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Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone

  • Cardiac
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Abstract

Objectives

To determine the optimal inversion time (TI) from Look-Locker scout images using a convolutional neural network (CNN) and to investigate the feasibility of correcting TI using a smartphone.

Methods

In this retrospective study, TI-scout images were extracted using a Look-Locker approach from 1113 consecutive cardiac MR examinations performed between 2017 and 2020 with myocardial late gadolinium enhancement. Reference TI null points were independently determined visually by an experienced radiologist and an experienced cardiologist, and quantitatively measured. A CNN was developed to evaluate deviation of TI from the null point and then implemented in PC and smartphone applications. Images on 4 K or 3-megapixel monitors were captured by a smartphone, and CNN performance on each monitor was determined. Optimal, undercorrection, and overcorrection rates using deep learning on the PC and smartphone were calculated. For patient analysis, TI category differences in pre- and post-correction were evaluated using the TI null point used in late gadolinium enhancement imaging.

Results

For PC, 96.4% (772/749) of images were classified as optimal, with under- and overcorrection rates of 1.2% (9/749) and 2.4% (18/749), respectively. For 4 K images, 93.5% (700/749) of images were classified as optimal, with under- and overcorrection rates of 3.9% (29/749) and 2.7% (20/749), respectively. For 3-megapixel images, 89.6% (671/749) of images were classified as optimal, with under- and overcorrection rates of 3.3% (25/749) and 7.0% (53/749), respectively. On patient-based evaluations, subjects classified as within optimal range increased from 72.0% (77/107) to 91.6% (98/107) using the CNN.

Conclusions

Optimizing TI on Look-Locker images was feasible using deep learning and a smartphone.

Key Points

• A deep learning model corrected TI-scout images to within optimal null point for LGE imaging.

• By capturing the TI-scout image on the monitor with a smartphone, the deviation of the TI from the null point can be immediately determined.

• Using this model, TI null points can be set to the same degree as that by an experienced radiological technologist.

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Abbreviations

CMR:

Cardiac magnetic resonance imaging

CNN:

Convolutional neural network

DICOM:

Digital Imaging and Communications in Medicine

EDV:

End-diastolic volume

EF:

Ejection fraction

ESV:

End-systolic volume

LGE:

Late gadolinium enhancement

LV:

Left ventricle

PC:

Personal computer

ROI:

Region of interest

SV:

Stroke volume

TI:

Inversion time

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Funding

This study was supported by JSPS KAKENHI Grant Number 19K17188.

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Authors and Affiliations

  1. Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, 564-8565, Japan

    Yasutoshi Ohta, Emi Tateishi, Yoshiaki Morita, Tatsuya Nishii, Akiyuki Kotoku, Hiroki Horinouchi, Midori Fukuyama & Tetsuya Fukuda

Authors
  1. Yasutoshi Ohta
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  2. Emi Tateishi
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  3. Yoshiaki Morita
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  4. Tatsuya Nishii
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  5. Akiyuki Kotoku
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  6. Hiroki Horinouchi
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  7. Midori Fukuyama
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  8. Tetsuya Fukuda
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Corresponding author

Correspondence to Yasutoshi Ohta.

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Guarantor

The scientific guarantor of this publication is Dr. Tetsuya Fukuda.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

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Ohta, Y., Tateishi, E., Morita, Y. et al. Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone. Eur Radiol 33, 4688–4697 (2023). https://doi.org/10.1007/s00330-023-09465-8

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  • DOI: https://doi.org/10.1007/s00330-023-09465-8

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