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Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population

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Abstract

This study aimed to assess the image quality and accuracy of respiratory-gated real-time two-dimensional (2D) cine incorporating deep learning reconstruction (DLR) for the quantification of biventricular volumes and function compared with those of the standard reference, that is, breath-hold 2D balanced steady-state free precession (bSSFP) cine, in an adult population. Twenty-four patients (15 men, mean age 50.7 ± 16.5 years) underwent cardiac magnetic resonance for clinical indications, and 2D DLR and bSSFP cine were acquired on the short-axis view. The image quality scores were based on three main criteria: blood-to-myocardial contrast, endocardial edge delineation, and presence of motion artifacts throughout the cardiac cycle. Biventricular end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and left ventricular mass (LVM) were analyzed. The 2D DLR cine had significantly shorter scan time than bSSFP (41.0 ± 11.3 s vs. 327.6 ± 65.8 s; p < 0.0001). Despite an analysis of endocardial edge definition and motion artifacts showed significant impairment using DLR cine compared with bSSFP (p < 0.01), the two sequences demonstrated no significant difference in terms of biventricular EDV, ESV, SV, and EF (p > 0.05). Moreover, the linear regression yielded good agreement between the two techniques (r ≥ 0.76). However, the LVM was underestimated for DLR cine (109.8 ± 34.6 g) compared with that for bSSFP (116.2 ± 40.2 g; p = 0.0291). Respiratory-gated 2D DLR cine is a reliable technique that could be used in the evaluation of biventricular volumes and function in an adult population.

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Data availability

The dataset analyzed during the present study is available from the corresponding author on reasonable request.

Abbreviations

2D:

two-dimensional

bSSFP:

balanced steady-state free precession

DLR:

deep learning reconstruction

CMR:

cardiac magnetic resonance

LVEDV:

left ventricular (LV) end-diastolic volume

LVESV:

LV end-systolic volume

LVSV:

LV stroke volume

LVEF:

LV ejection fraction

LVM:

LV mass

RVEDV:

right ventricular (RV) end-diastolic volume

RVESV:

RV end-systolic volume

RVSV:

RV stroke volume

RVEF:

RV ejection fraction

SD:

standard deviation

ICC:

intraclass correlation coefficients

CI:

confidence interval

CHD:

congenital heart disease

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Acknowledgements

The authors would like to thank Enago (www.enago.jp) for the English language review.

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Authors and Affiliations

  1. Department of Radiology, Iwate Medical University, 2-1-1, Idaidori, Yahaba, 028-3695, Iwate, Japan

    Makoto Orii, Misato Sone, Takeshi Osaki & Kunihiro Yoshioka

  2. Department of Radiology Service, Iwate Medical University, Iwate, Japan

    Kei Kikuchi & Tsuyoshi Sugawara

  3. GE Healthcare, Menlo Park, CA, USA

    Xucheng Zhu

  4. GE Healthcare, Munich, Germany

    Martin A. Janich

  5. GE Healthcare, Tokyo, Japan

    Atsushi Nozaki

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  1. Makoto Orii
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  2. Misato Sone
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  5. Tsuyoshi Sugawara
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Contributions

MO designed and drafted the manuscript. MO and MS acquired data and assessed CMR. MS and TO supported the statistical analysis. KK, TS, XZ, MJ, and AN provided technical assistance. KY substantially contributed to the manuscript and revised it critically for important intellectual content. All authors approved the submitted version.

Corresponding author

Correspondence to Makoto Orii.

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Ethics approval and consent to participate

The study was approved by the institution’s human research committee (Iwate Medical University). All methods were carried out in accordance with relevant guidelines and regulations, and informed consent was obtained from all individual participants included in the study.

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Written informed consent for publication was obtained from all participants.

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The authors declare that they have no conflict of interest.

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Orii, M., Sone, M., Osaki, T. et al. Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population. Int J Cardiovasc Imaging 39, 1001–1011 (2023). https://doi.org/10.1007/s10554-023-02793-2

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