Deep learning based assessment of diagnostic image quality from Free Induction Decay Navigators

Motivation: Motion-induced artifacts in pediatric MRI lead to frequent need for rescan, which increases examination time, costs and patient's discomfort. Goal(s): To develop and validate a deep learning-based method for automated assessment of diagnostic image quality, overcoming limitations of existing motion measurement techniques. Approach: FID navigators embedded into MPRAGE sequence can provide valuable motion information without prolonging scan time. We train a deep neural network on these signals to accurately predict the diagnostic quality of the image that is to be reconstructed. Results: Our method surpasses the existing FIDnavΔ approach, achieving AUC of 0.90, with 30% higher specificity and 21% improved precision. Impact: Our model streamlines MRI procedures by accurately predicting the need for rescans due to patient motion. It has potential to reduce healthcare costs and patient discomfort, and opens new avenues for early scan termination and enhanced clinical workflow efficiency.