Contrast Media & Molecular Imaging

The image enhancement for the natural images is the vast field where the quality of the images degrades based on the capturing and processing methods employed by the capturing devices. Based on noise type and estimation of noise, filter need to be adopted for enhancing the quality of the image. In the same manner, the medical field also needs some filtering mechanism to reduce the noise and detection of the disease based on the clarity of the image captured; in accordance with it, the preprocessing steps play a vital role to reduce the burden on the radiologist to make the decision on presence of disease. Based on the estimated noise and its type, the filters are selected to delete the unwanted signals from the image. Hence, identifying noise types and denoising play an important role in image analysis. The proposed framework addresses the noise estimation and filtering process to obtain the enhanced images. This paper estimates and detects the noise types, namely Gaussian, motion artifacts, Poisson, salt-andpepper, and speckle noises. Noise is estimated by using discrete wavelet transformation (DWT). This separates the image into quadruple sub-bands. Noise and HH sub-band are high-frequency components. HH sub-band also has vertical edges. These vertical edges are removed by performing Hadamard operation on downsampled Sobel edge-detected image and HH sub-band. Using HH sub-band after removing vertical edges is considered for estimating the noise. The Rician energy equation is used to estimate the noise. This is given as input for Artificial Neural Network to improve the estimated noise level. For identifying noise type, CNN is used. After removing vertical edges, the HH sub-band is given to the CNN model for classification. The classification accuracy results of identifying noise type are 100% on natural images and 96.3% on medical images.

This paper investigates the correlation between the degree and severity of CT inflammatory infiltration in the retroperitoneal space of acute pancreatitis (AP). A total of 113 patients were included based on diagnostic criteria. The general data of the patients and the relationship between the computed tomography severity index (CTSI) and pleural effusion (PE), involvement, degree of inflammatory infiltration of retroperitoneal space (RPS), number of peripancreatic effusion sites, and degree of pancreatic necrosis on contrast-enhanced CT at different times were studied. The results showed that the mean age of onset in females was later than that in males; 62 cases involved RPS to varying degrees, with a positive rate of 54.9% (62/113), and the total involvement rates of only the anterior pararenal space (APS); both APS and perirenal space (PS); and APS, PS, and posterior pararenal space (PPS) were 46.9% (53/113), 53.1% (60/113), and 17.7% (20/113), respectively. The degree of inflammatory infiltration in the RPS worsened with the increase in CTSI score; the incidence of PE was higher in the group greater than 48 hours than in the group less than 48 hours; necrosis >50% grade was predominant (43.2%) 5 to 6 days after onset, with a higher detection rate than other time periods ( < 0.05). Thus, when the PPS was involved, the patient’s condition can be treated as severe acute pancreatitis (SAP); the higher the degree of inflammatory infiltration in the retroperitoneum, the higher the severity of AP. Enhanced CT examination 5 to 6 days after onset in patients with AP revealed the greatest extent of pancreatic necrosis.

Objective. To investigate the toxic effects of microRNA-27a on breast cancer cells through inositol-acquiring enzyme 1-TNF receptor-associated factor 2 inhibition by mepivacaine. Methods. The elevation of miR-27a in MCF-7 of BCC lines was measured, and groups were set up as control, mepivacaine, and elevated groups. Cells from each group were examined for inflammatory progression. Results. Elevated miR-27a in MCF-7 cells was able to distinctly augment the cell advancement () and decline cell progression (). Meanwhile, miR-27a reduced the content of intracellular inflammatory factors IL-1β () and IL-6 (), elevated the content of IL-10 (), suppressed levels of cleaved-caspase-3 and p-signal transducer and activator of transcription-3 (STAT3) (), and increased Bcl-2/Bax (). Conclusion. Elevated miR-27a in MCF-7 of BCC lineage was effective in reducing the toxic effects of mepivacaine on cells and enhancing cell progression. This mechanism is thought to be related to the activation of the IRE1-TRAF2 signaling pathway in BCC. The findings may provide a theoretical basis for targeted treatment of BC in clinical practice.

