Motion robust MR fingerprinting scan to image neonates with prenatal opioid exposure

ArXiv [Preprint]. 2023 Jun 29:arXiv:2306.16656v1.

Abstract

Background: A noninvasive and sensitive imaging tool is needed to assess the fast-evolving baby brain. However, using MRI to study non-sedated babies faces roadblocks, including high scan failure rates due to subjects motion and the lack of quantitative measures for assessing potential developmental delays. This feasibility study explores whether MR Fingerprinting scans can provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure, presenting a viable alternative to clinical MR scans.

Assessment: MRF image quality was compared to pediatric MRI scans using a fully crossed, multiple reader multiple case study. The quantitative T1 and T2 values were used to assess brain tissue changes between babies younger than one month and babies between one and two months.

Statistical tests: Generalized estimating equations (GEE) model was performed to test the significant difference of the T1 and T2 values from eight white matter regions of babies under one month and those are older. MRI and MRF image quality were assessed using Gwets second order auto-correlation coefficient (AC2) with its confidence levels. We used the Cochran-Mantel-Haenszel test to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features.

Results: In infants under one month of age, the T1 and T2 values are significantly higher (p<0.005) compared to those between one and two months. A multiple-reader and multiple-case study showed superior image quality ratings in anatomical features from the MRF images than the MRI images.

Conclusions: This study suggested that the MR Fingerprinting scans offer a motion-robust and efficient method for non-sedated infants, delivering superior image quality than clinical MRI scans and additionally providing quantitative measures to assess brain development.

Publication types

  • Preprint