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

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 he...

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Autores principales: Herrmann, Judith, Afat, Saif, Gassenmaier, Sebastian, Grunz, Jan-Peter, Koerzdoerfer, Gregor, Lingg, Andreas, Almansour, Haidara, Nickel, Dominik, Patzer, Theresa Sophie, Werner, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486923/
https://www.ncbi.nlm.nih.gov/pubmed/37685285
http://dx.doi.org/10.3390/diagnostics13172747
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author Herrmann, Judith
Afat, Saif
Gassenmaier, Sebastian
Grunz, Jan-Peter
Koerzdoerfer, Gregor
Lingg, Andreas
Almansour, Haidara
Nickel, Dominik
Patzer, Theresa Sophie
Werner, Sebastian
author_facet Herrmann, Judith
Afat, Saif
Gassenmaier, Sebastian
Grunz, Jan-Peter
Koerzdoerfer, Gregor
Lingg, Andreas
Almansour, Haidara
Nickel, Dominik
Patzer, Theresa Sophie
Werner, Sebastian
author_sort Herrmann, Judith
collection PubMed
description 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.
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spelling pubmed-104869232023-09-09 Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging Herrmann, Judith Afat, Saif Gassenmaier, Sebastian Grunz, Jan-Peter Koerzdoerfer, Gregor Lingg, Andreas Almansour, Haidara Nickel, Dominik Patzer, Theresa Sophie Werner, Sebastian Diagnostics (Basel) Article 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. MDPI 2023-08-24 /pmc/articles/PMC10486923/ /pubmed/37685285 http://dx.doi.org/10.3390/diagnostics13172747 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Herrmann, Judith
Afat, Saif
Gassenmaier, Sebastian
Grunz, Jan-Peter
Koerzdoerfer, Gregor
Lingg, Andreas
Almansour, Haidara
Nickel, Dominik
Patzer, Theresa Sophie
Werner, Sebastian
Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging
title Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging
title_full Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging
title_fullStr Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging
title_full_unstemmed Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging
title_short Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging
title_sort faster elbow mri with deep learning reconstruction—assessment of image quality, diagnostic confidence, and anatomy visualization compared to standard imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486923/
https://www.ncbi.nlm.nih.gov/pubmed/37685285
http://dx.doi.org/10.3390/diagnostics13172747
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