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Deep learning–based acceleration of Compressed Sense MR imaging of the ankle

OBJECTIVES: To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. METHODS: Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensin...

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Autores principales: Foreman, Sarah C., Neumann, Jan, Han, Jessie, Harrasser, Norbert, Weiss, Kilian, Peeters, Johannes M., Karampinos, Dimitrios C., Makowski, Marcus R., Gersing, Alexandra S., Woertler, Klaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705492/
https://www.ncbi.nlm.nih.gov/pubmed/35751695
http://dx.doi.org/10.1007/s00330-022-08919-9
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author Foreman, Sarah C.
Neumann, Jan
Han, Jessie
Harrasser, Norbert
Weiss, Kilian
Peeters, Johannes M.
Karampinos, Dimitrios C.
Makowski, Marcus R.
Gersing, Alexandra S.
Woertler, Klaus
author_facet Foreman, Sarah C.
Neumann, Jan
Han, Jessie
Harrasser, Norbert
Weiss, Kilian
Peeters, Johannes M.
Karampinos, Dimitrios C.
Makowski, Marcus R.
Gersing, Alexandra S.
Woertler, Klaus
author_sort Foreman, Sarah C.
collection PubMed
description OBJECTIVES: To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. METHODS: Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6–5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9–7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen’s kappa were used to compare CSAI with CS sequences. RESULTS: The correlation was perfect between CS and CSAI2x (κ = 1.0) and excellent for CS and CSAI3x (κ = 0.86–1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower (p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS (p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS (p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve (p ≤ 0.005). CONCLUSIONS: Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan. KEY POINTS: • Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality. • Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients. • CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times.
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spelling pubmed-97054922022-11-30 Deep learning–based acceleration of Compressed Sense MR imaging of the ankle Foreman, Sarah C. Neumann, Jan Han, Jessie Harrasser, Norbert Weiss, Kilian Peeters, Johannes M. Karampinos, Dimitrios C. Makowski, Marcus R. Gersing, Alexandra S. Woertler, Klaus Eur Radiol Musculoskeletal OBJECTIVES: To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. METHODS: Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6–5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9–7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen’s kappa were used to compare CSAI with CS sequences. RESULTS: The correlation was perfect between CS and CSAI2x (κ = 1.0) and excellent for CS and CSAI3x (κ = 0.86–1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower (p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS (p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS (p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve (p ≤ 0.005). CONCLUSIONS: Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan. KEY POINTS: • Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality. • Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients. • CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times. Springer Berlin Heidelberg 2022-06-25 2022 /pmc/articles/PMC9705492/ /pubmed/35751695 http://dx.doi.org/10.1007/s00330-022-08919-9 Text en © The Author(s) 2022 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 Musculoskeletal
Foreman, Sarah C.
Neumann, Jan
Han, Jessie
Harrasser, Norbert
Weiss, Kilian
Peeters, Johannes M.
Karampinos, Dimitrios C.
Makowski, Marcus R.
Gersing, Alexandra S.
Woertler, Klaus
Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
title Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
title_full Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
title_fullStr Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
title_full_unstemmed Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
title_short Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
title_sort deep learning–based acceleration of compressed sense mr imaging of the ankle
topic Musculoskeletal
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9705492/
https://www.ncbi.nlm.nih.gov/pubmed/35751695
http://dx.doi.org/10.1007/s00330-022-08919-9
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