We present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface topography images of the patient on the treatment couch. However, OSI cannot visualize internal anatomy. This study proposes an Advanced Surface Imaging (A-SI) framework to address this issue. In the proposed A-SI framework, a high-speed surface imaging camera consistently captures surface images during radiation delivery, and a CBCT imager captures single-angle X-ray projections at low frequency. The A-SI then utilizes a generative model to generate real-time volumetric images with full anatomy, referred to as Optical Surface-Derived cone beam computed tomography (OSD-CBCT), based on the real-time high-frequent surface images and the low-frequency collected single-angle X-ray projections. The generated OSD-CBCT can provide accurate tumor motion for precise radiation delivery. The A-SI framework uses a patient-specific generative model: physics-integrated consistency-refinement denoising diffusion probabilistic model (PC-DDPM). This model leverages patient-specific anatomical structures and respiratory motion patterns derived from four-dimensional CT (4DCT) during treatment planning. It then employs a geometric transformation module (GTM) to extract volumetric anatomy information from the single-angle X-ray projection. A simulation study with 22 lung cancer patients evaluated the A-SI framework supported by PC-DDPM. The results showed that the framework produced real-time OSD-CBCT with high reconstruction fidelity and precise tumor localization. This study demonstrates the potential of A-SI to enable real-time tumor tracking with minimal imaging dose, advancing SGRT for motion-associated cancers and interventional procedures.

Background: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from LA projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for X-ray spectra measurement or paired datasets for model training. Purpose: This work aims to facilitate the clinical applications of fast and low-dose DECBCT by developing a practical solution for image reconstruction in LA-DECBCT. Methods: An inter-spectral structural similarity-based regularization was integrated into the iterative image reconstruction in LA-DECBCT. By enforcing the similarity between the DE images, LA artifacts were efficiently reduced in the reconstructed DECBCT images. The proposed method was evaluated using four physical phantoms and three digital phantoms, demonstrating its efficacy in quantitative DECBCT imaging. Conclusions: The proposed method achieves accurate image reconstruction without the need for X-ray spectra measurement for optimization or paired datasets for model training, showing great practical value in clinical implementations of LA-DECBCT.

Objective: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. Approach: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Results: In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278+/-0.122%, PSNR of 33.783+/-0.824dB, SSIM of 0.964+/-0.009, NCC of 0.968+/-0.011, HRS of 4.543, and SUV Error of 0.255+/-0.318%, with an average generation time of 62 seconds per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12x faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973+/-0.066%, PSNR of 36.172+/-0.801dB, SSIM of 0.984+/-0.004, NCC of 0.990+/-0.005, HRS of 4.428, and SUV Error of 0.151+/-0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario.

The advantage of proton therapy over photon therapy lies in the Bragg peak effect, which allows protons to deposit most of their energy precisely at the tumor site, minimizing damage to surrounding healthy tissue. Despite this, the standard approach to clinical treatment planning does not fully consider the differences in biological effectiveness between protons and photons. Currently, a uniform Relative Biological Effectiveness (RBE) value of 1.1 is used in clinical settings to compare protons to photons, despite evidence that proton RBE can vary significantly. This variation underscores the need for more refined proton therapy treatment planning those accounts for the variable RBE. A critical parameter in assessing the RBE of proton therapy is the Dose-Average Linear Energy Transfer (LETd), which is instrumental in optimizing proton treatment plans. Accurate LETd distribution calculations require complex physical models and the implementation of sophisticated Monte-Carlo (MC) simulation software. These simulations are both computationally intensive and time-consuming. To address these challenges, we propose a Deep Learning (DL)-based framework aimed at predicting the LETd distribution map from the dose distribution map. This framework utilizes Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Normalized Cross Correlation (NCC) to measure discrepancies between MC-derived LETd and the LETd maps generated by our model. Our approach has shown promise in producing synthetic LETd maps from dose maps, potentially enhancing proton therapy planning through the provision of precise LETd information. This development could significantly contribute to more effective and individualized proton therapy treatments, optimizing therapeutic outcomes while further minimizing harm to healthy tissue.

BACKGROUND:Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings.PURPOSE:This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT.

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM).Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020).Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively.Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETddistributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETddistribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETdmap generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETdmaps generated by the proposed method are 0.096 ± 0.019 keVμm-1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keVμm-1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETdmaps from dose maps and has the potential to improve proton therapy planning by providing accurate LETdinformation.

