A Multi Directional Perfect Reconstruction Filter Bank Designed with 2-D Eigenfilter Approach: Application to Ultrasound Speckle Reduction.

B-Mode ultrasound images are degraded by inherent noise called Speckle, which creates a considerable impact on image quality. This noise reduces the accuracy of image analysis and interpretation. Therefore, reduction of speckle noise is an essential task which improves the accuracy of the clinical diagnostics. In this paper, a Multi-directional perfect-reconstruction (PR) filter bank is proposed based on 2-D eigenfilter approach. The proposed method used for the design of two-dimensional (2-D) two-channel linear-phase FIR perfect-reconstruction filter bank. In this method, the fan shaped, diamond shaped and checkerboard shaped filters are designed. The quadratic measure of the error function between the passband and stopband of the filter has been used an objective function. First, the low-pass analysis filter is designed and then the PR condition has been expressed as a set of linear constraints on the corresponding synthesis low-pass filter. Subsequently, the corresponding synthesis filter is designed using the eigenfilter design method with linear constraints. The newly designed 2-D filters are used in translation invariant pyramidal directional filter bank (TIPDFB) for reduction of speckle noise in ultrasound images. The proposed 2-D filters give better symmetry, regularity and frequency selectivity of the filters in comparison to existing design methods. The proposed method is validated on synthetic and real ultrasound data which ensures improvement in the quality of ultrasound images and efficiently suppresses the speckle noise compared to existing methods.

Deep learning based correction of low performing pixel in computed tomography.

Low Performing Pixel (LPP)/bad pixel in CT detectors cause ring and streaks artifacts, structured non-uniformities and deterioration of the image quality. These artifacts make the image unusable for diagnostic purposes. A missing/defective detector pixel translates to a channel missing across all views in sinogram domain and its effect gets spill over entire image in reconstruction domain as artifacts. Most of the existing ring and streak removal algorithms perform correction only in the reconstructed image domain. In this work, we propose a supervised deep learning algorithm that operates in sinogram domain to remove distortions cause by the LPP. This method leverages CT scan geometry, including conjugate ray information to learn the interpolation in sinogram domain. While the experiments are designed to cover the entire detector space, we emphasize on LPPs near detector iso-center as these have most adverse impact on image quality specially if the LPPs fall on the high frequency region (bone-tissue interface). We demonstrated efficacy of the proposed method using data acquired on GE RevACT multi-slice CT system with flat-panel detector. Experimental results on head scans show significant reduction in ring artifacts regardless of LPP location in the detector geometry. We have simulated isolated LPPs accounting for 5% and 10% of total channels. Detailed statistical analysis illustrates approximately 5dB improvement in SNR in both sinogram and reconstruction domain as compared to classical bicubic and Lagrange interpolation methods. Also, with reduction in ring and streak artifacts, the perceptual image quality is improved across all the test images.

A prospective observational study to assess quality of life and prescription pattern in osteoarthritis patients at tertiary health centre in Mumbai.

BACKGROUND: Osteoarthritis (OA) is a major cause of disability and is focused in "Bone and Joint Decade" declared by WHO which substantially affect different dimensions of quality of life. The aim of present study was to find the disease pattern in OA patients, monitoring prescription pattern to assess prognosis of osteoarthritis by WOMAC index. MATERIALS AND METHODS: An observational study on prospective data collected for the evaluation of Quality of Life (QOL) in OA was conducted at tertiary health care centre in Mumbai. Patients with a diagnosis of OA were enrolled. The patient's history and clinical examination was based on classification criteria of the American College of Rheumatology; drugs prescribed were noted on case record form. Same procedure was carried out for the first and second follow-ups at 6 th and 12 th weeks respectively. RESULTS: The patients belong to primary OA (84%) as compared to secondary OA (16%). Females (70.56% and 10%) were affected more commonly than males (13.44% and 6%). Knee Joint was worst affected in 76%, followed by hip joint in 16% and shoulder, ankle, wrist, elbow joint each having 2% (n=1) involvement. NSAIDs continued to dominate prescriptions given to 84% of patients followed by antiarthritic drugs and calcium supplements in 54% cases. The WOMAC score was higher in most of patients. After medication hydroxy chloroquine sulfate has shown maximum reduction in average WOMAC sore followed by paracetamol, indomethacin and diclofenac sodium. CONCLUSION: Osteoarthritis has a significant impact on quality of life, only partly ameliorated by anti-arthritic drugs, as assessed by the WOMAC scale in this study population. Further, a study with larger sample size is needed to further support our findings.