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Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network
Objectives: Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruct...
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/PMC10606422/ https://www.ncbi.nlm.nih.gov/pubmed/37892062 http://dx.doi.org/10.3390/diagnostics13203241 |
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author | Herrmann, Judith Afat, Saif Gassenmaier, Sebastian Koerzdoerfer, Gregor Lingg, Andreas Almansour, Haidara Nickel, Dominik Werner, Sebastian |
author_facet | Herrmann, Judith Afat, Saif Gassenmaier, Sebastian Koerzdoerfer, Gregor Lingg, Andreas Almansour, Haidara Nickel, Dominik Werner, Sebastian |
author_sort | Herrmann, Judith |
collection | PubMed |
description | Objectives: Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSE(DL)) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance. Material and Methods: In this prospective, monocentric study, TSE(DL) was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSE(S)). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSE(DL)). Two radiologists assessed the TSE(DL) and TSE(S) regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSE(DL) and TSE(S). Results: Compared with TSE(S), TSE(DL) was rated to be significantly superior in terms of image quality (p ≤ 0.020) with significantly reduced noise (p ≤ 0.001) and significantly improved edge sharpness (p = 0.003). No difference was found between TSE(S) and TSE(DL) concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures (p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved. Conclusion: TSE(DL) of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSE(S), reducing the acquisition time significantly. |
format | Online Article Text |
id | pubmed-10606422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106064222023-10-28 Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network Herrmann, Judith Afat, Saif Gassenmaier, Sebastian Koerzdoerfer, Gregor Lingg, Andreas Almansour, Haidara Nickel, Dominik Werner, Sebastian Diagnostics (Basel) Article Objectives: Hip MRI using standard multiplanar sequences requires long scan times. Accelerating MRI is accompanied by reduced image quality. This study aimed to compare standard two-dimensional (2D) turbo spin echo (TSE) sequences with accelerated 2D TSE sequences with deep learning (DL) reconstruction (TSE(DL)) for routine clinical hip MRI at 1.5 and 3 T in terms of feasibility, image quality, and diagnostic performance. Material and Methods: In this prospective, monocentric study, TSE(DL) was implemented clinically and evaluated in 14 prospectively enrolled patients undergoing a clinically indicated hip MRI at 1.5 and 3T between October 2020 and May 2021. Each patient underwent two examinations: For the first exam, we used standard sequences with generalized autocalibrating partial parallel acquisition reconstruction (TSE(S)). For the second exam, we implemented prospectively undersampled TSE sequences with DL reconstruction (TSE(DL)). Two radiologists assessed the TSE(DL) and TSE(S) regarding image quality, artifacts, noise, edge sharpness, diagnostic confidence, and delineation of anatomical structures using an ordinal five-point Likert scale (1 = non-diagnostic; 2 = poor; 3 = moderate; 4 = good; 5 = excellent). Both sequences were compared regarding the detection of common pathologies of the hip. Comparative analyses were conducted to assess the differences between TSE(DL) and TSE(S). Results: Compared with TSE(S), TSE(DL) was rated to be significantly superior in terms of image quality (p ≤ 0.020) with significantly reduced noise (p ≤ 0.001) and significantly improved edge sharpness (p = 0.003). No difference was found between TSE(S) and TSE(DL) concerning the extent of artifacts, diagnostic confidence, or the delineation of anatomical structures (p > 0.05). Example acquisition time reductions for the TSE sequences of 52% at 3 Tesla and 70% at 1.5 Tesla were achieved. Conclusion: TSE(DL) of the hip is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared with TSE(S), reducing the acquisition time significantly. MDPI 2023-10-18 /pmc/articles/PMC10606422/ /pubmed/37892062 http://dx.doi.org/10.3390/diagnostics13203241 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 Koerzdoerfer, Gregor Lingg, Andreas Almansour, Haidara Nickel, Dominik Werner, Sebastian Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network |
title | Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network |
title_full | Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network |
title_fullStr | Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network |
title_full_unstemmed | Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network |
title_short | Image Quality and Diagnostic Performance of Accelerated 2D Hip MRI with Deep Learning Reconstruction Based on a Deep Iterative Hierarchical Network |
title_sort | image quality and diagnostic performance of accelerated 2d hip mri with deep learning reconstruction based on a deep iterative hierarchical network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606422/ https://www.ncbi.nlm.nih.gov/pubmed/37892062 http://dx.doi.org/10.3390/diagnostics13203241 |
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