Projection Domain Research Articles

Projection domain decomposition denoising algorithm based on low rank and similarity-based regularization.

Projection Domain Decomposition (PDD) is a dual energy reconstruction method which implements the decomposition process before image reconstruction. The advantage of PDD is that it can alleviate beam hardening artifacts and metal artifacts effectively as energy spectra estimation is considered in PDD. However, noise amplification occurs during the decomposition process, which significantly impacts the accuracy of effective atomic number and electron density. Therefore, effective noise reduction techniques are required in PDD. This study aims to develop a new algorithm capable of minimizing noise while simultaneously preserving edges and fine details. In this study, a denoising algorithm based on low rank and similarity-based regularization (LRSBR) is presented. This algorithm incorporates the low-rank characteristic of tensors into similarity-based regularization (SBR) framework. This method effectively addresses the issue of instability in edge pixels within the SBR algorithm and enhances the structural consistency of dual-energy images. A series of simulation and practical experiments were conducted on a dual-layer dual-energy CT system. Experiments demonstrate that the proposed method outperforms exiting noise removal methods in Peak Signal-to-noise Ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity (SSIM). Meanwhile, there has been a notable enhancement in the visual quality of CT images. The proposed algorithm has a significantly improved noise reduction compared to other competing approach in dual-energy CT. Meanwhile, the LRSBR method exhibits outstanding performance in preserving edges and fine structures, making it practical for PDD applications.

Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.

Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging.

The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization. To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction. A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49kV with 1.5mm aluminum filter and at 65kV and 0.257mm copper filter. A 10mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5mm iodine disk detection task at 49, 55, 60, and 65kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4mm. At 49kV, MTF errors were +5.1% and -5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min=3.7%; max=8.5%). At 65kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min=11.4%; max=16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2=0.993) and 19.14µGy/mA (R2=0.996) at 49 and 65kV, respectively. For iodine detection, a 0.25mm-thick copper filter at 65kV was found optimal, outperforming the default technique by 90%. The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.

The integration of construction planning and budget using Building Information Modelling (BIM): a systematic literature review

Although the current context of technological innovation and the applicability of the Building Information Modelling (BIM) methodology in Brazil and in several countries are widespread and in increasing evolution, errors/lack of information are noticed in the planning and budgeting structure of the construction industry daily during the design process, which often leads to delays in expected deadlines and, as a consequence, failure to meet expected costs during the construction process. Current tools show their relevant contribution regarding integrating information from the project domain by providing simulations aiming at increasingly assertive results. In this context, a systematic literature review was carried out through bibliometric analysis and addressed in this article to direct and guide the object of this study towards the development of a method for integrating construction planning and budgeting using BIM. The proposed method contributes to academia and industry in relevant issues, such as time management involving the generation/updating of schedules, construction time simulation, and cost management involving the generation/updating of quantitative surveys, cost estimates, and simulation of construction costs. The results obtained were highlighted and addressed. The research concluded that a small number of few documents were obtained, which can direct the study to fill in gaps, thus contributing to the development of new studies in the context of this article. During the development of the study, the authors highlight the contribution of the research to the proposed method, aiming for dissemination in the Academic area and application in Construction Companies. The proposed method is based on existing literature; however, due to the context of the topic, it inspires future investigations and continuous improvements.

Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework.

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP>DCNN-Weak>IR>DCNN-Medium>DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

CHICOT: A Developer-Assistance Toolkit for Code Search with High-Level Contextual Information

We propose a source code search system named CHICOT (Code search with HIgh level COnText) to assist developers in reusing existing code. While previous studies have examined code search on the basis of code-level, fine-grained specifications such as functionality, logic, or implementation, CHICOT addresses a unique mission: code search with high-level contextual information, such as the purpose or domain of a developer's project. It achieves this feature by first extracting the context information from codebases and then considering this context during the search. It provides a VSCode plugin for daily coding assistance, and the built-in crawler ensures up-to-date code suggestions. The case study attests to the utility of CHICOT in real-world scenarios.

Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images.

This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.

Proj2Proj: self-supervised low-dose CT reconstruction.

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method.

Swin-transformer-enhanced detector z-axis virtual alignment method for cone-beam CT system

ABSTRACT X-ray cone beam computed tomography (CBCT) is a common imaging tool in medical and industrial fields due to its non-invasive and efficient approach to 3D internal imaging. However, obtaining clear cross-section image with CBCT requires precise geometry alignment. Previous alignment methods have mostly relied on specially designed phantoms and/or the iterative optimisation of reconstructed images, both of which are costly and inefficient. This paper presented a Swin-Transformer-enhanced detector z-axis virtual geometry alignment method, which considered the tilt angle (around the central column of the detector) and the horizontal deviation of the centre of projection. The method took advantage of both cross-section and projection domains to virtually restore the geometric misalignments with the help of mathematical models. The maximum prediction error of the tilt angle was 0.0696°, and the single prediction time was about 0.0395 s. To the best of our knowledge, this work was the first study to detect the multiple geometric errors from the slice using deep learning method. It provided a new geometric alignment method for the CBCT system.

