Fast 5-minute shoulder MRI protocol with accelerated TSE-sequences and deep learning image reconstruction for the assessment of shoulder pain at 1.5 and 3 Tesla

Judith Herrmann; You-Shan Feng; Sebastian Gassenmaier; Jan-Peter Grunz; Gregor Koerzdoerfer; Andreas Lingg; Haidara Almansour; Dominik Nickel; Ahmed E Othman; Saif Afat

doi:10.1016/j.ejro.2024.100557

Fast 5-minute shoulder MRI protocol with accelerated TSE-sequences and deep learning image reconstruction for the assessment of shoulder pain at 1.5 and 3 Tesla

Eur J Radiol Open. 2024 Mar 8:12:100557. doi: 10.1016/j.ejro.2024.100557. eCollection 2024 Jun.

Authors

Affiliations

¹ Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany.
² Institute for Clinical Epidemiology and Applied Biometrics, University Hospital Tuebingen, Eberhard Karls University, Tuebingen, Germany.
³ Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
⁴ MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
⁵ Department of Neuroradiology, University Medical Center Mainz, Mainz, Germany.

Abstract

Purpose: The objective of this study was to implement a 5-minute MRI protocol for the shoulder in routine clinical practice consisting of accelerated 2D turbo spin echo (TSE) sequences with deep learning (DL) reconstruction at 1.5 and 3 Tesla, and to compare the image quality and diagnostic performance to that of a standard 2D TSE protocol.

Methods: Patients undergoing shoulder MRI between October 2020 and June 2021 were prospectively enrolled. Each patient underwent two MRI examinations: first a standard, fully sampled TSE (TSE_S) protocol reconstructed with a standard reconstruction followed by a second fast, prospectively undersampled TSE protocol with a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSE_DL). Image quality and visualization of anatomic structures as well as diagnostic performance with respect to shoulder lesions were assessed using a 5-point Likert-scale (5 = best). Interchangeability analysis, Wilcoxon signed-rank test and kappa statistics were performed to compare the two protocols.

Results: A total of 30 participants was included (mean age 50±15 years; 15 men). Overall image quality was evaluated to be superior in TSE_DL versus TSE_S (p<0.001). Noise and edge sharpness were evaluated to be significantly superior in TSE_DL versus TSE_S (noise: p<0.001, edge sharpness: p<0.05). No difference was found concerning qualitative diagnostic confidence, assessability of anatomical structures (p>0.05), and quantitative diagnostic performance for shoulder lesions when comparing the two sequences.

Conclusions: A fast 5-minute TSE_DL MRI protocol of the shoulder is feasible in routine clinical practice at 1.5 and 3 T, with interchangeable results concerning the diagnostic performance, allowing a reduction in scan time of more than 50% compared to the standard TSE_S protocol.

Keywords: Acceleration; Deep learning reconstruction; Diagnostic imaging; Image processing; Magnetic resonance imaging.