Skin cancer is one of the most serious forms of the disease, and it can spread to other parts of the body if not detected early. Therefore, it is crucial to diagnose and treat skin cancer patients at an early stage. Due to the fact that a manual diagnosis of skin cancer is both time-consuming and expensive, an incorrect diagnosis is made due to the high degree of similarity between the various skin lesions. Improved categorization of multi-class skin lesions requires the development of automated diagnostic systems. We offer a fully automated method for classifying several skin lesions by fine-tuning the deep learning models, namely VGG16, ResNet50, and ResNet101. Prior to model creation, the training dataset should undergo data augmentation using traditional image transformation techniques and generative adversarial networks (GANs) to prevent class imbalance issues that may lead to model overfitting. In this study, we investigate the feasibility of creating dermoscopic images that have a realistic appearance using conditional generative adversarial network (CGAN) techniques. Afterward, the traditional augmentation methods are used to augment our existing training set to improve the performance of pretrained deep models on the skin lesion classification task. This improved performance is then compared to the models developed using the unbalanced dataset. In addition, we formed an ensemble of finely tuned transfer learning models, which we trained on balanced and unbalanced datasets. These models were used to make predictions about the data. With appropriate data augmentation, the proposed models attained an accuracy of 92% for VGG16, 92% for ResNet50, and 92.25% for ResNet101. The ensemble of these models increased the accuracy to 93.5%. There was a comprehensive discussion on the performance of the models. It is possible to conclude that using such a method leads to enhanced performance in skin lesion categorization compared to the efforts made in the past.

In order to explore the computerized tomography (CT) based on three-dimensional reconstruction of coronary artery model, the functional evaluation was made after percutaneous coronary intervention (PCI). In this study, 90 patients with coronary heart disease who received elective PCI were selected. The blood flow reserve fraction (FFR) and SYNTAX score were calculated by three-dimensional reconstruction of CT images, followed up for 2–4 years. According to the SYNTAX score, 0–22 points were defined as the low group (28 cases), 23–32 points as the medium group (33 cases), and 33 points as the high group (29 cases). In this paper, the accuracy, sensitivity, and specificity of CT images of three-dimensional reconstructed coronary artery model are 91%, 73%, and 62%, respectively. The follow-up results showed that the incidence of major adverse cerebrovascular events in the high group was significantly higher than that in the low group and the middle group, and the difference was statistically significant (). Pearson correlation analysis showed that SYNTAX score was related to serum total cholesterol (r = 0.234, ), triglyceride (r = 0.237, ), low-density lipoprotein cholesterol (r = 0.285, ), and ApoB/ApoA1 (R = 0.004). In this study, FFR is calculated by CT images based on three-dimensional reconstruction of coronary artery model, which can provide support for the diagnosis and treatment of coronary heart disease. SYNTAX score can be used as a risk predictor for PCI patients with coronary heart disease.

To study the application of intraoperative EMG intelligent monitoring in orthopedic surgery under general anesthesia, a total of 53 patients who underwent orthopedic surgery from February 2021 to February 2022 were selected. The combined monitoring of somatosensory evoked potential (SEP), motor evoked potential (MEP), and electromyography (EMG) was used to analyze the monitoring efficiency. In 38 of the 53 patients, the intraoperative signal was normal, and there was no postoperative neurological dysfunction; one case had abnormal signal, and the abnormality still existed after debugging, but no obvious neurological dysfunction was found after operation; the remaining 14 cases had abnormal signal. There were 13 early warnings in SEP monitoring; 12 early warnings in MEP monitoring; 10 early warnings in EMG monitoring. There were 15 cases of early warning in the joint monitoring of the three, and the sensitivity of the combined monitoring of SEP + MEP + EMG was significantly higher than that of the single monitoring of SEP, MEP, and EMG . There was no significant difference in specificity, positive predictive value, and negative predictive value between combined monitoring and single monitoring . The combined monitoring of EMG, MEP, and SEP in orthopedic surgery can significantly improve the safety of surgery, its sensitivity and negative predictive value were significantly higher than the monitoring effects of the two alone.