A low- dose CT reconstruction algorithm across different scanners based on federated feature learning

To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning (FedCT) to improve the generalization of deep learning models for multiple CT scanners and protect data privacy. In the proposed FedCT framework, each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning. A projection- domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain. Federated feature learning is introduced in the model, which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain. In the cross-client, multi-scanner, and multi-protocol low-dose CT reconstruction experiments, FedCT achieved the highest PSNR (+2.8048, +2.7301, and +2.7263 compared to the second best federated learning method), the highest SSIM (+0.0009, +0.0165, and +0.0131 in the same comparison), and the lowest RMSE (- 0.6687, - 1.5956, and - 0.9962). In the ablation experiment, compared with the general federated learning strategy, the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set. The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80. FedCT provides an effective solution for collaborative construction of CT reconstruction models, which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.

Development and tuning of models for accurate simulation of CT spatial resolution using CatSim.

Objective. We sought to systematically evaluate CatSim's ability to accurately simulate the spatial resolution produced by a typical 64-detector-row clinical CT scanner in the projection and image domains, over the range of clinically used x-ray techniques.Approach.Using a 64-detector-row clinical scanner, we scanned two phantoms designed to evaluate spatial resolution in the projection and image domains. These empirical scans were performed over the standard clinically used range of x-ray techniques (kV, and mA). We extracted projection data from the scanner, and we reconstructed images. For the CatSim simulations, we developed digital phantoms to represent the phantoms used in the empirical scans. We developed a new, realistic model for the x-ray source focal spot, and we empirically tuned a published model for the x-ray detector temporal response. We applied these phantoms and models to simulate scans equivalent to the empirical scans, and we reconstructed the simulated projections using the same methods used for the empirical scans. For the empirical and simulated scans, we qualitatively and quantitatively compared the projection-domain and image-domain point-spread functions (PSFs) as well as the image-domain modulation transfer functions. We reported four quantitative metrics and the percent error between the empirical and simulated results.Main Results.Qualitatively, the PSFs matched well in both the projection and image domains. Quantitatively, all four metrics generally agreed well, with most of the average errors substantially less than 5% for all x-ray techniques. Although the errors tended to increase with decreasing kV, we found that the CatSim simulations agreed with the empirical scans within limits required for the anticipated applications of CatSim.Significance.The new focal spot model and the new detector temporal response model are significant contributions to CatSim because they enabled achieving the desired level of agreement between empirical and simulated results. With these new models and this validation, CatSim users can be confident that the spatial resolution represented by simulations faithfully represents results that would be obtained by a real scanner, within reasonable, known limits. Furthermore, users of CatSim can vary parameters including but not limited to system geometry, focal spot size/shape and detector parameters, beyond the values available in physical scanners, and be confident in the results. Therefore, CatSim can be used to explore new hardware designs as well as new scanning and reconstruction methods, thus enabling acceleration of improved CT scan capabilities.

Dynamic-Projection-Integrated Particle-Filtering-Based Identification of Friction Characteristic Curve for Train Wheelset on Slipping Fault Condition

This paper proposes a dynamic-projection-integrated particle-filtering-based identification strategy for the friction characteristic curve of a train wheelset under the slipping fault condition. This strategy aims to achieve the identification of the fault friction characteristic curve (FFCC) in the early slipping fault stage. First, a multi-dimensional integrated particle-filtering (MDIPF)-based parameters correction method is proposed. The MDIPF constructs an error particle state transition model encompassing multi-dimensional parameters, which integrates inter-particle correlation to facilitate error fusion during the state transition process. Then, a dynamic projection domain (DPD)-based particle refinement method is proposed. The DPD constructed the contraction factors to dynamically fine-tune the particle projection domain. Finally, a multi-level evaluation-based identification method for the FFCC is proposed. And the dynamic-projection-integrated particle-filtering-based identification strategy is validated, which can actualize the rapid and accurate identification of the FFCC.

