Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality

Background and purpose: This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging.

Methods: In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSE_S ) and DL TSE sequences (TSE_DL ) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert scale.

Results: TSE_DL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSE_S (p < .05). TSE_DL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated imaging.

Conclusion: The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.