Computed tomography (CT) imaging is widely used for medical diagnosis and image guidance for treatment. Metal artifacts are observed on the reconstructed CT images if metal implants are carried by patients due to the beam hardening effects. In this condition, the acquired projection data cannot be used for analytical reconstruction as they do not meet Tuy's data sufficiency condition. Numerous deep learning-based methods have been developed for metal artifact reduction (MAR), providing superior performance. Nevertheless, all the reported models are data-driven and require large-size referenced images for the manifold approximation. In this work, we propose a physics-driven sinogram manifold learning method, which fully exploits the projection data correlation in CT scanning for MAR, and the proposed method is ready to be extended to other data-incomplete CT reconstruction problems.

This study aims to enhance the resolution of Magnetic Resonance Imaging (MRI) using a cutting-edge diffusion probabilistic Deep Learning (DL) technique, addressing the challenges posed by long image acquisition times and limited scanning dimensions. In this research, we propose a novel approach utilizing a probabilistic DL model to synthesize High-Resolution MRI (HR-MRI) images from Low-Resolution (LR) inputs. The proposed model consists of two main steps. In the forward process, Gaussian noise is systematically introduced to LR images through a Markov chain. In the reverse process, a U-Net model is trained using a loss function based on Kullback-Leibler divergence, which maximizes the likelihood of producing ground truth images. We assess the effectiveness of our method on T2-FLAIR images from 120 brain patients in the public BraTS2020 T2-FLAIR database. To gauge performance, we compare our approach with a clinical bicubic model (referred to as Bicubic) and Conditional Generative Adversarial Networks (CGAN). On the BraTS2020 dataset, our framework enhances the Peak Signal-to-Noise Ratio (PSNR) of LR images by 7%, whereas CGAN results in a 3% reduction. The corresponding Multi-scale Structural similarity (MSSIM) values for the proposed method and CGAN are 0.972±0.017 and 0.966±0.024. In this study, we have examined the potential of a diffusion probabilistic DL framework to elevate MRI image resolution. Our proposed method demonstrates the capability to generate high-quality HR images while avoiding issues such as mode collapse or learning multimodal distributions, which are commonly observed in CGAN-based approaches. This framework has the potential to significantly reduce MRI acquisition times for HR imaging, thereby mitigating the risk of motion artifacts and crosstalk.

Background: Dual-energy imaging on cone-beam CT (CBCT) scanners has great potential in different clinical applications, including image-guided surgery and adaptive proton therapy. However, the clinical practice of dual-energy CBCT (DE-CBCT) has been hindered by the requirement of sophisticated hardware components. Purpose: In this work, we aim to propose a practical solution for single-scan dual-energy imaging on current CBCT scanners without hardware modifications, using two complementary limited-angle scans with a projection-consistent diffusion model. Methods: Our approach has two major components: data acquisition using two complementary limited-angle scans, and dual-energy projections restoration with subsequent FDK reconstruction. Two complementary scans at different kVps are performed in a single rotation by switching the tube voltage at the middle of the source trajectory, acquiring the mixed-spectra projection in a single CBCT scan. Full-sampled dual-energy projections are then restored by a projection-consistent diffusion model in a slice-by-slice manner, followed by the DE-CBCT reconstruction using the FDK algorithm. Results: The proposed method was evaluated in a simulation study of digital abdomen phantoms and a study of real rat data. In the simulation study, the proposed method produced DE-CBCT images at a mean absolute error (MAE) of 20 HU. In the small-animal study, reconstructed DE-CBCT images using the proposed method gave an MAE of 25 HU. Conclusion: This study demonstrates the feasibility of DE-CBCT imaging using two complementary limited-angle scans with a projection-consistent diffusion model in both half-fan and short scans. The proposed method may allow quantitative applications of DE-CBCT and enable DE-CBCT-based adaptive proton therapy.

