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Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging

Purpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as...

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Autores principales: Sun, Xiaowu, Cheng, Li-Hsin, Plein, Sven, Garg, Pankaj, Moghari, Mehdi H., van der Geest, Rob J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160163/
https://www.ncbi.nlm.nih.gov/pubmed/36763209
http://dx.doi.org/10.1007/s10554-023-02804-2
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author Sun, Xiaowu
Cheng, Li-Hsin
Plein, Sven
Garg, Pankaj
Moghari, Mehdi H.
van der Geest, Rob J.
author_facet Sun, Xiaowu
Cheng, Li-Hsin
Plein, Sven
Garg, Pankaj
Moghari, Mehdi H.
van der Geest, Rob J.
author_sort Sun, Xiaowu
collection PubMed
description Purpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. Results: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27°. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26°. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. Conclusion: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10554-023-02804-2.
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spelling pubmed-101601632023-05-06 Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging Sun, Xiaowu Cheng, Li-Hsin Plein, Sven Garg, Pankaj Moghari, Mehdi H. van der Geest, Rob J. Int J Cardiovasc Imaging Original Paper Purpose: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow. Methods: A convolutional neural network (CNN) was implemented, taking cine MRI as the input and the in-plane velocity derived from the 4D flow acquisition as the ground truth. The method was evaluated using velocity vector end-point error (EPE) and angle error. Additionally, the E/A ratio and diastolic function classification derived from the predicted velocities were compared to those derived from 4D flow. Results: For intra-cardiac pixels with a velocity > 5 cm/s, our method achieved an EPE of 8.65 cm/s and angle error of 41.27°. For pixels with a velocity > 25 cm/s, the angle error significantly degraded to 19.26°. Although the averaged blood flow velocity prediction was under-estimated by 26.69%, the high correlation (PCC = 0.95) of global time-varying velocity and the visual evaluation demonstrate a good agreement between our prediction and 4D flow data. The E/A ratio was derived with minimal bias, but with considerable mean absolute error of 0.39 and wide limits of agreement. The diastolic function classification showed a high accuracy of 86.9%. Conclusion: Using a deep learning-based algorithm, intra-cardiac blood flow velocities can be predicted from long-axis cine MRI with high correlation with 4D flow derived velocities. Visualization of the derived velocities provides adjunct functional information and may potentially be used to derive the E/A ratio from conventional CMR exams. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10554-023-02804-2. Springer Netherlands 2023-02-10 2023 /pmc/articles/PMC10160163/ /pubmed/36763209 http://dx.doi.org/10.1007/s10554-023-02804-2 Text en © The Author(s) 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Paper
Sun, Xiaowu
Cheng, Li-Hsin
Plein, Sven
Garg, Pankaj
Moghari, Mehdi H.
van der Geest, Rob J.
Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
title Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
title_full Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
title_fullStr Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
title_full_unstemmed Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
title_short Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
title_sort deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10160163/
https://www.ncbi.nlm.nih.gov/pubmed/36763209
http://dx.doi.org/10.1007/s10554-023-02804-2
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