OBJECTIVES:To construct a Transformer-based multimodal data encoding model for predicting hospital-acquired infections (HAI). METHODS:Laboratory test data of 300 000 patients were extracted from the publicly available MIMIC-IV database. The laboratory data of 1172 patients and chest X-ray images from 274 of these patients were collected from Nanfang Hospital. A novel Transformer-based encoding model was developed to process the data, which was then connected to a machine learning classifier for predicting HAI. The radiomic and deep learning features were extracted from the chest X-ray images for predicting ventilator-associated pneumonia (VAP). These imaging features were subsequently integrated with the laboratory test data using a feature fusion algorithm. The model performance was evaluated by assessing the accuracy, the area under the ROC curve (AUC), sensitivity, and specificity. The proposed algorithm was quantitatively compared against traditional machine learning classifiers to validate its effectiveness and feasibility. RESULTS:The results demonstrated that the model developed in this study achieved an AUC of 0.989 in the internal validation set. In the external validation set, the optimal model for predicting HAI attained an AUC of 0.98, and following the integration of imaging features, the optimal model reached an AUC of 0.93 in the VAP prediction task, demonstrating superior performance over the baseline models. CONCLUSIONS:The Transformer-based model for processing laboratory test data has excellent predictive capability and good clinical applicability for HAI prediction with also good performance for predicting VAP.

Nasopharyngeal carcinoma is a prevalent malignancy in the head and neck region, where accurate delineation of the gross tumor volume of the nasopharyngeal lesion and clinical target volume is essential for precise radiotherapy planning. However, existing segmentation approaches often rely on multiple imaging modalities, substantially increasing the clinical burden of image acquisition and manual annotation. This study aims to achieve multimodal-level segmentation performance using only single-modality input, thereby reducing clinical costs while maintaining high accuracy. To this end, we propose a novel two-stage deep learning framework that integrates multimodal synthetic image generation and feature aggregation for cost-effective nasopharyngeal carcinoma tumor segmentation. In the first stage, a re-implemented synchronous generation network synthesizes intermediate multimodal magnetic resonance images from a single T1-weighted scan, effectively reconstructing cross-modality information without requiring additional imaging sequences. In the second stage, a Convolutional neural network-Transformer hybrid network incorporates cross-modality attention and feature aggregation mechanisms to leverage the generative priors for improved tumor boundary representation and contextual understanding. Extensive experiments were conducted on 167 retrospectively collected clinical nasopharyngeal carcinoma cases. The proposed framework achieves mean dice similarity coefficients of 0.81 +/- 0.03 for the gross tumor volume of the nasopharyngeal lesion and 0.85 +/- 0.04 for clinical target volume, comparable to multimodal segmentation networks while requiring only single-modality input. Moreover, the method demonstrates superior robustness and consistency across different tumor stages. This novel framework significantly enhances segmentation accuracy while reducing the dependency on multiple medical imaging modalities, paving the way for low-cost and high-accuracy nasopharyngeal carcinoma diagnosis and treatment.

Radiotherapy (RT) for treating breast cancer can result in incidental dose deposition to the liver, leading to functional impairment. FLASH radiotherapy (FLASH-RT), delivered at ultra-high dose rates (UHDR), has demonstrated remarkable normal tissue sparing. We investigated the hepatoprotective effects and metabolic alterations associated with FLASH-RT and conventional radiotherapy (CONV-RT) in a preclinical breast cancer model. Female BALB/c mice bearing syngeneic 4T1 breast tumors were randomized to receive a single 20 Gy fraction of either FLASH-RT (625 Gy/s) or CONV-RT (0.54 Gy/s) using a 6 MeV electron beam. Tumor kinetics and systemic toxicity were monitored for 14 days. Hepatic integrity was assessed via histology, immunohistochemistry, oxidative-stress markers, serum biochemical assays, and non-targeted UPLC-MS/MS metabolomics of liver tissue. Both modalities achieved isoeffective tumor growth delay. However, FLASH-RT induced only transient body-weight reduction followed by rapid recovery, and was associated with reduced hepatic injury markers compared with CONV-RT. Histopathological analysis revealed that FLASH-RT preserved hepatic architecture and attenuated early pro-fibrotic signaling, as reflected by reduced α-SMA and Nestin expression compared with CONV-RT. FLASH-treated mice exhibited significantly lower serum ALT/ALP levels and reduced oxidative stress (lower MDA levels and higher GSH-PX levels). Furthermore, FLASH-RT attenuated immune-mediated inflammation, as evidenced by diminished infiltration of CD8 + T cells and F4/80 + macrophages. Metabolomic profiling demonstrated that FLASH-RT preserved hepatic metabolic homeostasis, preventing the profound lipid and choline metabolism disruptions observed following CONV-RT. FLASH-RT was associated with substantial hepatic sparing by preserving structural integrity, attenuating oxidative–inflammatory signaling, and maintaining metabolic stability. These findings suggest that FLASH-RT may help expand the therapeutic window by uncoupling antitumor efficacy from collateral hepatic toxicity.