Dual-Energy CT (DECT) has risen to prominence as a valuable instrument in diagnostic imaging, boasting a range of clinical applications. Contrast-DECT (C-DECT) is particularly useful in clinical by generating iodine density map, which could benefit radiation oncologists in treatment planning process. However, DECT scanners are not widely equipped among the radiation therapy centers. Moreover, side effects from iodine agents restrict the use of DECT iodine contrast imaging for all patients. The purpose of this work is to generate synthetic C-DECT images based on non-contrast single-energy CT (SECT) via deep learning (DL) method. 108 head-and-neck cancer patients' images were retrospectively investigated in this work. All patients were scanned with non-contrast SECT and contrast DECT protocols. A conditional Denoising Diffusion Probalistic Model (DDPM) was implemented to generate synthetic High-energy CT (H-CT) and Low-energy CT (L-CT). The training and application dataset was separated strictly, 100 patients' data were used as the training dataset and the rest eight patients' data were used as the application dataset. The performance of the proposed method was evaluated with three quantitative metrics including Mean Absolute Error (MAE), Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). For H-CT and L-CT, the quantitative evaluation results of MAE, SSIM and PSNR are 19.15±2.23 (HU) and 23.34±3.45 (HU), 0.74±0.13 and 0.75±0.19, 28.13±2.83 (dB) and 28.18±3.55 (dB), respectively. This approach holds potential significance for radiation therapy facilities lacking DECT scanners, as well as for specific patients who may not be suitable candidates for iodine agent injection.

BACKGROUND:Cone-beam computed tomography (CBCT) scans, performed fractionally (e.g., daily or weekly), are widely utilized for patient alignment in the image-guided radiotherapy (IGRT) process, thereby making it a potential imaging modality for the implementation of adaptive radiotherapy (ART) protocols. Nonetheless, significant artifacts and incorrect Hounsfield unit (HU) values hinder their application in quantitative tasks such as target and organ segmentations and dose calculation. Therefore, acquiring CT-quality images from the CBCT scans is essential to implement online ART in clinical settings. PURPOSE:This work aims to develop an unsupervised learning method using the patient-specific diffusion model for CBCT-based synthetic CT (sCT) generation to improve the image quality of CBCT. METHODS:The proposed method is in an unsupervised framework that utilizes a patient-specific score-based model as the image prior alongside a customized total variation (TV) regularization to enforce coherence across different transverse slices. The score-based model is unconditionally trained using the same patient's planning CT (pCT) images to characterize the manifold of CT-quality images and capture the unique anatomical information of the specific patient. The efficacy of the proposed method was assessed on images from anatomical sites including head and neck (H&N) cancer, pancreatic cancer, and lung cancer. The performance of the proposed CBCT correction method was evaluated using quantitative metrics including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). Additionally, the proposed algorithm was benchmarked against two other unsupervised diffusion model-based CBCT correction algorithms.

Iodine maps can be obtained from contrast enhanced dual-energy compute tomography (DECT) scans to emphasize iodine contrast agent uptake in cancer patients' tissues, which benefits radiation oncologists in the treatment planning process. However, DECT scanners are not widely equipped among the radiation therapy centers. Furthermore, certain patients, i.e., either with iodine allergies or renal dysfunction, are not suitable for iodine contrast DECT scans. The purpose of this work is to generate synthetic iodine maps based on non-contrast single-energy CT (SECT) images via deep learning (DL) method. 130 head-and-neck patients' images were retrospectively investigated in this work. All patients were scanned with non-contrast SECT and contrast DECT protocols. The ground truth iodine maps were generated from contrast DECT scans using vender software. A denoising diffusion probabilistic model (DDPM) was implemented to generate synthetic iodine maps. The training and application datasets were kept strictly separate, containing data from 100 and 8 patients respectively. A CycleGAN was implemented as a reference method to assess the proposed DDPM method. The accuracy of the proposed DDPM was evaluated using three quantitative metrics: Mean absolute error (MAE) (19.31 +/- 3.38 HU), structural similarity index (0.79 +/- 0.13) and peak signal-to-noise ratio (22.25 +/- 4.23dB) respectively. Compared to the reference method, the proposed method demonstrated superior performance, which was further corroborated by paired two-tailed t-tests, across these metrics. To our best knowledge, this work is the first of its kind to demonstrate the capability to provide synthetic iodine maps based on SECT via DDPM method.

Dual-energy cone-beam CT (DECBCT) has great potential for quantitative imaging tasks in CBCT-guided radiation therapy. However, the lack of a practical single-scan solution in data acquisition impedes the practice of CBCT-based adaptive radiotherapy (ART). In this work, we propose an efficient way to achieve DECBCT using the primary beam splitting in a single short scan or half-fan scan. To restore complete dual-energy sinograms sufficient for analytical image reconstruction, the conditional diffusion model is introduced to convert the acquired spectral-mixed sinogram to the complete dual-energy sinograms via a data refinement strategy. The proposed method is compared with the other two diffusion model-based methods and the preliminary results demonstrate the feasibility of the proposed method in quantitative imaging tasks, making it a promising solution to CBCT-based dose calculation and replanning.

Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. We propose an MRI-to-CT transformer-based denoising diffusion probabilistic model (MC-DDPM) to transform MRI into high-quality sCT to facilitate radiation treatment planning. MC-DDPM implements diffusion processes with a shifted-window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process which adds Gaussian noise to real CT scans, and a reverse process in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans. With an optimally trained Swin-Vnet, the reverse diffusion process was used to generate sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on a brain dataset and a prostate dataset. Qualitative evaluation was performed using the mean absolute error (MAE) of Hounsfield unit (HU), peak signal to noise ratio (PSNR), multi-scale Structure Similarity index (MS-SSIM) and normalized cross correlation (NCC) indexes between ground truth CTs and sCTs. MC-DDPM generated brain sCTs with state-of-the-art quantitative results with MAE 43.317 HU, PSNR 27.046 dB, SSIM 0.965, and NCC 0.983. For the prostate dataset, MC-DDPM achieved MAE 59.953 HU, PSNR 26.920 dB, SSIM 0.849, and NCC 0.948. In conclusion, we have developed and validated a novel approach for generating CT images from routine MRIs using a transformer-based DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high-quality synthetic CT (sCT) images to be generated in minutes.

Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets for AI models.Approach. The proposed 2D image synthesis framework is based on a diffusion model using a Swin-transformer-based network. This model consists of a forward Gaussian noise process and a reverse process using the transformer-based diffusion model for denoising. Training data includes four image datasets: chest x-rays, heart MRI, pelvic CT, and abdomen CT. We evaluated the authenticity, quality, and diversity of the synthetic images using visual Turing assessments conducted by three medical physicists, and four quantitative evaluations: the Inception score (IS), Fréchet Inception Distance score (FID), feature similarity and diversity score (DS, indicating diversity similarity) between the synthetic and true images. To leverage the framework value for training AI models, we conducted COVID-19 classification tasks using real images, synthetic images, and mixtures of both images.Main results. Visual Turing assessments showed an average accuracy of 0.64 (accuracy converging to50%indicates a better realistic visual appearance of the synthetic images), sensitivity of 0.79, and specificity of 0.50. Average quantitative accuracy obtained from all datasets were IS = 2.28, FID = 37.27, FDS = 0.20, and DS = 0.86. For the COVID-19 classification task, the baseline network obtained an accuracy of 0.88 using a pure real dataset, 0.89 using a pure synthetic dataset, and 0.93 using a dataset mixed of real and synthetic data.Significance. A image synthesis framework was demonstrated for medical image synthesis, which can generate high-quality medical images of different imaging modalities with the purpose of supplementing existing training sets for AI model deployment. This method has potential applications in many data-driven medical imaging research.

Background: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan. Purpose: This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT domain for the image quality improvement of CBCT. Methods: The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time-embedded U-net architecture with residual and attention blocks to gradually transform standard Gaussian noise to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head-and-neck (H&N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model-based sCT generation methods. Conclusions: The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT-based organ segmentation and dose calculation for online ART.

This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each other to generate synthetic images from two different MRI pulse sequences. The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities. This improves image-to-image translation ac-curacy. We evaluated the CG-DDPM quantitatively using mean absolute error (MAE), multi-scale structural similarity index measure (MSSIM), and peak sig-nal-to-noise ratio (PSNR), as well as the network synthesis consistency, on the BraTS2020 dataset. Our proposed method showed high accuracy and reliable consistency for MRI synthesis. In addition, we compared the CG-DDPM with several other state-of-the-art networks and demonstrated statistically significant improvements in the image quality of synthetic MRIs. The proposed method enhances the capability of current multimodal MRI synthesis approaches, which could contribute to more accurate diagnosis and better treatment planning for patients by synthesizing additional MRI modalities.

Compared with commercial dual-energy computed tomography (DECT) solutions, beam modulation is an economical and efficient alternative to achieve single-scan DECT which can be enabled with conventional diagnostic or cone-beam CT machines. Existing beam modulation methods employ regularization strategy for DECT reconstruction, which cannot handle the half-fan mode due to inevitable data loss in each spectrum. Besides, all previous works employ primarymodulation scheme, leading to magnified penumbra area in the detector side. In this work, a dedicated beam modulator is attached to the detector surface to realize dual-energy projection acquisition during a single scan. Then the detectormodulated sinogram is fed into the pre-trained pix2pix GAN model to generate full-sampled sinograms at high- and lowenergy spectra. Analytical reconstruction and material decomposition can be subsequently implemented using the complete dual-energy sinogram data.