Accelerated brain magnetic resonance imaging with deep learning reconstruction: a comparative study on image quality in pediatric neuroimaging.

Abstract

Background: Magnetic resonance imaging (MRI) is crucial in pediatric radiology; however, the prolonged scan time is a major drawback that often requires sedation. Deep learning reconstruction (DLR) is a promising method for accelerating MRI acquisition.

Objective: To evaluate the clinical feasibility of accelerated brain MRI with DLR in pediatric neuroimaging, focusing on image quality compared to conventional MRI.

Materials and methods: In this retrospective study, 116 pediatric participants (mean age 7.9 ± 5.4 years) underwent routine brain MRI with three reconstruction methods: conventional MRI without DLR (C-MRI), conventional MRI with DLR (DLC-MRI), and accelerated MRI with DLR (DLA-MRI). Two pediatric radiologists independently assessed the overall image quality, sharpness, artifacts, noise, and lesion conspicuity. Quantitative image analysis included the measurement of image noise and coefficient of variation (CoV).

Results: DLA-MRI reduced the scan time by 43% compared with C-MRI. Compared with C-MRI, DLA-MRI demonstrated higher scores for overall image quality, noise, and artifacts, as well as similar or higher scores for lesion conspicuity, but similar or lower scores for sharpness. DLC-MRI demonstrated the highest scores for all the parameters. Despite variations in image quality and lesion conspicuity, the lesion detection rates were 100% across all three reconstructions. Quantitative analysis revealed lower noise and CoV for DLA-MRI than those for C-MRI. Interobserver agreement was substantial to almost perfect (weighted Cohen's kappa = 0.72-0.97).

Conclusion: DLR enabled faster MRI with improved image quality compared with conventional MRI, highlighting its potential to address prolonged MRI scan times in pediatric neuroimaging and optimize clinical workflows.