Finding the minimum number of retrieved lymph nodes and negative lymph nodes in gastric cancer surgery: a real-world study.

BACKGROUND: Lymph node retrieval deficiency can lead to understagement and postoperative cancer recurrence, it is crucial to establish the standard number of retrieved lymph nodes (rLNs) and negative lymph nodes (nLNs) for patients undergoing gastrectomy. METHODS: Patients who has gastric adenocarcinoma and underwent either radical subtotal gastrectomy (RSG) or radical total gastrectomy (RTG) between 2000 and 2022 were retrospectively included. We utilized restricted cubic spline (RCS) analysis to determine the ideal threshold for rLNs and nLNs. Survival analysis was conducted using Kaplan-Meier (KM) curves, log-rank tests and forest plots. Propensity score matching (PSM) was utilized to balance parameters between two groups. The median follow-up time for this study was 3,095 days. RESULTS: Our study found that there are significant tumor characteristic differences between RSG and RTG. For patients with N0-N3a stage undergoing RSG, retrieving≥24 lymph nodes intraoperatively were associated with better prognosis both before and after PSM (OS: P<0.001, P=0.019); whereas for N3b stage, at least 32 rLNs were required (OS: P=0.006, P=0.023). Similarly, for patients with N0-N3a stage undergoing RTG, retrieving≥27 lymph nodes intraoperatively were associated with better prognosis both before and after PSM (OS: P<0.001, P=0.047); whereas for N3b stage, at least 34 rLNs were required (OS: P<0.001, P=0.003). Additionally, for patients undergoing RSG, having ≥21 nLNs (OS: P<0.001, P=0.013), and for those undergoing RTG, having ≥22 nLNs (OS: P<0.001, P<0.001), were also associated with better prognosis both before and after PSM. CONCLUSIONS: For patients receiving RSG, rLNs should reach 24 when lymph nodes are limited, and 32 when lymph node metastasis is more extensive, with a minimum number of nLNs ideally reaching 21. Similarly, for patients receiving RTG, rLNs should reach 27 when lymph nodes are limited, 34 when lymph node metastasis is more extensive, and a minimum number of nLNs ideally reaching 22.

Ideal Photodetector Based on WS2/CuInP2S6 Heterostructure by Combining Band Engineering and Ferroelectric Modulation.

Two-dimensional van der Waals (2D vdW) heterostructure photodetectors have garnered significant attention for their potential applications in next-generation optoelectronic systems. However, current 2D vdW photodetectors inevitably encounter compromises between responsivity, detectivity, and response time due to the absence of multilevel regulation for free and photoexcited carriers, thereby restricting their widespread applications. To address this challenge, we propose an efficient 2D WS2/CuInP2S6 vdW heterostructure photodetector by combining band engineering and ferroelectric modulation. In this device, the asymmetric conduction and valence band offsets effectively block the majority carriers (free electrons), while photoexcited holes are efficiently tunneled and rapidly collected by the bottom electrode. Additionally, the ferroelectric CuInP2S6 layer generates polarization states that reconfigure the built-in electric field, reducing dark current and facilitating the separation of photocarriers. Moreover, photoelectrons are trapped during long-distance lateral transport, resulting in a high photoconductivity gain. Consequently, the device achieves an impressive responsivity of 88 A W-1, an outstanding specific detectivity of 3.4 × 1013 Jones, and a fast response time of 37.6/371.3 µs. Moreover, the capability of high-resolution imaging under various wavelengths and fast optical communication has been successfully demonstrated using this device, highlighting its promising application prospects in future optoelectronic systems.

Reducing pediatric total-body PET/CT imaging scan time with multimodal artificial intelligence technology.

OBJECTIVES: This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. METHODS: A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. RESULTS: Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. CONCLUSION: Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts.

Deep Generalized Learning Model for PET Image Reconstruction.

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising. Incorporating DL into the traditional iterative optimization model can effectively improve its image quality and recover fine structures, but little research has considered the full relaxation of the model, resulting in the performance of this hybrid model not being sufficiently exploited. In this paper, we propose a learning framework that deeply integrates DL and an alternating direction of multipliers method (ADMM)-based iterative optimization model. The innovative feature of this method is that we break the inherent forms of the fidelity operators and use neural networks to process them. The regularization term is deeply generalized. The proposed method is evaluated on simulated data and real data. Both the qualitative and quantitative results show that our proposed neural network method can outperform partial operator expansion-based neural network methods, neural network denoising methods and traditional methods.

MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks.

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.

Performance Descriptors for Catalysts Based on Molybdenum, Tungsten, or Rhenium Oxides for Metathesis of Ethylene with 2-Butenes to Propene.

The metathesis of ethylene with 2-butenes to propene is an established large-scale process. However, the fundamentals behind in situ transformation of supported WOx , MoOx , or ReOx species into catalytically active metal-carbenes and the intrinsic activity of the latter as well as the role of metathesis-inactive cocatalysts are still unsolved. This is detrimental for catalyst development and process optimization. In this study, we provide the required essentials derived from steady-state isotopic transient kinetic analysis. For the first time, the steady-state concentration, the lifetime, and the intrinsic reactivity of metal carbenes were determined. The obtained results can be directly used for the design and the preparation of metathesis-active catalysts and cocatalysts, thereby opening up possibilities for optimizing propene productivity.

Geochemical characteristics and provenance of the detrital sediments in the junction area of Yinggehai and Qiongdongnan basins, South China Sea.

Together, the Yinggehai and Qiongdongnan basins have received a large amount of terrigenous sediments, but the provenance evolution of Cenozoic sediments in the two basins remains disputable. Combined with previous studies in the Yinggehai and Qiongdongnan basins, the elemental geochemistry of Oligocene to Pliocene sediment samples in the junction area of the two basins were analyzed to explore the tectonic implications, parent rock characteristics, and provenance evolution of the two basins during the Cenozoic. The results reveal that all the sediment samples were derived from continental island arc to passive continental margin settings. The light REE enrichment and stable content of heavy REE with large negative Eu anomalies indicate that they were probably derived from Hainan Island. The reconstructed provenance evolution model showed that the Red River Source (RRS) provided sedimentary materials for the Central Depression of Yinggehai Basin from the Oligocene to the Pliocene, and Hainan Island Source (HIS) was also one of the sources for sediments deposited in the Central Depression of Yinggehai Basin during the Miocene. However, most of the sediments preserved in the Yingdong Slope and Qiongdongnan Basin were derived from the HIS from the Oligocene to the Pliocene, and sediments deposited in the Yingdong Slope were also derived from the RRS during the Miocene. Furthermore, the junction area of the two basins had a mixed source of the RRS and HIS during the Cenozoic.

School-based Mental Health Interventions Targeting Depression or Anxiety: A Meta-analysis of Rigorous Randomized Controlled Trials for School-aged Children and Adolescents.

Past meta-analyses in mental health interventions failed to use stringent inclusion criteria and diverse moderators, therefore, there is a need to employ more rigorous methods to provide evidence-based and updated results on this topic. This study presents an updated meta-analysis of interventions targeting anxiety or depression using more stringent inclusion criteria (e.g., baseline equivalence, no significant differential attrition) and additional moderators (e.g., sample size and program duration) than previous reviews. This meta-analysis includes 29 studies of 32 programs and 22,420 students (52% female, 79% White). Among these studies, 22 include anxiety outcomes and 24 include depression outcomes. Overall, school-based mental health interventions in grades K-12 are effective at reducing depression and anxiety (ES = 0.24, p = 0.002). Moderator analysis shows that improved outcomes for studies with anxiety outcomes, cognitive behavioral therapy, interventions delivered by clinicians, and secondary school populations. Selection modeling reveals significant publication and outcome selection bias. This meta-analysis suggests school-based mental health programs should strive to adopt cognitive behavioral therapy and deliver through clinicians at the secondary school level where possible.

Paperfetcher: A tool to automate handsearching and citation searching for systematic reviews.

Systematic reviews are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic. In addition to database searching, handsearching is an important supplementary technique that helps increase the likelihood of identifying all relevant studies in a literature search. Traditional handsearching requires reviewers to manually browse through a curated list of field-specific journals and conference proceedings to find articles relevant to the review topic. This manual process is not only time-consuming, laborious, costly, and error-prone due to human fatigue, but it also lacks replicability due to its cumbersome manual nature. To address these issues, this paper presents a free and open-source Python package and an accompanying web-app, Paperfetcher, to automate the retrieval of article metadata for handsearching. With Paperfetcher's assistance, researchers can retrieve article metadata from designated journals within a specified time frame in just a few clicks. In addition to handsearching, it also incorporates a beta version of citation searching in both forward and backward directions. Paperfetcher has an easy-to-use interface, which allows researchers to download the metadata of retrieved studies as a list of DOIs or as an RIS file to facilitate seamless import into systematic review screening software. To the best of our knowledge, Paperfetcher is the first tool to automate handsearching with high usability and a multi-disciplinary focus.

