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An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance
BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and elect...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367083/ https://www.ncbi.nlm.nih.gov/pubmed/35948936 http://dx.doi.org/10.1186/s12968-022-00879-9 |
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author | Morales, Manuel A. Assana, Salah Cai, Xiaoying Chow, Kelvin Haji-valizadeh, Hassan Sai, Eiryu Tsao, Connie Matos, Jason Rodriguez, Jennifer Berg, Sophie Whitehead, Neal Pierce, Patrick Goddu, Beth Manning, Warren J. Nezafat, Reza |
author_facet | Morales, Manuel A. Assana, Salah Cai, Xiaoying Chow, Kelvin Haji-valizadeh, Hassan Sai, Eiryu Tsao, Connie Matos, Jason Rodriguez, Jennifer Berg, Sophie Whitehead, Neal Pierce, Patrick Goddu, Beth Manning, Warren J. Nezafat, Reza |
author_sort | Morales, Manuel A. |
collection | PubMed |
description | BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR. METHODS: A 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal). RESULTS: The DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL [− 11.7, 17.8], P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL [− 1.3, 15.3], P < 0.001) and LV ejection fraction (− 5.0% [− 11.1, 1.0], P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame. CONCLUSIONS: Our proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-022-00879-9. |
format | Online Article Text |
id | pubmed-9367083 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93670832022-08-12 An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance Morales, Manuel A. Assana, Salah Cai, Xiaoying Chow, Kelvin Haji-valizadeh, Hassan Sai, Eiryu Tsao, Connie Matos, Jason Rodriguez, Jennifer Berg, Sophie Whitehead, Neal Pierce, Patrick Goddu, Beth Manning, Warren J. Nezafat, Reza J Cardiovasc Magn Reson Technical Notes BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR. METHODS: A 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal). RESULTS: The DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL [− 11.7, 17.8], P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL [− 1.3, 15.3], P < 0.001) and LV ejection fraction (− 5.0% [− 11.1, 1.0], P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame. CONCLUSIONS: Our proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-022-00879-9. BioMed Central 2022-08-11 /pmc/articles/PMC9367083/ /pubmed/35948936 http://dx.doi.org/10.1186/s12968-022-00879-9 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Technical Notes Morales, Manuel A. Assana, Salah Cai, Xiaoying Chow, Kelvin Haji-valizadeh, Hassan Sai, Eiryu Tsao, Connie Matos, Jason Rodriguez, Jennifer Berg, Sophie Whitehead, Neal Pierce, Patrick Goddu, Beth Manning, Warren J. Nezafat, Reza An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance |
title | An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance |
title_full | An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance |
title_fullStr | An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance |
title_full_unstemmed | An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance |
title_short | An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance |
title_sort | inline deep learning based free-breathing ecg-free cine for exercise cardiovascular magnetic resonance |
topic | Technical Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367083/ https://www.ncbi.nlm.nih.gov/pubmed/35948936 http://dx.doi.org/10.1186/s12968-022-00879-9 |
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