Objective To propose a novel method based on three-dimensional depthwise separable convolution network(3D DSN)for the separation of PET images with dual tracers of 18F-FDG and 18F-FAPI.Methods A total of 120 pairs of 18F-FDG and 18F-FAPI PET images of the same patient scanned separately at different time points were collected,and the dual-tracer PET image was generated through simulation.After the image registration of PET images of two tracers for ensuring spatial position matching,the registered PET images were forward-projected to generate sinogram data,and the sinogram data of two tracers were accumulated to obtain mixed sinogram data.Subsequently,the dual-tracer PET image was reconstructed using maximum likelihood expectation maximization and input into a 3D DSN based network for image separation,thereby obtaining PET images of two single tracers.Results Compared with 3D CNN method,the proposed method increased the structure similarity index measure(SSIM)of the separated 18F-FDG images to the real 18F-FDG images by 0.87%,increased the peak signal-to-noise ratio(PSNR)by 11.8%,and reduced the normalized root mean square error(NRMSE)by 52%.The SSIM of the separated 18F-FAPI images to the real 18F-FAPI images increased by 1.1%,PSNR increased by 17.0%,and NRMSE decreased by 51%.Conclusion The proposed method can be effectively applied to simultaneous PET imaging with dual PET tracers,reducing the number of scans and costs in time and money,and providing clinical doctors more accurate and abundant diagnostic information.

Objective To propose a novel method based on three-dimensional depthwise separable convolution network(3D DSN)for the separation of PET images with dual tracers of 18F-FDG and 18F-FAPI.Methods A total of 120 pairs of 18F-FDG and 18F-FAPI PET images of the same patient scanned separately at different time points were collected,and the dual-tracer PET image was generated through simulation.After the image registration of PET images of two tracers for ensuring spatial position matching,the registered PET images were forward-projected to generate sinogram data,and the sinogram data of two tracers were accumulated to obtain mixed sinogram data.Subsequently,the dual-tracer PET image was reconstructed using maximum likelihood expectation maximization and input into a 3D DSN based network for image separation,thereby obtaining PET images of two single tracers.Results Compared with 3D CNN method,the proposed method increased the structure similarity index measure(SSIM)of the separated 18F-FDG images to the real 18F-FDG images by 0.87%,increased the peak signal-to-noise ratio(PSNR)by 11.8%,and reduced the normalized root mean square error(NRMSE)by 52%.The SSIM of the separated 18F-FAPI images to the real 18F-FAPI images increased by 1.1%,PSNR increased by 17.0%,and NRMSE decreased by 51%.Conclusion The proposed method can be effectively applied to simultaneous PET imaging with dual PET tracers,reducing the number of scans and costs in time and money,and providing clinical doctors more accurate and abundant diagnostic information.

Objective To summarize the best evidence for perioperative exercise in renal transplant patients and provide evidence-based evidence for clinical staff to perform exercise in renal transplant patients.Methods Evidence-based questions were identified according to the PIPOST model,and computerized searches were conducted on BMJ Best Practice,UpToDate,the World Health Organization,the US National Guidelines,the website of the Chinese Society of Nephrology,the website of the British Renal Association,the website of the American Society of Nephrology,CNKI,the knowledge service platform of Wanfang Data,the Wipo database and other databases for all evidence on perioperative exercise in renal transplant recipients,including clinical guidelines,clinical decision-making,systematic evaluations,evidence summaries,and expert consensus,with a timeframe for searching from the establishment of the database to August 2023.Quality assessment and evidence extraction of the literature were performed independently by 2 nursing graduate students.Results A total of 13 publications were included,including 1 clinical decision,1 guideline,3 expert consensus,and 8 systematic evaluations.A total of 24 pieces of best evidence were summarized in 7 areas:exercise benefits,exercise assessment,preoperative exercise strategies,postoperative exercise strategies,precautions,exercise instruction monitoring,and exercise support.Conclusions This study summarizes the best evidence about perioperative exercise exercise in renal transplantation and provides a reference for standardizing perioperative exercise exercise in renal transplantation.Clinical staff should fully identify the impediments and facilitators of exercise exercise in renal transplant patients,and develop personalized exercise exercise programs based on medical scenarios,patient conditions,and combined with patients'exercise wishes.

Anxiety is a strong behavioral and psychological reaction with fear components, while depression is a mental disorder dominated by high or low mood, both of which are accompanied by cognitive and behavioral changes, and are common comorbidities in patients with heart disease. Cardiac surgery is one of the important factors which trigger specific emotional and physiological reactions of patients. Persistent or initial depression and anxiety after surgery will not only increase surgical complications, short- or long-term mortality and medical costs, but also seriously affect patients' social function and quality of life. With the transformation of bio-psycho-social medical model, it is necessary to evaluate the perioperative psychological state and biological risk of patients undergoing cardiac surgery. This article reviews the characteristics, related mechanisms and therapeutic interventions of anxiety and depression in patients undergoing cardiac surgery.

Takayasu arteritis (TA) is a primary, chronic, non-specific, inflammatory disease of the aorta and its larger branches. The pulmonary artery trunk and its branches could be impacted by TA, which could cause stenosis or occlusion of lesion vessels. TA also affects the normal function of the aortic valve and other heart valves, mainly due to valvular insufficiency. Aortic regurgitation caused by TA is mainly treated by surgical operation. In this review, the examination technique, operation timing, operation method and prognosis of aortic valve involved in TA are discussed systematically.

Objective To evaluate the treatment effect of negative pressure wound therapy ( NPWT) followed by ultrasonic debridement surgical system ( UDSS) in patients with diabetic foot. Methods Forty-seven diabetic foot ulcer patients( WagnerⅡ~Ⅲ) were divided into two groups,of which 26 patients treated with UDSS combined NPWT,and 21 patients treated with traditional surgical debridement combined NPWT were control group. Cure rate, markedly effective rate and adverse reaction and complication were compared between two groups. Results Compared with control group,the experiment group had higher markedly effective rate in the first week(P<0. 05),and healing rates were higher in the 2th,4th,6th,12th weeks (P<0. 05). There were no differences in the adverse reactions and complications between the two groups. Conclusion UDSS combined with NPWT can accelerate wound cure,may play an important role for the treatment of diabetic foot ulcer.

OBJECTIVETo compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging.

METHODSHuber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators.

RESULTSThe experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal.

CONCLUSIONBoth of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.

Objective To compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging. Methods Huber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators. Results The experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal. Conclusion Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.

Objective We propose a method using total variation (TV) regularization in deconvolution for partial volume correction in PET imaging. In the degraded image model, we used TV regularization procedure in Van Cittert (VC) and Richardson-Lucy (RL) deconvolution algorithms. These methods were tested in simulated NCAT images and images of NEMA NU4-2008 IQ phantom and tumor-bearing mouse scanned by Simens Invoen microPET. The simulated experiment and tumor-bearing mouse experiment showed that the algorithms using TV regularization provided superior qualitative and quantitative appearance compared with traditional VC and RL algorithms. When the mean intensity of the tumor increased by (10 ± 1.8)%, the SD increase percentage was decreased from 49.98%to 14.26%and from 42.76%to 4.70%, suggesting the efficiency of the proposed algorithms for reducing PVEs in PET.

Objective To compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging. Methods Huber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators. Results The experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal. Conclusion Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.