Multi-source computed tomography (MSCT) significantly improves temporal resolution but suffers from severe forward and cross scatter artifacts. Software-based scatter correction methods avoid additional hardware costs and radiation dose; however, model-based methods struggle with high-order scatter estimation, while deep learning-based methods lack physical constraints. To address these limitations, we propose a neural-analytical fusion (NAF) scatter correction method. Specifically, first-order Compton and Rayleigh scatter are analytically estimated using a GPU-accelerated physics model-based method. For high-order scatter, the multi-step scattering process is reformulated as an equivalent single-step interaction, and an equivalent high-order cross-section prediction network (EHCP-Net) is embedded within the analytical model to enable fast and physically constrained estimation of high-order scatter. The proposed method is validated on simulated and real data under different scanning geometries. Compared to the state-of-the-art scatter correction methods, our method demonstrates higher correction accuracy and effectively suppresses high-frequency artifacts. In scatter distribution comparison experiments, it achieved a mean absolute percentage error (MAPE) of less than 3% relative to Monte Carlo (MC) simulations. In scatter correction experiments, it yielded mean absolute errors (MAE) below 20 HU on simulated data, and below 30 HU on real data. The proposed method achieves effective scatter correction with strong physical constraints for MSCT.

PURPOSE:Accurate prediction of distant metastasis (DM) and locoregional recurrence (LR) in head and neck cancer (HNC) patients following radiotherapy is critical for individualized treatment planning. This study aimed to develop and validate an explainable deep learning model integrating anatomical and dosimetric information for HNC DM and LR risk stratification. METHODS AND MATERIALS:237 HNC patients treated with definitive radiotherapy were collected and divided into training, internal, and external validation cohorts. A dual-task deep learning model based on 3D squeeze-and-excitation residual networks was constructed to predict DM and LR risk using CT images, 3D dose distributions, and gross tumor volume (GTV) masks. Model performance was evaluated by concordance index (C-index), time-dependent ROC, and decision curve analysis. Interpretability was enhanced using Grad-CAM and a novel Activation Volume Histogram (AVH) method to quantify attention patterns across spatial-dosimetric subregions. RESULTS:The model achieved C-indices of 0.92 and 0.74 for DM and LR, respectively, outperforming radiomic models. Grad-CAM visualizations revealed distinct activation patterns aligned with recurrence sites- lymphatic regions for DM and tumor zones for LR. AVH analysis identified GTV extended by 3mm receiving dose ≥ 65 Gy and GTV receiving dose ≥ 65 Gy as the most discriminative subregions, showing significant differences in activation distributions between high- and low-risk groups, particularly within the 0.4-0.5 intensity range. CONCLUSION:This dual-task model enables accurate DM risk prediction and showed potential for LR risk prediction in HNC, providing spatial-dosimetric information to support risk-adaptive radiotherapy. Further extensive multicenter validation is warranted to confirm its clinical applicability.