Accurate total-body Ki parametric imaging with shortened dynamic 18 F-FDG PET scan durations via effective data processing.

BACKGROUND: Total-body dynamic positron emission tomography (dPET) imaging using 18 F-fluorodeoxyglucose (18 F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately 1 h seriously limits the application and promotion of this imaging technique. In this study, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 min post-injection (PI) with the help of a novel Patlak data processing algorithm for accurate Ki estimations of tumor lesions. METHODS: Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using 18 F-FDG of 15 patients with different tumor types were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired 1 h PI. Patlak analysis-based Ki parametric imaging was conducted based on the imaging data corresponding to the first 30 min PI, during which a Patlak data processing method based on cubic Hermite interpolation was applied. The resultant Ki images acquired by 30 min dynamic PET data and the standard 1 h Ki images were compared in terms of visual imaging effect, region signal-to-noise ratio, and Ki estimation accuracy to evaluate the performance of the proposed Ki imaging method with a shortened scan duration. RESULTS: With the help of Patlak data processing, acceptable Ki parametric images were obtained from dynamic PET data acquired with a scan duration of 30 min PI. Compared with Ki images obtained from unprocessed Patlak data, the resulting images from the proposed method performed better in terms of noise reduction. Moreover, Bland-Altman plot and Pearson correlation coefficient analysis showed that that 30 min Ki images obtained from the processed Patlak data had higher accuracy for tumor lesions. CONCLUSION: Satisfactory Ki parametric images with high tumor accuracy can be acquired from dynamic imaging data corresponding to the first 30 min PI. Patlak data processing can help achieve higher Ki imaging quality and higher accuracy regarding tumor lesion Ki values. Clinically, it is possible to shorten the dynamic scan duration of 18 F-FDG PET to 30 min to acquire an accurate tumor Ki and further effective tumor detection with uEXPLORER scanners.

Psychological Capital, Well-being, and Distress of International Students.

International students continue to experience myriad of challenges, some of which further transpired disproportionately during the COVID-19 pandemic era. To this effect, this study investigated psychological capital (PsyCap), psychological distress, and well-being among 188 international students attending U.S universities. Results using Hayes PROCESS indicated that well-being mediated the relationship between PsyCap and psychological distress and in particular moderated the relationship between PsyCap and depression. When higher education institutions are considering steps to mitigate psychological distress experienced by international students during the COVID-19 pandemic and beyond, based on the findings of our study, we suggest investing efforts and resources into two aspects: (a) promotion of positive mental health and well-being and (b) identification and development of positive psychological capital. We further discuss these results and implications for mental health promotion of international students in light of its limitations and recommendations for future research.

Exploring the Mechanism of Weikang Keli in Inhibiting Gastric Cancer through the MAPK Signaling Pathway: Based on Network Pharmacology and Experimental Verification.

Background: With a high incidence and limited treatments, gastric cancer (GC) seriously threatens human health worldwide. Weikang Keli (WK) is a compound prescription summed up from clinical experience. In our previous studies, WK has been proved to exert antitumor effects. However, there are no research studies to discuss and verify its mechanism as a compound. Objective: The aim of the study is to explore the potential molecular mechanism of WK in the treatment of GC with the aid of network pharmacology and verify it through experiments. Methods: Related databases were used to obtain genes and targets of WK and gastric cancer. A protein-protein interaction (PPI) network is constructed and visualized by Cytoscape 3.7.2. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyze core targets. The cell viability of MFC and BGC-823 cells was determined by CCK8. Immunofluorescence was used to determine autophagy of GC cells. Moreover, the effect of WK on the MAPK signaling pathway in GC cells and tumor tissues of ICR mice was detected by Western blot. Results: A total of 106 cross targets of WK and GC were obtained. According to the enrichment analysis of GO and KEGG, we target the MAPK signaling pathway to discuss the mechanism of WK on GC. Cell experiments proved that WK inhibited the viability of gastric cancer cells in a dose-dependent and time-dependent manner. Autophagosome aggregation and an increase in the expression of an autophagy marker protein LC3-II can also be observed in WK groups. Further animal experiments showed that the tumor inhibition rate of WK showed a dose-effect relationship. Moreover, the expressions of p-JNK, p-p38, and p-ERR1/2 proteins in the MAPK signaling pathway in WK Group were downregulated both in the cell and animal experiments, compared with the blank control group. Conclusion: WK showed an explicit antitumor effect on gastric cancer through the MAPK signaling pathway, and the curative effect varies in different concentrations. Besides, in model mice, the antitumor effect of high-dose WK group is close to that of platinum. This study provided a theoretical basis for the application of WK in the clinical treatment of gastric cancer.

