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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10486923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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