PURPOSE:We propose a CT number-preserving bidirectional cycle generative adversarial network (BiHU-GAN) for two-way synthesis between non-contrast CT (NCCT) and contrast-enhanced CT (CECT) in head-and-neck radiotherapy, aiming to generate synthetic CECT (sCECT) from NCCT to improve target delineation, and synthetic NCCT (sNCCT) from CECT to enable accurate dose calculation. METHODS AND MATERIALS:BiHU-GAN integrates an explicit CT number deviation loss (enforcing CT number fidelity), a gradient-consistency loss (preserving structural edges), and a convolutional block attention module (CBAM)for low-contrast feature enhancement. Experiments were conducted on 606 paired head-and-neck NCCT/CECT cases (118,779 slices) from our institution and externally validated on 140 paired cases from SegRap2023/2025. We benchmarked against recent state-of-the-art methods using standard image-quality assessments and blinded 5-point clinical scoring. In addition, we assessed contouring consistency to validate sCECT for target delineation, and dose calculation accuracy to verify sNCCT for radiotherapy dose calculation. RESULTS:BiHU-GAN outperformed all other methods. sCECT closely matched the CECT in structural similarity and CT number fidelity, supporting reliable nodal target delineation, with high contour agreement to references. At least 85% of synthetic images were rated clinically usable (score 4 or 5 on the 5‑point clinical scoring system), reaching 96% in the internal cohort. sNCCT maintained excellent CT number accuracy (with a global mean absolute deviation of CT numbers ≤ 5.5 Hounsfield units) and high clinical acceptability, with 89%-95% clinically usable ratings. Dose calculations on sNCCT showed no significant differences from NCCT in dose-volume histogram endpoints or 3D gamma pass rates at the 2%/2 mm and 3%/3 mm criteria (gamma pass rates ≥ 95%). By contrast, direct dose calculation on CECT yielded larger systematic dose deviations and lower gamma pass rates. CONCLUSIONS:BiHU-GAN generates anatomically faithful and CT number-accurate synthetic images-improving delineation via sCECT while preserving dose accuracy via sNCCT. It avoids contrast and extra scans, reduces patient burden, and suits contrast-contraindicated or resource-limited settings. Robust multicenter performance supports broad clinical adoption.

Different from the conventional X-ray point tube, the cold-cathode flat-panel X-ray source (FPXS) achieved planar integration of tens of thousands of X-ray point sources. However, the X-ray source distribution of the FPXS at the generating plane (Mo target) is unknown and cannot be directly measured, which hinders its practical use. To address this issue, we propose to decompose the X-ray distribution of the FPXS from the aliased projection of a coded mask. The aliased projection was a circular convolution of the source distribution of FPXS and the basic coded mask, which uniquely encodes the FPXS flux. Besides, the maximum-likelihood expectation-maximization (ML-EM) algorithm was derived to reconstruct the source distribution. Both Monte Carlo (MC) simulation and real experiments were conducted to demonstrate the effectiveness of the proposed method. In the real experiments, the X-ray emission shapes formed by placing lead plates on the surface of the anode substrate of two FPXSs were successfully reconstructed by the proposed method. Moreover, knowing and then compensating the non-uniformity distribution of the FPXS is paramount for future novel and practical applications of the X-ray source.

BACKGROUND:The development of multi-source cone-beam computed tomography (CBCT) systems has significantly improved imaging speed, making them crucial in various clinical and research applications, particularly in dynamic studies. However, the simultaneous exposure of multiple X-ray sources increases the complexity of scatter within the system. The detector receives not only forward scattering from the directly aligned source but also cross scattering from other sources. Traditional scatter correction methods are mainly designed for single-source or dual-source CT, which often fail to address the challenges posed by multi-source scenario. PURPOSE:The purpose of this work is to proposes a GPU-based Monte Carlo (MC) simulation method to accurately model and analyze the scatter distribution and the characteristics of different components in multi-source CBCT systems, as well as the impact of cross scattering on image reconstruction quality. METHODS:We utilize the GPU-based MC package gMCDRR to simulate multi-source CBCT system, including both flat-panel and spherical detector configurations. The simulation process consists of three main components: imaging system modeling, photon initialization, and the simulation of physical interactions in phantom. The whole process of photon interaction with the geometry and its arrival at the detector is simulated in parallel using multiple GPU cores to enhance computational efficiency. We compare the proposed method with Geant4 method to validate the simulation accuracy, the intensity of cross scattering and its impact on reconstructed images are also quantitively evaluated. RESULTS:The simulation results show that the scattering intensity in multi-source CBCT increases with the number of X-ray sources. The cross scattering is mainly affected by the relative deflection angle between the sources and detector, as well as the characteristics of the imaging object. The backward scattering intensity at 180° potentially may exceed forward scattering, and this effect is more pronounced in larger phantoms. The reconstructed images from multi-source CBCT are severely affected by the superimposed cross scattering, resulting in increased artifacts and inaccurate CT values, which can tremendously degrade the image quality. CONCLUSIONS:The proposed GPU-based MC simulation method can accurately model and analyze the scatter distribution and characteristics of different components in multi-source CBCT systems, which can be utilized to correct the cross scattering of the multi-source CBCT.

