Synthesizing pseudo-T2w images to recapture missing data in neonatal neuroimaging with applications in rs-fMRI

Sydney Kaplan, Anders Perrone, Dimitrios Alexopoulos, Jeanette K. Kenley, Deanna M. Barch, Claudia Buss, Jed T. Elison, Alice M. Graham, Jeffrey J. Neil, Thomas G. O'Connor, Jerod M. Rasmussen, Monica D. Rosenberg, Cynthia E. Rogers, Aristeidis Sotiras, Damien A. Fair, Christopher D. Smyser

Research output: Contribution to journalArticlepeer-review

Abstract

T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data.

Original languageEnglish (US)
Article number119091
JournalNeuroImage
Volume253
DOIs
StatePublished - Jun 2022

Bibliographical note

Funding Information:
We would like to thank the eLABE and ECHO working groups, D. Alexopoulos, D. Meyer, J. Dust, S. O'Hara, and A. Lee for data processing assistance, and J. Moran for helpful comments on the draft of the manuscript. This work was supported by the National Institutes of Health (Grant Nos. R01 MH113883 , P50 HD103525 , R01 MH096773 , UH3 OD023349 , R01 MH115357 , R01 MH125829 , R01 MH105538 ), the March of Dimes Prematurity Research Center at Washington University , the Bill and Melinda Gates Foundation (Grant Nos. OPP1184813 and INV-015711 ), and the Lynne and Andrew RedLeaf Foundation.

Publisher Copyright:
© 2022 The Authors

Keywords

  • Deep learning
  • Multi-atlas fusion
  • Neonate
  • Neuroimaging
  • Structural MRI
  • Synthetic medical images

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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