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AI-assisted accelerated MRI of the ankle: clinical practice assessment
BACKGROUND: High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been r...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587051/ https://www.ncbi.nlm.nih.gov/pubmed/37857868 http://dx.doi.org/10.1186/s41747-023-00374-5 |
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author | Zhao, Qiang Xu, Jiajia Yang, Yu Xin Yu, Dan Zhao, Yuqing Wang, Qizheng Yuan, Huishu |
author_facet | Zhao, Qiang Xu, Jiajia Yang, Yu Xin Yu, Dan Zhao, Yuqing Wang, Qizheng Yuan, Huishu |
author_sort | Zhao, Qiang |
collection | PubMed |
description | BACKGROUND: High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI) . METHODS: We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2–3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. RESULTS: ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32−43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). CONCLUSIONS: The optimum ACS acceleration factors for ankle MRI were found to be 3.2–3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. RELEVANCE STATEMENT: AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. KEY POINTS: • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-023-00374-5. |
format | Online Article Text |
id | pubmed-10587051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-105870512023-10-21 AI-assisted accelerated MRI of the ankle: clinical practice assessment Zhao, Qiang Xu, Jiajia Yang, Yu Xin Yu, Dan Zhao, Yuqing Wang, Qizheng Yuan, Huishu Eur Radiol Exp Original Article BACKGROUND: High-spatial resolution magnetic resonance imaging (MRI) is essential for imaging ankle joints. However, the clinical application of fast spin-echo sequences remains limited by their lengthy acquisition time. Artificial intelligence-assisted compressed sensing (ACS) technology has been recently introduced as an integrative acceleration solution. We compared ACS-accelerated 3-T ankle MRI to conventional methods of compressed sensing (CS) and parallel imaging (PI) . METHODS: We prospectively included 2 healthy volunteers and 105 patients with ankle pain. ACS acceleration factors for ankle protocol of T1-, T2-, and proton density (PD)-weighted sequences were optimized in a pilot study on healthy volunteers (acceleration factor 3.2–3.3×). Images of patients acquired using ACS and conventional acceleration methods were compared in terms of acquisition times, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic agreement. Shapiro-Wilk test, Cohen κ, intraclass correlation coefficient, and one-way ANOVA with post hoc tests (Tukey or Dunn) were used. RESULTS: ACS acceleration reduced the acquisition times of T1-, T2-, and PD-weighted sequences by 32−43%, compared with conventional CS and PI, while maintaining image quality (mostly higher SNR with p < 0.004 and higher CNR with p < 0.047). The diagnostic agreement between ACS and conventional sequences was rated excellent (κ = 1.00). CONCLUSIONS: The optimum ACS acceleration factors for ankle MRI were found to be 3.2–3.3× protocol. The ACS allows faster imaging, yielding similar image quality and diagnostic performance. RELEVANCE STATEMENT: AI-assisted compressed sensing significantly accelerates ankle MRI times while preserving image quality and diagnostic precision, potentially expediting patient diagnoses and improving clinical workflows. KEY POINTS: • AI-assisted compressed sensing (ACS) significantly reduced scan duration for ankle MRI. • Similar image quality achieved by ACS compared to conventional acceleration methods. • A high agreement by three acceleration methods in the diagnosis of ankle lesions was observed. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-023-00374-5. Springer Vienna 2023-10-20 /pmc/articles/PMC10587051/ /pubmed/37857868 http://dx.doi.org/10.1186/s41747-023-00374-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Zhao, Qiang Xu, Jiajia Yang, Yu Xin Yu, Dan Zhao, Yuqing Wang, Qizheng Yuan, Huishu AI-assisted accelerated MRI of the ankle: clinical practice assessment |
title | AI-assisted accelerated MRI of the ankle: clinical practice assessment |
title_full | AI-assisted accelerated MRI of the ankle: clinical practice assessment |
title_fullStr | AI-assisted accelerated MRI of the ankle: clinical practice assessment |
title_full_unstemmed | AI-assisted accelerated MRI of the ankle: clinical practice assessment |
title_short | AI-assisted accelerated MRI of the ankle: clinical practice assessment |
title_sort | ai-assisted accelerated mri of the ankle: clinical practice assessment |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587051/ https://www.ncbi.nlm.nih.gov/pubmed/37857868 http://dx.doi.org/10.1186/s41747-023-00374-5 |
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