Identifying and Addressing Obstacles to PMO Success in Construction Projects: An AHP Approach

The development of construction projects serves as an indicator of societal progress, reflecting the growth of various project domains in Libya, especially in the Misrata Free Zone. Despite the significance of these projects, they encounter several issues during different stages, including scope changes, delivery delays, and cost overruns. The Project Management Office (PMO) plays a crucial role in ensuring the successful completion of projects, overcoming challenges, and minimizing delays and cost increases through planning, coordination, and monitoring, ensuring the project stays within its framework and objectives. This paper examines the primary obstacles to establishing a PMO in the Misrata Free Zone using the Analytical Hierarchy Process (AHP). The research findings are expected to lead to key strategies for overcoming these obstacles.

Metal Artifacts Reducing Method Based on Diffusion Model Using Intraoral Optical Scanning Data for Dental Cone-beam CT.

In dental cone-beam computed tomography (CBCT), metal implants can cause metal artifacts, affecting image quality and the final medical diagnosis. To reduce the impact of metal artifacts, our proposed metal artifacts reduction (MAR) method takes a novel approach by integrating CBCT data with intraoral optical scanning data, utilizing information from these two different modalities to correct metal artifacts in the projection domain using a guided-diffusion model. The intraoral optical scanning data provides a more accurate generation domain for the diffusion model. We have proposed a multi-channel generation method in the training and generation stage of the diffusion model, considering the physical mechanism of CBCT, to ensure the consistency of the diffusion model generation. In this paper, we present experimental results that convincingly demonstrate the feasibility and efficacy of our approach, which introduces intraoral optical scanning data into the analysis and processing of projection domain data using the diffusion model for the first time, and modifies the diffusion model to better adapt to the physical model of CBCT.

Порівняльний аналіз методів оцінювання соціально-економічної ефективності диджитал-інноваційних проєктів

Digital innovation projects occupy a leading place in the modern mechanism for stimulating economic development. The key in this context is the formulation of a system for evaluating the efficiency of these innovations. The aim of the article is to compare the key methods for evaluating the efficiency of digital innovation projects and to develop instruments to improve the efficiency of the results of these projects. This article proposes a model for assessing the socioeconomic efficiency of digital innovation projects using five key methods, namely: net present value; internal rate of return; profitability index; risk-based efficiency assessment method; social return on investment. Each of these assessment methods is described in the context of its strengths and weaknesses, as well as typical application situations. The article also examines the specifics of innovative projects, including their uncertainty, high risk, and the potential for a significant impact on society and the economy. The importance of introducing a system of key efficiency indicators, which allows you to quantify the success of innovative projects, for example, by measuring the time to implementation, the level of customer satisfaction and the efficiency of the use of resources, is emphasized. Recommendations for the State policy in the field of stimulating the development of innovations have been formed, which emphasizes the importance of financing scientific research and implementing insurance programs and supporting innovative projects. It is emphasized that such an approach can ensure sustainable economic development and a response to the social needs of modern society. The proposed article can be useful for researchers and analysts in the domain of digital innovation projects, business, the State and local authorities. Prospects for further research include the development of a digital dimension of the proposed key efficiency indicators using data from other benchmark countries for digital innovation activity.

Deep learning-based projection synthesis for low-dose cone-beam computed tomography imaging in image-guided radiotherapy.

The imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed. Included in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions. The phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10-4, 10.3×10-4, and 21.7×10-4, the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10-2, 18.9×10-2 and 33.9×10-2, for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains. The proposed SynCNN model effectively improves the quality of sparse-view CBCT images at a low time cost. With the SynCNN model, the CBCT imaging dose in IGRT could be reduced potentially.

One-Sample Diffusion Modeling in Projection Domain for Low-Dose CT Imaging

One-Sample Diffusion Modeling in Projection Domain for Low-Dose CT Imaging

Projection Domain Denoising Method for Multi-Energy Computed Tomography via Dual-Stream Transformer

Projection Domain Denoising Method for Multi-Energy Computed Tomography via Dual-Stream Transformer

Integrating Lean Thinking into Project Management: A Conceptual Model for the IT Sector

Lean Thinking (LT) is a philosophy that prioritizes customer value optimization and waste minimization. Although initially developed for manufacturing, LT principles and tools have been contributing to improve performance and competitiveness’s in several industries worldwide. However, the existing literature exploring the connection between LT and PM still fails when addressing both approaches, being primarily focused on construction projects, leaving other project contexts relatively unexplored. To address this research gap, this research aims to investigate the potential benefits of integrating LT into Information Technology (IT) PM practices across diverse project domains. Through a comprehensive literature review of relevant concepts, this research proposes a tailored conceptual model that links LT principles and tools with IT PM, targeting waste elimination and overall project efficiency enhancement. The model was developed based on the current literature and provides a valuable structure for improving efficiency, reducing waste, and enhancing project success, thereby enriching the literature and empowering practitioners in the field with a comprehensive approach to deliver high-quality results effectively.