Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning.

PURPOSE: The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult to widely use. Therefore, we attempt to utilize high-quality images generated by uEXPLORER to optimize the quality of images from short-axis PET scanners through deep learning technology while controlling costs. METHODS: The experiments were conducted using PET images of three anatomical locations (brain, lung, and abdomen) from 335 patients. To simulate PET images from different axes, two protocols were used to obtain PET image pairs (each patient was scanned once). For low-quality PET (LQ-PET) images with a 320-mm AFOV, we applied a 300-mm FOV for brain reconstruction and a 500-mm FOV for lung and abdomen reconstruction. For high-quality PET (HQ-PET) images, we applied a 1940-mm AFOV during the reconstruction process. A 3D Unet was utilized to learn the mapping relationship between LQ-PET and HQ-PET images. In addition, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed to evaluate the model performance. Furthermore, two nuclear medicine doctors evaluated the image quality based on clinical readings. RESULTS: The generated PET images of the brain, lung, and abdomen were quantitatively and qualitatively compatible with the HQ-PET images. In particular, our method achieved PSNR values of 35.41 ± 5.45 dB (p < 0.05), 33.77 ± 6.18 dB (p < 0.05), and 38.58 ± 7.28 dB (p < 0.05) for the three beds. The overall mean SSIM was greater than 0.94 for all patients who underwent testing. Moreover, the total subjective quality levels of the generated PET images for three beds were 3.74 ± 0.74, 3.69 ± 0.81, and 3.42 ± 0.99 (the highest possible score was 5, and the minimum score was 1) from two experienced nuclear medicine experts. Additionally, we evaluated the distribution of quantitative standard uptake values (SUV) in the region of interest (ROI). Both the SUV distribution and the peaks of the profile show that our results are consistent with the HQ-PET images, proving the superiority of our approach. CONCLUSION: The findings demonstrate the potential of the proposed technique for improving the image quality of a PET scanner with a 320 mm or even shorter AFOV. Furthermore, this study explored the potential of utilizing uEXPLORER to achieve improved short-axis PET image quality at a limited economic cost, and computer-aided diagnosis systems that are related can help patients and radiologists.

Weakly supervised video-based cardiac detection for hypertensive cardiomyopathy.

INTRODUCTION: Parameters, such as left ventricular ejection fraction, peak strain dispersion, global longitudinal strain, etc. are influential and clinically interpretable for detection of cardiac disease, while manual detection requires laborious steps and expertise. In this study, we evaluated a video-based deep learning method that merely depends on echocardiographic videos from four apical chamber views of hypertensive cardiomyopathy detection. METHODS: One hundred eighty-five hypertensive cardiomyopathy (HTCM) patients and 112 healthy normal controls (N) were enrolled in this diagnostic study. We collected 297 de-identified subjects' echo videos for training and testing of an end-to-end video-based pipeline of snippet proposal, snippet feature extraction by a three-dimensional (3-D) convolutional neural network (CNN), a weakly-supervised temporally correlated feature ensemble, and a final classification module. The snippet proposal step requires a preliminarily trained end-systole and end-diastole timing detection model to produce snippets that begin at end-diastole, and involve contraction and dilatation for a complete cardiac cycle. A domain adversarial neural network was introduced to systematically address the appearance variability of echo videos in terms of noise, blur, transducer depth, contrast, etc. to improve the generalization of deep learning algorithms. In contrast to previous image-based cardiac disease detection architectures, video-based approaches integrate spatial and temporal information better with a more powerful 3D convolutional operator. RESULTS: Our proposed model achieved accuracy (ACC) of 92%, area under receiver operating characteristic (ROC) curve (AUC) of 0.90, sensitivity(SEN) of 97%, and specificity (SPE) of 84% with respect to subjects for hypertensive cardiomyopathy detection in the test data set, and outperformed the corresponding 3D CNN (vanilla I3D: ACC (0.90), AUC (0.89), SEN (0.94), and SPE (0.84)). On the whole, the video-based methods remarkably appeared superior to the image-based methods, while few evaluation metrics of image-based methods exhibited to be more compelling (sensitivity of 93% and negative predictive value of 100% for the image-based methods (ES/ED and random)). CONCLUSION: The results supported the possibility of using end-to-end video-based deep learning method for the automated diagnosis of hypertensive cardiomyopathy in the field of echocardiography to augment and assist clinicians. TRIAL REGISTRATION: Current Controlled Trials ChiCTR1900025325, Aug, 24, 2019. Retrospectively registered.

