Carbon-Boosted and Nitrogen-Stabilized Isolated Single-Atom Sites for Direct Dehydrogenation of Lower Alkanes.

Lower olefins are widely used in the chemical industry as basic carbon-based feedstocks. Here, we report the catalytic system featuring isolated single-atom sites of iridium (Ir1) that can function within the entire temperature range of 300-600 °C and transform alkanes with conversions close to thermodynamics-dictated levels. The high turnover frequency values of the Ir1 system are comparable to those of homogeneous catalytic reactions. Experimental data and theoretical calculations both indicate that Ir1 is the primary catalytic site, while the coordinating C and N atoms help to enhance the activity and stability, respectively; all three kinds of elements cooperatively contribute to the high performance of this novel active site. We have further immobilized this catalyst on particulate Al2O3, and we found that the resulting composite system under mimicked industrial conditions could still give high catalytic performances; in addition, we have also developed and established a new scheme of periodical in situ regeneration specifically for this composite particulate catalyst.

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.

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.

PDV1 and PDV2 Differentially Affect Remodeling and Assembly of the Chloroplast DRP5B Ring.

Chloroplasts divide by binary fission, which is driven by a ring-like multiprotein complex spanning the inner and outer envelope membranes (OEMs) at the division site. The cytosolic DYNAMIN-RELATED PROTEIN 5B (DRP5B/ARC5) is a mechanochemical GTPase involved in binary fission of the chloroplast membrane in Arabidopsis (Arabidopsis thaliana), but the dynamics of its interactions with the chloroplast membranes and their regulation by guanine nucleotides and protein effectors remain poorly characterized. Using an Arabidopsis phot2 mutant with defects in chloroplast photorelocation movement, we determined that the ring structures of DRP5B at the chloroplast division site underwent subunit exchange with a cytosolic DRP5B pool. Mutant DRP5B proteins with impaired GTPase activity retained the ability to self-assemble at the constriction sites of chloroplasts, but did not rescue the chloroplast division defects in the Arabidopsis drp5B mutant. Our in vivo kinetic measurements of the DRP5B mutant T82D suggested that turnover of the DRP5B ring at the chloroplast division site is coupled to GTP hydrolysis. Furthermore, we established that DRP5B targeting to the chloroplast surface and assembly into a ring structure at the division site are specifically determined by the chloroplast outer OEM protein PLASTID DIVISION2 (PDV2), and that DRP5B-OEM dissociation is mainly mediated by PDV1, a paralog of PDV2. Thus, this study suggests that the mechanochemical properties of DRP5B on the chloroplast surface are dynamically regulated by its GTPase activity and major binding partners.

MCD1 Associates with FtsZ Filaments via the Membrane-Tethering Protein ARC6 to Guide Chloroplast Division.

Chloroplasts replicate by binary fission, a process driven by ring-like dynamic division machinery at mid-chloroplast. In Arabidopsis thaliana, the first molecular assembly of this machinery, the Z-ring, forms via the association of FtsZ1 and FtsZ2 heteropolymers with the inner envelope membrane through the membrane-tethering protein ACCUMULATION AND REPLICATION OF CHLOROPLASTS6 (ARC6). Spatial control of Z-ring assembly ensures the correct placement of the division machinery and, therefore, symmetric chloroplast division. The plant-specific protein MULTIPLE CHLOROPLAST DIVISION SITE1 (MCD1) plays a role in Z-ring positioning and chloroplast division site placement, but its mechanism of action is unknown. Here, we provide evidence that MCD1 is a bitopic inner membrane protein whose C terminus faces the chloroplast stroma. Interaction analysis showed that MCD1 and ARC6 directly interact in the stroma and that MCD1 binds to FtsZ2 in an ARC6-dependent manner. These results are consistent with the in vivo observation that ARC6 influences the localization of MCD1 to membrane-tethered FtsZ filaments. Additionally, we found that MCD1 is required for the regulation of Z-ring positioning by ARC3 and MinE1, two components of the chloroplast Min (minicell) system, which negatively regulates Z-ring placement. Together, our findings indicate that MCD1 is part of the chloroplast Min system that recognizes membrane-tethered FtsZ filaments during chloroplast division-ring positioning.

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.

Clinical practice of Chinese medicine navel therapy for chronic diarrhea: A literature review.

Chronic diarrhea is one of the most common complaints in clinical practice for both adults and children. The purpose of this study was to assess the commonly used Chinese herbal medicine navel therapy for the treatment of chronic diarrhea (traditional Chinese medicine syndrome of spleen deficiency). The literature search was up to June 2018. Four types of studies (clinical trials, case series, case reports, and experts' experience) researched on Chinese medicine navel therapy used alone or combined with other therapies for the treatment of chronic diarrhea all included. Information on prescriptions, effectiveness, and safety of intervention was collected. Traditional Chinese Medicine Inheritance Support System V2.5 was used to do data analysis. We included 416 studies. All these studies carried out in 194 cities of China. We obtained the most commonly used single herbs, herbal medicine combination, and Chinese patent medicine for both adults and children. The top 3 single herbs were Caryophylli Flos (Dingxiang), Cinnamomi Cortex (Rougui), and Euodiae Fructus (Wuzhuyu). The most frequently used Chinese patent medicines were Ding Gui Infantile Navel Paste and Huoxiang Zhengqi Liquid. The effectiveness assessment was based on clinical trials, but we did not perform a meta-analysis because of different study design and unsatisfactory methodological quality. No serious adverse reaction happened in original studies. The application of Chinese medicine navel therapy could be one of the ideal treatments for chronic diarrhea in the future, especially for children. However, high-quality studies are very needed to provide clear evidence.

