ABSTRACT:The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state-of-the-art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 3 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method.

BackgroundNovel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (or coronavirus disease 2019; has caused a large number of infections across the globe. Numerous markers are being used to predict the severity of infection. This study was undertaken to assess the utility of platelet count, mean platelet volume (MPV), platelet distribution width (PDW), and platelet lymphocyte ratio (PLR) as markers of severity and mortality among patients with COVID-19 infection. MethodologyThis is a retrospective study conducted in a tertiary care center in India from April 2021 to June 2021. Patients admitted with COVID-19 infection were included in the study. Based on the severity, patients were categorized into the mild and severe (moderate severity included) groups. Platelet count, MPV, PDW, and PLR done at admission were studied and correlated with the disease severity and mortality. StatisticsThe independent t-test was used to compare the variables. The receiver operating characteristic (ROC) curve was done to identify the cut-off value. Statistical analysis was performed using SPSS 18 software (SPSS Inc. Released 2009. PASW Statistics for Windows, Version 18.0. Chicago: SPSS Inc). ResultsOne hundred patients admitted with COVID-19 infection were studied. 51 patients had a mild and 49 had a severe infection. The mean PLR was 141.40 among patients with mild illness and 252.6 with severe infection (P<0.001). The mean PLR among survivors was 104.4 (SD-23.56) and among nonsurvivors was 302.78 (SD-34.5) (P<0.001). There was no statistically significant difference between the two groups with respect to platelet count, MPV, and PDW. ConclusionPLR was found to be a reliable marker of severity and mortality among patients with COVID-19 illness.

Background: Coronavirus disease 2019 (COVID-19) disproportionately affects individuals with various comorbidities. Among these, chronic kidney disease (CKD) has been shown to be strongly associated with the progression to severe disease. This study aimed to assess the severity and disease outcomes in patients with COVID-19 infection and CKD. Methods: This is a retrospective study conducted at a tertiary care hospital from July 2021 to September 2021. The case records of patients with CKD and COVID-19 were studied. They were compared with age and gender-matched controls equally. The presenting symptoms, clinical course, severity of illness, laboratory markers, need for ventilator support, and mortality outcomes were studied. Results: In total, 40 CKD and 40 non-CKD patients with COVID-19 were included in the study. It was also observed that among the patients with CKD, more patients had fever, breathlessness, and diarrhea. The requirement for noninvasive ventilation, ventilator, and inotropes was on the higher average for patients with CKD. Overall mortality was 27.5% in the CKD group and 2.5% in the non-CKD group, which was statistically significant (p = 0.002). Conclusions: COVID-19 patients with CKD had more severe illnesses with a requirement of ventilator support and had higher mortality than the patients without CKD. Patients with CKD are a key subset of patients with COVID-19 for whom more aggressive early treatment and stricter preventive measures may be beneficial.