Improper handling of vomitus as a risk factor in the human norovirus outbreak in a kindergarten in Wuyi County, Zhejiang Province, China.

This study investigated an outbreak in a kindergarten in Wuyi County of acute gastroenteritis concerning a large number of students and teachers. We performed a case-control study, and collected information on the layout of the school, symptoms, onset time of all cases and vomiting sites. A total of 62 individuals fit the definition of probable cases; among these, there were 19 cases of laboratory-confirmed norovirus infection. Nausea and vomiting were the most common symptoms in the outbreak. Seven student norovirus patients vomited in the school. The odds ratio (OR) of norovirus illness was 15.75 times higher among teachers who handled or interacted with student vomitus without respiratory protection than compared to those without this type of exposure (OR 15.75, 95% CI 1.75-141.40). Nine samples were successfully genotyped; eight samples were norovirus GII.2[P16], one sample was norovirus GII.4 Sydney[P16]. This study revealed that improper handling of vomitus is a risk factor of norovirus infection. Therefore, more attention should be given to train school staff in knowledge of disinfection.

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images.

Objective.Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion.Approach.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices.Main results.To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods.Significance.The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.

Few-shot segmentation framework for lung nodules via an optimized active contour model.

BACKGROUND: Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge. PURPOSE: Existing deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings. METHODS: In this paper, we propose a few-shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high-order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours. RESULTS: We compared our proposed method with state-of-the-art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators. CONCLUSION: Our approach utilizes the output of few-shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples.

Analysis of a super-transmission of SARS-CoV-2 omicron variant BA.5.2 in the outdoor night market.

Introduction: The COVID-19 pandemic continues to ravage the world, and mutations of the SARS-CoV-2 continues. The new strain has become more transmissible. The role of aerosol transmission in the pandemic deserves great attention. Methods: In this observational study, we collected data from market customers and stallholders who had been exposed to the virus in the Qingkou night market on July 31 and were subsequently infected. We analyzed the possible infection zones of secondary cases and aerosol suspension time in ambient air. We described and analyzed the characteristics of the secondary cases and the transmission routes for customers. Results: The point source outbreak of COVID-19 in Qingkou night market contained a cluster of 131 secondary cases. In a less-enclosed place like the Qingkou night market, aerosols with BA.5.2 strain released by patients could suspend in ambient air up to 1 h 39 min and still be contagious. Conclusion: Aerosols with viruses can spread over a relatively long distance and stay in ambient air for a long time in a less enclosed space, but shorter than that under experimental conditions. Therefore, the aerosol suspension time must be considered when identifying and tracing close contact in outbreak investigations.

Corrigendum: Analysis of a super-transmission of SARS-CoV-2 omicron variant BA.5.2 in the outdoor night market.

[This corrects the article DOI: 10.3389/fpubh.2023.1153303.].

A cluster of two psittacosis cases among women farmers exposed to Chlamydia psittaci-infected domestic poultry in Zhejiang Province, China.

