Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality.
J Neuroimaging
; 34(2): 232-240, 2024.
Article
en En
| MEDLINE
| ID: mdl-38195858
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.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Órbita
/
Aprendizaje Profundo
Tipo de estudio:
Clinical_trials
/
Guideline
Límite:
Humans
Idioma:
En
Revista:
J Neuroimaging
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
NEUROLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania