Faster Elbow MRI with Deep Learning Reconstruction-Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging

Judith Herrmann; Saif Afat; Sebastian Gassenmaier; Jan-Peter Grunz; Gregor Koerzdoerfer; Andreas Lingg; Haidara Almansour; Dominik Nickel; Theresa Sophie Patzer; Sebastian Werner

doi:10.3390/diagnostics13172747

Faster Elbow MRI with Deep Learning Reconstruction-Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging

Diagnostics (Basel). 2023 Aug 24;13(17):2747. doi: 10.3390/diagnostics13172747.

Affiliations

¹ Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany.
² Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, 97080 Würzburg, Germany.
³ MR Application Predevelopment, Siemens Healthcare GmbH, 91052 Erlangen, Germany.

Abstract

Objective: The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy.

Materials and methods: Between October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20-70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSE_STD), and prospectively undersampled TSE sequences reconstructed with a DL reconstruction (TSE_DL). Two radiologists evaluated the images concerning image quality, noise, edge sharpness, artifacts, diagnostic confidence, and delineation of anatomical structures using a 5-point Likert scale, and rated the images concerning the detection of common pathologies.

Results: Image quality was significantly improved in TSE_DL (mean 4.35, IQR 4-5) compared to TSE_STD (mean 3.76, IQR 3-4, p = 0.008). Moreover, TSE_DL showed decreased noise (mean 4.29, IQR 3.5-5) compared to TSE_STD (mean 3.35, IQR 3-4, p = 0.004). Ratings for delineation of anatomical structures, artifacts, edge sharpness, and diagnostic confidence did not differ significantly between TSE_DL and TSE_STD (p > 0.05). Inter-reader agreement was substantial to almost perfect (κ = 0.628-0.904). No difference was found concerning the detection of pathologies between the readers and between TSE_DL and TSE_STD. Using DL, the acquisition time could be reduced by more than 35% compared to TSE_STD.

Conclusion: TSE_DL provided improved image quality and decreased noise while receiving equal ratings for edge sharpness, artifacts, delineation of anatomical structures, diagnostic confidence, and detection of pathologies compared to TSE_STD. Providing more than a 35% reduction of acquisition time, TSE_DL may be clinically relevant for elbow imaging due to increased patient comfort and higher patient throughput.

Keywords: MRI; acceleration; deep learning reconstruction; elbow; image processing.

Grants and funding

This research received no external funding.