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  1. Bilal M, Anis H, Khan N, Qureshi I, Shah J, Kadir KA
    Biomed Res Int, 2019;2019:6139785.
    PMID: 31119178 DOI: 10.1155/2019/6139785
    Background: Motion is a major source of blurring and ghosting in recovered MR images. It is more challenging in Dynamic Contrast Enhancement (DCE) MRI because motion effects and rapid intensity changes in contrast agent are difficult to distinguish from each other.

    Material and Methods: In this study, we have introduced a new technique to reduce the motion artifacts, based on data binning and low rank plus sparse (L+S) reconstruction method for DCE MRI. For Data binning, radial k-space data is acquired continuously using the golden-angle radial sampling pattern and grouped into various motion states or bins. The respiratory signal for binning is extracted directly from radially acquired k-space data. A compressed sensing- (CS-) based L+S matrix decomposition model is then used to reconstruct motion sorted DCE MR images. Undersampled free breathing 3D liver and abdominal DCE MR data sets are used to validate the proposed technique.

    Results: The performance of the technique is compared with conventional L+S decomposition qualitatively along with the image sharpness and structural similarity index. Recovered images are visually sharper and have better similarity with reference images.

    Conclusion: L+S decomposition provides improved MR images with data binning as preprocessing step in free breathing scenario. Data binning resolves the respiratory motion by dividing different respiratory positions in multiple bins. It also differentiates the respiratory motion and contrast agent (CA) variations. MR images recovered for each bin are better as compared to the method without data binning.

  2. Naqvi AA, Hassali MA, Iffat W, Zia M, Fatima M, Shakeel S, et al.
    Int J Rheum Dis, 2019 Nov;22(11):2031-2044.
    PMID: 31595667 DOI: 10.1111/1756-185X.13721
    AIM: Rheumatoid arthritis (RA) is a chronic progressive disabling disease that mainly affects joints. Studies documenting Pakistani patients' knowledge regarding RA disease are lacking and there is a need for such endeavor. The purpose of this study was to develop and validate a novel research tool to document patient knowledge about RA disease.

    METHODS: A novel research instrument known as the rheumatoid arthritis knowledge assessment scale (RAKAS) which consisted of 13 items, was formulated by a rheumatology panel and used for this study. This study was conducted in rheumatology clinics of three tertiary care hospitals in Karachi, Pakistan. The study was conducted in March-April 2018. Patients were recruited using a randomized computer-generated list of appointments. Sample size was calculated based on item-to-respondent ratio of 1:15. The validities, factor structure, sensitivity, reliability and internal consistency of RAKAS were assessed. The study was approved by the institutional Ethics Committee.

    RESULTS: A total of 263 patients responded to the study. Content validity was 0.93 and response rate was 89.6%. Factor analysis revealed a 3-factor structure. Fit indices, namely normed fit index (NFI), Tucker Lewis index (TLI), comparative fit index (CFI) and root mean square of error approximation (RMSEA) were calculated with satisfactory results, that is, NFI, TLI and CFI > 0.9, and RMSEA 19 and difficulty index <0.95. Sensitivity and specificity of RAKAS were above 90%. The tool established construct and known group validities.

    CONCLUSION: A novel tool to document disease knowledge in patients with RA was formulated and validated.

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