International students' perceived discrimination and psychological distress during the COVID-19 pandemic.

OBJECTIVE: This study examined the relationship between perceived discrimination and psychological distress among international students during the COVID-19 pandemic. METHODS: A total of 188 international students from two large U.S. universities participated in the study. Perceived discrimination, psychological distress, and demographic information were assessed using self-reported questionnaires. RESULTS: COVID-related variables and perceived discrimination were significantly associated with international students' psychological distress. Their COVID-related anxiety mediated the relationship between perceived discrimination and psychological distress. CONCLUSION: Initiatives to mitigate the perceived discrimination experienced by international students may improve their mental health.

Self-efficacy Development of Graduate Student Leaders in Facilitating Groups for International Students.

This article describes how facilitating a mindfulness-based well-being group for international students (MBWIS) impacts self-efficacy development in group facilitation for graduate counseling students. Twelve students facilitated these eight-week structured psychoeducation support groups online and were supervised weekly by group counseling faculty. With a qualitative case study design, students participated in focus groups to discuss their experiences in the group facilitation. Two broad qualitative themes emerged from the findings including areas of increased self-efficacy and factors promoting self-efficacy. Implications for incorporating such multicultural group facilitation experiences as a teaching strategy when preparing students to conduct group work are discussed.

Technical Note: A preliminary study of dual-tracer PET image reconstruction guided by FDG and/or MR kernels.

PURPOSE: Clinically, single radiotracer positron emission tomography (PET) imaging is a commonly used examination method; however, since each radioactive tracer reflects the information of only one kind of cell, it easily causes false negatives or false positives in disease diagnosis. Therefore, reasonably combining two or more radiotracers is recommended to improve the accuracy of diagnosis and the sensitivity and specificity of the disease when conditions permit. METHODS: This paper proposes incorporating 18 F-fluorodeoxyglucose (FDG) as a higher-quality PET image to guide the reconstruction of other lower-count 11 C-methionine (MET) PET datasets to compensate for the lower image quality by a popular kernel algorithm. Specifically, the FDG prior is needed to extract kernel features, and these features were used to build a kernel matrix using a k-nearest-neighbor (kNN) search for MET image reconstruction. We created a 2-D brain phantom to validate the proposed method by simulating sinogram data containing Poisson random noise and quantitatively compared the performance of the proposed FDG-guided kernelized expectation maximization (KEM) method with the performance of Gaussian and non-local means (NLM) smoothed maximum likelihood expectation maximization (MLEM), MR-guided KEM, and multi-guided-S KEM algorithms. Mismatch experiments between FDG/MR and MET data were also carried out to investigate the outcomes of possible clinical situations. RESULTS: In the simulation study, the proposed method outperformed the other algorithms by at least 3.11% in the signal-to-noise ratio (SNR) and 0.68% in the contrast recovery coefficient (CRC), and it reduced the mean absolute error (MAE) by 8.07%. Regarding the tumor in the reconstructed image, the proposed method contained more pathological information. Furthermore, the proposed method was still superior to the MR-guided KEM method in the mismatch experiments. CONCLUSIONS: The proposed FDG-guided KEM algorithm can effectively utilize and compensate for the tissue metabolism information obtained from dual-tracer PET to maximize the advantages of PET imaging.

Synthesizing PET/MR (T1-weighted) images from non-attenuation-corrected PET images.