PURPOSE:This study aims to evaluate the feasibility of using flat-panel X-ray source (FPXS) for brachytherapy through preclinical animal irradiation experiments. MATERIAL AND METHODS:A low-kV FPXS electronic brachytherapy (EB) system was constructed for dosimetry measurements and experimental validation. The surface and depth dose characteristics of FPXS were measured employing a 34013 chamber. An FPXS-based EB workflow was established, enabling the calculation of required exposure times for FPXS at a given prescription dose. The accuracy of the delivered dose and the therapeutic efficacy of FPXS irradiation were validated through brachytherapy experiments conducted on murine models with breast cancer. RESULTS:The FPXS operates with a maximum voltage of up to 50 kV and a maximum surface dose rate exceeding 2.30 Gy/min. The surface dose rate and current demonstrate an exponential increase with voltage, and the dose rate exhibits a linear correlation with the current. The calculated cumulative dose delivered to mice in the experimental group was slightly higher than the average in-field dose measured by film (21.88±0.61 Gy vs. 20.83 ± 1.03 Gy). Following FPXS irradiation, the increase in tumor volume of mice in the experimental group was markedly less pronounced than that observed in the control group, and the survival rate of mice in the experimental group over time was significantly higher than that of the control group (66.67% vs 12.50%). CONCLUSION:The intensity of FPXS is comparable to current EB systems, and its surface dose rate is adequate for therapeutic applications. Animal irradiation experiments confirm the accuracy of FPXS-delivered doses and demonstrate its efficacy in tumor irradiation.

PURPOSE:Monte Carlo (MC)-based dose calculation provides high accuracy in radiotherapy but is limited by statistical uncertainty (SU), which introduces noise and increases computational demands. This study proposes a Statistical Uncertainty-aware deep learning framework with a Dual-Path Dilated Convolution Fusion architecture to improve the accuracy and efficiency of MC dose denoising. METHODS:We designed a three-channel convolutional neural network that integrates noisy MC dose distributions, corresponding SU maps, and CT images. The dual-path structure combines standard and dilated convolutions to extract both local anatomical features and global contextual information. The model was trained and validated on 69 clinical IMRT plans from three tumor sites (head-and-neck, brain, and lung), each with six levels of simulated noise generated by a GPU-based MC engine. High-particle MC doses served as the ground truth. A total of six sub-models were trained for different noise levels, and performance was evaluated using mean dose error (MDE), gamma passing rate (GPR), and dose-volume histogram (DVH) analysis. RESULTS:The SU-aware model outperformed its two-channel SU-agnostic counterpart across all tumor sites, with MDEs reduced to (0.84 ± 0.26)% for head-and-neck, (0.85 ± 0.21)% for brain, and (0.36 ± 0.09)% for lung. GPRs exceeded 97 % (3 %/3 mm) with ≥ 1 × 105 histories. Denoising was completed within seconds, enabling real-time application. CONCLUSIONS:By explicitly incorporating SU into the network, the proposed model achieves fast, accurate dose denoising.

OBJECTIVES:To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. METHODS:A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. RESULTS:In terms of the dosemetric indicators, D95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. CONCLUSIONS:The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.

BACKGROUND:The flat-panel x-ray source (FPXS) is an innovative x-ray source comprising millions of micro sources that emit low-energy x-rays. To date, the dosimetric characteristics of FPXS remain unclear, limiting its clinical applications. PURPOSE:This study aims to characterize the physical and dosimetric characteristics of FPXS through Monte Carlo (MC) dose calculation and experimental dosimetry measurements and to analyze the feasibility of FPXS as an electronic brachytherapy (EB) unit. METHODS:A measurement setup and two specialized measurement platforms were developed. By using an energy spectrometer, ionization chamber, and Gafchromic film, a measurement procedure for physical beam characteristics and dosimetric characteristics of FPXS was established, and a series of experimental measurements were conducted. A previously in-house developed MC simulation toolkit, designated gFPDMC, was employed to perform FPXS structure simulation and parallel dose calculation. The measured dose distributions were compared with the gFPDMC-calculated dose distributions, thereby validating the accuracy of gFPDMC. RESULTS:The measured energy spectra are consistent with the MC-simulated spectra with mean relative error (MRE) values < 9% and an average energy difference < 1.40%. The measured surface dose rate of FPXS increases exponentially with the operating voltage, with a maximum dose rate of 2.45 Gy/min at 40 kV; the depth dose rate decreases exponentially with the measurement depth and increases with the operating voltage. The doses calculated by gFPDMC, DOSXYZnrc, and Geant4 exhibit excellent consistency, with mean absolute error (MAE) values of 0.31% and 0.40%. The gFPDMC-calculated percentage depth dose (PDDs) exhibit identical voltage-dependent depth dose characteristics as measured PDDs, with deviations < 4%. CONCLUSIONS:The precision of gFPDMC was rigorously validated through comparison with DOSXYZnrc, Geant4, and experimentally measured dose. Adequate operating voltage and suitable dose rate demonstrate the feasibility of FPXS as an EB unit.

