ganglia sensory neu- rons is enhanced by a preceding injury to the peripheral branches, we have demonstrated that perineuronal macrophages surrounding dorsal root ganglia neurons are critically involved in the maintenance of en- hanced regeneration capacity. Neuron-derived chemokine （C-C motif） ligand 2 （CCL2） seems to mediate neuron-macrophage interactions conveying injury signals to perineuronal macrophages taking on a soley pro-regenerative phenotype, which we designate as regeneration-associated macrophages （RAMs）. Ma- nipulation of the CCL2 signaling could boost regeneration potential mimicking the conditioning injury, suggesting that the chemokine-mediated RAM activation could be utilized as a regenerative therapeutic strategy for CNS injuries.

In recent years, the use of PACS for radiation therapy has become the norm in hospital environment and has suggested for collecting data and management from different TPSs with DICOM objects. However, some TPS does not provide the DVH exportation with text or other format. In addition, plan review systems for various TPSs often allow DVH recalculation with different algorithms. These algorithms result in the inevitable discrepancy between the values obtained with the recalculation and those obtained with TPS itself. The purpose of this study was to develop a simple method for generating reproducible DVH values obtained from the TPSs. Treatment planning information including structures and delivered dose was exported by the DICOM format from planning systems. The supersampling and trilinear interpolation methods were employed to calculate DVH data from 35 treatment plans. The discrepancies between DVHs extracted from each TPS and the proposed calculation method were evaluated with respect to the supersampling ratio. The volume, minimum dose, maximum dose, and mean dose were compared. The variation of DVHs from multiple TPSs was compared with a commercially available treatment planning comparison tool. The overall comparisons of the volume, minimum dose, maximum dose, and mean dose showed that the proposed method generated relatively smaller discrepancies compared with TPS than those by MIM software and TPS. As the structure volume decreased, the overall percent difference increased. Most large difference was observed in the small organs such as eye ball, lens, optic nerve which had below 10 cc volume. A simple and useful technique was developed to generate DVH with acceptable error from a proprietary TPS. This study provides the convenient and common framework which allows to use a single well-managed storage solution for the independent information system.

Energy and environmental issues presently attract a great deal of scientific attention. Recently, two-dimensional MXenes and MXene-based nanomaterials have attracted increasing interest because of their unique properties (e.g., remarkable safety, a very large interlayer spacing, environmental flexibility, a large surface area, and thermal conductivity). In 2011, multilayered MXenes (Ti 3 C 2 T x , a new family of two-dimensional (2D) materials) produced by etching an A layer from a MAX phase of Ti 3 AlC 2 , were first described by researchers at Drexel University. The term “MXene” was coined to distinguish this new family of 2D materials from graphene, and applies to both the original MAX phases and MXenes fabricated from them. We present a comprehensive review of recent studies on energy and environmental applications of MXene and MXene-based nanomaterials, including energy conversion and storage, adsorption, membrane, photocatalysis, and antimicrobial. Future research needs are discussed briefly with current challenges that must be overcome before we completely understand the extraordinary properties of MXene and MXene-based nanomaterials.