Synthesis of High-Resolution Research-Quality MRI Data from Clinical MRI Data in Patients with COVID-19

  1. Ryan J. Cali
  2. Holly J. Freeman
  3. Benjamin Billot
  4. Megan E. Barra
  5. David Fischer
  6. William R. Sanders
  7. Susie Y. Huang
  8. John Conklin
  9. Bruce Fischl
  10. Juan Eugenio Iglesias
  11. Brian L. Edlow

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Abstract

Pathophysiological mechanisms of neurological disorders in patients with coronavirus disease 2019 (COVID-19) are poorly understood, partly because of a lack of high-resolution neuroimaging data. We applied SynthSR, a convolutional neural network that synthesizes high-resolution isotropic research-quality data from thick-slice clinical MRI data, to a cohort of 11 patients with severe COVID-19. SynthSR successfully synthesized T1-weighted MPRAGE data at 1 mm spatial resolution for all 11 patients, each of whom had at least one brain lesion. Correlations between volumetric measures derived from synthesized and acquired MPRAGE data were strong for the cortical grey matter, subcortical grey matter, brainstem, hippocampus, and hemispheric white matter ( r =0.84 to 0.96, p≤0.001), but absent for the cerebellar white matter and corpus callosum ( r =0.04 to 0.17, p>0.61). SynthSR creates an opportunity to quantitatively study clinical MRI scans and elucidate the pathophysiology of neurological disorders in patients with COVID-19, including those with focal lesions.

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    SciScore for 10.1101/2021.11.25.21266090: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    BlindingThe rater was thus not blinded to the identity of a dataset as acquired or synthesized, but the rater was blinded to the lesion ratings performed by the neuroradiologists and the cortical volumetric data.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A trained data analyst with FreeSurfer expertise (H.J.F.) performed four visual QA ratings per dataset: left and right hemisphere surface and left and right subcortical segmentation.
    FreeSurfer
    suggested: (FreeSurfer, RRID:SCR_001847)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.11.25.21266090v1 on medRxiv