Background At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality. Methods A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans. Results The average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially D max of the brainstem and spinal cord, and D mean of the left and right parotid glands significantly decreased ( P < 0.05). Conclusion We have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.

Traditional X-ray imaging has difficulty in differentiating between soft biological tissues due to low absorption contrast. However, the Talbot-Lau grating interferometer (GI) could be a promising solution to this problem by enabling X-ray phase contrast imaging (XPCI). Unfortunately, the G0 grating has some limitations regarding fabrication and efficiency. To overcome this issue, we have developed a new cold cathode coherent-structure flat panel X-ray source (FPXS) that eliminates the need for G0. This innovation has the potential to significantly improve the capabilities of Talbot-Lau GI for phase contrast imaging in the future.

X-ray intraoperative radiotherapy (IORT) is an important method for treating specific tumors. Area-addressable transparent flat-panel X-ray source can achieve selective-area radiotherapy and direct optical imaging guide, which could enhance the capability of the current IORT technique and has not been reported to date. In this paper, an area-addressable transparent flat-panel X-ray source was realized using zinc oxide (ZnO) nanowire field emitter arrays (FEAs) and an indium tin oxide (ITO) transparent anode. Planar-gate ZnO nanowire FEAs were fabricated and demonstrated a good addressing performance and uniform electron emission characteristics. A maximum anode current density of 884 mu A/cm2 was measured at one area under gate-addressed emission conditions, and the current fluctuation was approximately 5.8% in 2.5 h. The planar-gated ZnO nanowire FEAs were applied in the transparent flat-panel X-ray source. A radiation dose rate of 5.76 mGy/s was measured at the anode surface of the flat-panel X-ray source under the application of 40 kV anode voltages. The reported X-ray source device has potential applications in advanced intraoperative radiotherapy.

The advantages of a flat-panel X-ray source (FPXS) make it a promising candidate for imaging applications. Accurate imaging-system modeling and projection simulation are critical for analyzing imaging performance and resolving overlapping projection issues in FPXS. The conventional analytical ray-tracing approach is limited by the number of patterns and is not applicable to FPXS-projection calculations. However, the computation time of Monte Carlo (MC) simulation is independent of the size of the patterned arrays in FPXS. This study proposes two high-efficiency MC projection simulators for FPXS: a graphics processing unit (GPU)-based phase-space sampling MC (gPSMC) simulator and GPU-based fluence sampling MC (gFSMC) simulator. The two simulators comprise three components: imaging-system modeling, photon initialization, and physical-interaction simulations in the phantom. Imaging-system modeling was performed by modeling the FPXS, imaging geometry, and detector. The gPSMC simulator samples the initial photons from the phase space, whereas the gFSMC simulator performs photon initialization from the calculated energy spectrum and fluence map. The entire process of photon interaction with the geometry and arrival at the detector was simulated in parallel using multiple GPU kernels, and projections based on the two simulators were calculated. The accuracies of the two simulators were evaluated by comparing them with the conventional analytical ray-tracing approach and acquired projections, and the efficiencies were evaluated by comparing the computation time. The results of simulated and realistic experiments illustrate the accuracy and efficiency of the proposed gPSMC and gFSMC simulators in the projection calculation of various phantoms.

The X-ray phase contrast imaging using a Talbot-Lau grating interferometer (GI) can achieve high resolution of low-contrast objects such as soft tissues. The periodic microstructured X-ray source is the key to achieving this technology. We have prepared a vacuum-encapsulated cold cathode periodic microstructured flat-panel X-ray source (FPXS) using zinc oxide (ZnO) nanowire field emitter arrays and microarrays of a Mo thin film transmission target. The device has an operating voltage of 40 kV and a current of 684 mu A. A radiation dose rate of 428 mu Gy/s was measured at a distance of 10 cm from the periodic microstructured FPXS. The X-ray emission distribution is characterized by optical images and radiochromic film, and the localized X-ray emission was confirmed. The X-ray imaging capability was validated by using projection imaging. Our device has potential applications in Talbot-Lau GI phase-contrast imaging.

BackgroundThe Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time.PurposeThis work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures.MethodsThe shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases.ResultsThe efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by similar to 37.8% and the one in the clinical case by similar to 20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation.ConclusionsThe proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.