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

Abstract

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.