Low-Dose Computed Tomography Image Super-Resolution Reconstruction via Random Forests.

Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.

Positron emission tomography image reconstruction using feature extraction.

PURPOSE: To reduce the cost of positron emission tomography (PET) scanning systems, image reconstruction algorithms for low-sampled data have been extensively studied. However, the current method based on total variation (TV) minimization regularization nested in the maximum likelihood-expectation maximization (MLEM) algorithm cannot distinguish true structures from noise resulting losing some fine features in the images. Thus, this work aims to recover fine features lost in the MLEM-TV algorithm from low-sampled data. METHOD: A feature refinement (FR) approach previously developed for statistical interior computed tomography (CT) reconstruction is applied to PET imaging to recover fine features in this study. The proposed method starts with a constant initial image and the FR step is performed after each MLEM-TV iteration to extract the desired structural information lost during TV minimization. A feature descriptor is specifically designed to distinguish structure from noise and artifacts. A modified steepest descent method is adopted to minimize the objective function. After evaluating the impacts of different patch sizes on the outcome of the presented method, an optimal patch size of 7×7 is selected in this study to balance structure-detection ability and computational efficiency. RESULTS: Applying MLEM-TV-FR algorithm to the simulated brain PET imaging using an emission activity phantom, a standard Shepp-Logan phantom, and mouse results in the increased peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as comparing to using the conventional MLEM-TV algorithm, as well as the substantial reduction of the used sampling numbers, which improves the computational efficiency. CONCLUSIONS: The presented algorithm can achieve image quality superior to that of the MLEM and MLEM-TV approaches in terms of the preservation of fine structure and the suppression of undesired artifacts and noise, indicating its useful potential for low-sampled data in PET imaging.

Iterative image reconstruction for sparse-view CT via total variation regularization and dictionary learning.

Recently, low-dose computed tomography (CT) has become highly desirable due to the increasing attention paid to the potential risks of excessive radiation of the regular dose CT. However, ensuring image quality while reducing the radiation dose in the low-dose CT imaging is a major challenge. Compared to classical filtered back-projection (FBP) algorithms, statistical iterative reconstruction (SIR) methods for modeling measurement statistics and imaging geometry can significantly reduce the radiation dose, while maintaining the image quality in a variety of CT applications. To facilitate low-dose CT imaging, we in this study proposed an improved statistical iterative reconstruction scheme based on the penalized weighted least squares (PWLS) standard combined with total variation (TV) minimization and sparse dictionary learning (DL), which is named as a method of PWLS-TV-DL. To evaluate this PWLS-TV-DL method, we performed experiments on digital phantoms and physical phantoms, and analyzed the results in terms of image quality and calculation. The results show that the proposed method is better than the comparison methods, which indicates the potential of applying this PWLS-TV-DL method to reconstruct low-dose CT images.

Few-view CT image reconstruction using improved total variation regularization.

X-ray radiation is harmful to human health. Thus, obtaining a better reconstructed image with few projection view constraints is a major challenge in the computed tomography (CT) field to reduce radiation dose. In this study, we proposed and tested a new algorithm that combines penalized weighted least-squares using total generalized variation (PWLS-TGV) and dictionary learning (DL), named PWLS-TGV-DL to address this challenge. We first presented and tested this new algorithm and evaluated it through both data simulation and physical experiments. We then analyzed experimental data in terms of image qualitative and quantitative measures, such as the structural similarity index (SSIM) and the root mean square error (RMSE). The experiments and data analysis indicated that applying the new algorithm to CT data recovered images more efficiently and yielded better results than the traditional CT image reconstruction approaches.

Rapid labeling of amino acid neurotransmitters with a fluorescent thiol in the presence of o-phthalaldehyde.

LIF detection often requires labeling of analytes with fluorophores; and fast fluorescent derivatization is valuable for high-throughput analysis with flow-gated CE. Here, we report a fast fluorescein-labeling scheme for amino acid neurotransmitters, which were then rapidly separated and detected in flow-gated CE. This scheme was based on the reaction between primary amines and o-phthalaldehyde in the presence of a fluorescent thiol, 2-((5-fluoresceinyl)aminocarbonyl)ethyl mercaptan (FACE-SH). The short reaction time (<30 s) was suited for on-line mixing and derivatization that was directly coupled with flow-gated CE for rapid electrophoretic separation and sensitive LIF detection. To maintain the effective concentration of reactive FACE-SH, Tris(2-carboxyethyl)phosphine was added to the derivatization reagents to prevent thiol loss due to oxidation. This labeling scheme was applied to the detection of neurotransmitters by coupling in vitro microdialysis with online derivatization and flow-gated CE. It is also anticipated that this fluorophore tagging scheme would be valuable for on-chip labeling of proteins retained on support in SPE.

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.

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.

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.

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.

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.

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.