Image quality evaluation of neonatal brain magnetic resonance imaging using a deep learning reconstruction algorithm: A quantitative and multireader study using variable denoising levels at 3 tesla.

Objectives: Neonatal imaging is particularly challenging because newborns are prone to head motion, which can degrade image quality and complicate interpretation. Improving brain magnetic resonance imaging (MRI) image quality may help reduce diagnostic uncertainty and facilitate the nuanced assessment of early myelinating structures in the neonatal brain. Although deep learning reconstruction algorithms designed to improve MRI image quality have been evaluated in pediatric imaging, they have not been specifically studied in exclusively neonatal populations. This pilot study sought to evaluate image quality improvement through the employment of a deep learning reconstruction algorithm in neonatal brain imaging. Materials and Methods: 3D T1-weighted brain MRIs were obtained in a small cohort of 15 neonates. A deep learning reconstruction algorithm was applied to the image sets using low, medium, and high levels of denoising. Three radiologists qualitatively rated image quality (signal-to-noise ratio [SNR], presence of artifacts, and overall clarity) on a 4-point scale of eight early myelinating structures. Objective apparent SNR (aSNR) and apparent contrast-to-noise ratio (aCNR), based on signal intensities of white and gray matter, were measured across all three denoising levels. Results: Evaluation by radiologists indicated an overall increase in all image quality categories and increased conspicuity of the early myelinating structures as the level of denoising increased. Objective aSNR and aCNR values also increased progressively with denoising, with significant differences observed for nearly all pairwise comparisons. Conclusion: Our findings suggest improvement in image quality with the use of the deep learning reconstruction algorithm in 3D T1-weighted neonatal brain MRI, though the small sample size and pilot design limit generalizability. Diagnostic accuracy and clinical outcomes were not evaluated and warrant future investigation.