Positron emission tomography (PET) imaging can be used for early detection, diagnosis and postoperative patient monitoring of many diseases. Traditional PET imaging requires not only additional computed tomography (CT) imaging or magnetic resonance imaging (MR) to provide anatomical information but also attenuation correction (AC) map calculation based on CT images or MR images for accurate quantitative estimation. During a patient's treatment, PET/CT or PET/MR scans are inevitably repeated many times, leading to additional doses of ionizing radiation (CT scans) and additional economic and time costs (MR scans). To reduce adverse effects while obtaining high-quality PET/MR images in the course of a patient's treatment, especially in the stage of evaluating the effect of postoperative treatment, in this work, we propose a new method based on deep learning, which can directly obtain synthetic attenuation-corrected PET (sAC PET) and synthetic T1-weighted MR (sMR) images based only on non-attenuation-corrected PET (NAC PET) images. Our model, based on the Wasserstein generative adversarial network, first removes noise and artifacts from the NAC PET images to generate sAC PET images and then generates sMR images from the obtained sAC PET images. To evaluate the performance of this generative model, we evaluated it on paired PET/MR images from a total of eighty clinical patients. Based on qualitative and quantitative analysis, the generated sAC PET and sMR images showed a high degree of similarity to the real AC PET and real MR images. These results indicated that our proposed method can reduce the frequency of additional anatomical imaging scans during PET imaging and has great potential in improving doctors' clinical diagnosis efficiency, saving patients' economic expenditure and reducing the radiation risk brought by CT scanning.

LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks.

BACKGROUND: Reducing the radiation tracer dose and scanning time during positron emission tomography (PET) imaging can reduce the cost of the tracer, reduce motion artifacts, and increase the efficiency of the scanner. However, the reconstructed images to be noisy. It is very important to reconstruct high-quality images with low-count (LC) data. Therefore, we propose a deep learning method called LCPR-Net, which is used for directly reconstructing full-count (FC) PET images from corresponding LC sinogram data. METHODS: Based on the framework of a generative adversarial network (GAN), we enforce a cyclic consistency constraint on the least-squares loss to establish a nonlinear end-to-end mapping process from LC sinograms to FC images. In this process, we merge a convolutional neural network (CNN) and a residual network for feature extraction and image reconstruction. In addition, the domain transform (DT) operation sends a priori information to the cycle-consistent GAN (CycleGAN) network, avoiding the need for a large amount of computational resources to learn this transformation. RESULTS: The main advantages of this method are as follows. First, the network can use LC sinogram data as input to directly reconstruct an FC PET image. The reconstruction speed is faster than that provided by model-based iterative reconstruction. Second, reconstruction based on the CycleGAN framework improves the quality of the reconstructed image. CONCLUSIONS: Compared with other state-of-the-art methods, the quantitative and qualitative evaluation results show that the proposed method is accurate and effective for FC PET image reconstruction.

Low-dose CT reconstruction method based on prior information of normal-dose image.

BACKGROUND: Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, an advanced reconstruction algorithm is also needed. OBJECTIVE: To develop a novel algorithm used for sparse-view CT reconstruction associated with the prior image. METHODS: A low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) involving a transformed model for attenuation coefficients of the object to be reconstructed and prior information application in the forward-projection process was used to reconstruct CT images from sparse-view projection data. A digital extended cardiac-torso (XCAT) ventral phantom and a diagnostic head phantom were employed to evaluate the performance of the proposed PI-NDI method. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR) and mean percent absolute error (MPAE) of the reconstructed images were measured for quantitative evaluation of the proposed PI-NDI method. RESULTS: The reconstructed images with sparse-view projection data via the proposed PI-NDI method have higher quality by visual inspection than that via the compared methods. In terms of quantitative evaluations, the RMSE measured on the images reconstructed by the PI-NDI method with sparse projection data is comparable to that by MLEM-TV, PWLS-TV and PWLS-PICCS with fully sampled projection data. When the projection data are very sparse, images reconstructed by the PI-NDI method have higher PSNR values and lower MPAE values than those from the compared algorithms. CONCLUSIONS: This study presents a new low-dose CT reconstruction method based on prior information of normal-dose image (PI-NDI) for sparse-view CT image reconstruction. The experimental results validate that the new method has superior performance over other state-of-art methods.

Evaluation of reconstruction algorithms for a stationary digital breast tomosynthesis system using a carbon nanotube X-ray source array.

Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study.

Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging.

The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the second is a domain transformation operation, and the last is a convolutional neural network operating in the CT image domain. After training the network, we can obtain artifact-suppressed CT images directly from the sinogram domain. Verification results based on numerical, experimental and clinical data confirm that the proposed method can significantly reduce serious artifacts.