Discrepancies in the clinical and radiological profiles of COVID-19:A case-based discussion and review of literature

The current gold standard for the diagnosis of coronavirus disease-19(COVID-19)is a positive reverse transcriptase polymerase chain reaction(RT-PCR)test,on the background of clinical suspicion.However,RT-PCR has its limitations;this includes issues of low sensitivity,sampling errors and appropriate timing of specimen collection.As pulmonary involvement is the most common manifestation of severe COVID-19,early and appropriate lung imaging is important to aid diagnosis.However,gross discrepancies can occur between the clinical and imaging findings in patients with COVID-19,which can mislead clinicians in their decision making.Although chest X-ray(CXR)has a low sensitivity for the diagnosis of COVID-19 associated lung disease,especially in the earlier stages,a positive CXR increases the pre-test probability of COVID-19.CXR scoring systems have shown to be useful,such as the COVID-19 opacification rating score which helps to predict the need of tracheal intubation.Furthermore,artificial intelligence-based algorithms have also shown promise in differentiating COVID-19 pneumonia on CXR from other lung diseases.Although costlier than CXR,unenhanced computed tomographic(CT)chest scans have a higher sensitivity,but lesser specificity compared to RT-PCR for the diagnosis of COVID-19 pneumonia.A semi-quantitative CT scoring system has been shown to predict short-term mortality.The routine use of CT pulmonary angiography as a first-line imaging modality in patients with suspected COVID-19 is not justifiable due to the risk of contrast nephropathy.Scoring systems similar to those pioneered in CXR and CT can be used to effectively plan and manage hospital resources such as ventilators.Lung ultrasound is useful in the assessment of critically ill COVID-19 patients in the hands of an experienced operator.Moreover,it is a convenient tool to monitor disease progression,as it is cheap,non-invasive,easily accessible and easy to sterilise.Newer lung imaging modalities such as magnetic resonance imaging(MRI)for safe imaging among children,adolescents and pregnant women are rapidly evolving.Imaging modalities are also essential for evaluating the extra-pulmonary manifestations of COVID-19:these include cranial imaging with CT or MRI;cardiac imaging with ultrasonography(US),CT and MRI;and abdominal imaging with US or CT.This review critically analyses the utility of each imaging modality to empower clinicians to use them appropriately in the management of patients with COVID-19 infection.

PULMONARY BLOOD DISTRIBUTION AFTER TOTAL CAVOPULMONARY CONNECTION OF DIFFERENT TYPES

Objective.To assess the feature of pulmonary blood flow distribution after total cavopulmonary connection(TCPC)of different types,and to provide the selection of the best type.Methods. Thirty-two consecutive survival patients after TCPC underwent radionuclide lung perfusion imaging. According to the radionuclide counts in the left and right lungs,analyses of the distribution of blood flow from superior venous cava(SVC) and inferior venous cava(IVC)and the whole pulmonary blood flow in both lungs were made. All patients were divided into 4 groups by the the anastomosis between IVC and pulmonary artery.Results. GroupⅠ:The flow ratio of the IVC to left lung was greater than that to the right lung,P≤0.01;the flow ratio of the SVC to right lung was greater than that to the left lung,P≤0.01;and the whole pulmonary blood flow went dominantly to the left lung,P≤0.05,which is not in line with physiological distribution. GroupⅡ:the flows from the SVC and IVC were mixed in the middle of the junction and ran evenly into the right and left lungs,the whole pulmonary blood flow went to both lungs,P≥0.05. Group Ⅲ:the flow ratio of the SVC to both lungs were the same,P≥0.05,and major part from IVC went to the right lung,P≤0.01;the pulmonary blood flow go dominantly to the right lung,P≤0.05,which is in accord with physiological distribution. Group Ⅳ:the flows from the right SVC went to right lung by 100%,P≤0.01,and that from the left SVC went to left lung by 100% too,P≤0.01;the flows from IVC went dominantly to the left lung,with little part to the right lung ,P≤0.05.Conclusions. Different types of TCPC can result in different pulmonary blood distributions. The best flow distribution between the left and right lungs can be obtained by an offset of the IVC anastomosis toward the RPA with widening anastomosis for the patients without persist left superior venous cava(PLSVC).

The Study of Image Segmentation Based on the Combination of the Wavelet Multi-scale Edge Detection and the Entropy Iterative Threshold Selection

This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold selection.Image for segmentation is divided into two parts by high- and low-frequency.In the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection method.Through the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the lungs.Experimental results show that the method is efficient to segment the interesting region of an image compared with conventional methods.

Preparation of ^(68)Ga-DOTA-cRGD and its utility for lung adenocarcinoma bearing nude mice imaging

Objective To synthesize68Ga-DOTA-cyclic RDG（cRGD）,to evaluate its biodistribution in nomal mice,and to perform68Ga-DOTA-cRGD micro PET imaging on nude mice bearing lung adenocarcinoma（LA）xenografts.Methods68Ga-DOTA-cRGD was synthesized in HEPES buffer system（p H 5.0）.The radiolabeled yield and radiochemistry purity were measured with HPLC.

Application of Medical Image Detection Technology Based on Deep Learning in Pneumoconiosis Diagnosis

Pneumoconiosis is a disease characterized by pulmonary tissue deposition caused by dust exposure in the workplace.In China,due to the large number and wide distribution of pneumoconiosis patients,there is a high demand for the case data of lung biopsy during the diagnosis of pneumoconiosis.This text studied the application of medical image detection technology in pneumoconiosis diagnosis based on deep learning(DL).A medical image detection and convolution neural network(CNN)based on DL was analyzed,and the application of DL medical image technology in pneumoconiosis diagnosis was researched.The experimental results in this paper showed that in the last round of testing,the accuracy of ResNet model including deconvolution structure reached 95.2%.The area under curve(AUC)value of the working characteristics of the subject is 0.987.The sensitivity was 99.66%,and the specificity was 88.61%.The non staging diagnosis of pneumoconiosis improved the diagnostic sensitivity while ensuring high specificity.At the same time,Delong test method was used to conduct AUC analysis on the three models,and the results showed that model C was more effective than model A and model B.There is no significant difference between model A and model B,and there is no significant difference in diagnostic efficiency.In a word,the diagnosis of the model has high sensitivity and low probability of missed diagnosis,which can greatly reduce the working pressure of diagnostic doctors and effectively improve the efficiency of diagnosis.