A cluster of Chlamydia psittaci (C. psittaci) cases was reported in Zhejiang Province, China, 2019. This study evaluates the extent of the outbreak and determines the source of infection. Real-time PCR and sequencing of the ompA gene of C. psittaci were performed to identify the cases, the domesticated poultry and close contacts. The index patient was a 76-year-old woman with chronic vertigo, and Case 2 was a 64-year-old female farmer with herpes zoster. Both women bought psittaci-infected chickens or ducks from the same mobile street vendor and raised them for 10 days and 23 days before fever onset. There were no direct contact between the two women. C. psittaci test was positive for the two patients, one sick chicken, three healthy ducks and the vendor's chicken cage. Phylogenetic analysis showed that all seven C. psittaci positive samples carried identical ompA genotype A of C. psittaci. Of all of the patients' 148 close contacts, none tested positive for C. psittaci, or developed acute respiratory symptoms. Both patients were discharged after a 4-week hospital stay. In conclusion, the source of this cluster was the poultry infected with C. psittaci, which occasionally cause infections in farmers, but inter-human transmission seems unlikely.

The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network.

BACKGROUND: The dose of radiation a patient receives when undergoing dual-energy computed tomography (CT) is of significant concern to the medical community, and balancing the tradeoffs between the level of radiation used and the quality of CT images is challenging. This paper proposes a method of synthesizing high-energy CT (HECT) images from low-energy CT (LECT) images using a neural network that achieves an alternative to HECT scanning by employing an LECT scan, which greatly reduces the radiation dose a patient receives. METHODS: In the training phase, the proposed structure cyclically generates HECT and LECT images to improve the accuracy of extracting edge and texture features. Specifically, we combine multiple connection methods with channel attention (CA) and pixel attention (PA) mechanisms to improve the network's mapping ability of image features. In the prediction phase, we use a model consisting of only the network component that synthesizes HECT images from LECT images. RESULTS: Our proposed method was conducted on clinical hip CT image data sets from Guizhou Provincial People's Hospital. In a comparison with other available methods [a generative adversarial network (GAN), a residual encoder-to-decoder network with a visual geometry group (VGG) pretrained model (RED-VGG), a Wasserstein GAN (WGAN), and CycleGAN] in terms of metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized mean square error (NMSE), and a visual effect evaluation, the proposed method was found to perform better on each of these evaluation criteria. Compared with the results produced by CycleGAN, the proposed method improved the PSNR by 2.44%, the SSIM by 1.71%, and the NMSE by 15.2%. Furthermore, the differences in the statistical indicators are statistically significant, proving the strength of the proposed method. CONCLUSIONS: The proposed method synthesizes high-energy CT images from low-energy CT images, which significantly reduces both the cost of treatment and the radiation dose received by patients. Based on both image quality score metrics and visual effects comparisons, the results of the proposed method are superior to those obtained by other methods.

A study of family clustering in two young girls with novel avian influenza A (H7N9) in Dongyang, Zhejiang Province, in 2014.

BACKGROUND: The avian influenza A H7N9 virus, previously unknown in humans, has infected humans in many areas of China since February 2013. Here we report on a clustering case of H7N9 in two little girls in one family in Dongyang city, Jinhua area, Zhejiang Province. OBJECTIVES: To determine (1) whether the infections were due to person-to-person transmission or to co-exposure to poultry and (2) the prevalence of this novel H7N9 virus in Dongyang inferred by this family clustering case. STUDY DESIGN: Samples were collected from patients and environment. We undertook detailed epidemiological investigations and laboratory work. Phylogenetic analyses were done based on the sequenced genomes. The concentration of cytokines and chemokines in the serum was detected by cytometric bead array analyses. RESULTS: A mixture of H7 and H9 was detected from the environmental sample. The three H7N9 viruses shared one infection source. The index patient who had significantly higher levels of IL-4, IL-8 and IL-10 suffered severe infection. CONCLUSIONS: Based on a comparison with previous isolations of the virus in 2013, H7N9 has evolved different lineages through recombination with local H9N2 viruses. Determining whether it was human-to-human transmission or exposure to the same live poultry, since both patients had identical exposure histories, was ambiguous. The results from the cytokine analyses agreed with the conclusion that H7N9 severity is associated with a higher level of cytokines/chemokines. Long term influenza surveillance remains essential to allow for early warning of potential transmission events.