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 (TSES) and DL TSE sequences (TSEDL) 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
TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL 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.
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This post is Copyright: Arne Estler,
Leonie Zerweck,
Merle Brunnée,
Bent Estler,
Vivien Richter,
Anja Örgel,
Eva Bürkle,
Hannes Becker,
Helene Hurth,
Stéphane Stahl,
Eva‐Maria Konrad,
Carina Kelbsch,
Ulrike Ernemann,
Till‐Karsten Hauser,
Georg Gohla | March 11, 2024
Wiley: Journal of Neuroimaging: Table of Contents