Browsing by Subject "harmonization"

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    Inter-site and inter-scanner diffusion MRI data harmonization
    (Elsevier, 2016-07) Mirzaalian, H.; Ning, L.; Savadjiev, P.; Pasternak, O.; Bouix, S.; Michailovich, O.; Grant, G.; Marx, C. E.; Morey, R. A.; Flashman, L. A.; George, M. S.; McAllister, Thomas W.; Andaluz, N.; Shutter, L.; Coimbra, R.; Zafonte, R. D.; Coleman, M. J.; Kubicki, M.; Westin, C. F.; Stein, M. B.; Shenton, M. E.; Rathi, Y.; Department of Psychiatry, IU School of Medicine
    We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.
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    Standardization of SARS-CoV-2 Cycle Threshold Values: Multisite Investigation Evaluating Viral Quantitation across Multiple Commercial COVID-19 Detection Platforms
    (ASM, 2023-01) Gavina, Kenneth; Franco, Lauren C.; Robinson, Christopher M.; Hymas, Weston; Lei, Guang-Sheng; Sinclair, Will; Hall, Tara; Carlquist, John; Lavik, John-Paul; Emery, Christopher L.; Heaton, Phillip R.; Hillyard, David; Lopransi, Bert K.; Relich, Ryan F.; Pathology and Laboratory Medicine, School of Medicine
    The demand for testing during the coronavirus disease 2019 (COVID-19) pandemic has resulted in the production of several different commercial platforms and laboratory-developed assays for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This has created several challenges, including, but not limited to, the standardization of diagnostic testing, utilization of cycle threshold (CT) values for quantitation and clinical interpretation, and data harmonization. Using reference standards consisting of a linear range of SARS-CoV-2 concentrations quantitated by viral culture-based methods and droplet digital PCR, we investigated the commutability and standardization of SARS-CoV-2 quantitation across different laboratories in the United States. We assessed SARS-CoV-2 CT values generated on multiple reverse transcription-PCR (RT-PCR) platforms and analyzed PCR efficiencies, linearity, gene targets, and CT value agreement. Our results demonstrate the inappropriateness of using SARS-CoV-2 CT values without established standards for viral quantitation. Further, we emphasize the importance of using reference standards and controls validated to independent assays, to compare results across different testing platforms and move toward better harmonization of COVID-19 quantitative test results.