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Recursive multiresolution convolutional neural networks for 3D aortic valve annulus planimetry

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Transcatheter aortic valve replacement (TAVR) is the standard of care in a large population of patients with severe symptomatic aortic valve stenosis. The sizing of TAVR devices is done from ECG-gated CT angiographic image volumes. The most crucial step of the analysis is the determination of the aortic valve annular plane. In this paper, we present a fully tridimensional recursive multiresolution convolutional neural network (CNN) to infer the location and orientation of the aortic valve annular plane.

Methods

We manually labeled 1007 ECG-gated CT volumes from 94 patients with severe degenerative aortic valve stenosis. The algorithm was implemented and trained using the TensorFlow framework (Google LLC, USA). We performed K-fold cross-validation with K = 9 groups such that CT volumes from a given patient are assigned to only one group.

Results

We achieved an average out-of-plane localization error of (0.7 ± 0.6) mm for the training dataset and of (0.9 ± 0.8) mm for the evaluation dataset, which is on par with other published methods and clinically insignificant. The angular orientation error was (3.9 ± 2.3)° for the training dataset and (6.4 ± 4.0)° for the evaluation dataset. For the evaluation dataset, 84.6% of evaluation image volumes had a better than 10° angular error, which is similar to expert-level accuracy. When measured in the inferred annular plane, the relative measurement error was (4.73 ± 5.32)% for the annular area and (2.46 ± 2.94)% for the annular perimeter.

Conclusions

The proposed algorithm is the first application of CNN to aortic valve planimetry and achieves an accuracy on par with proposed automated methods for localization and approaches an expert-level accuracy for orientation. The method relies on no heuristic specific to the aortic valve and may be generalizable to other anatomical features.

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Funding

This study received no external funding.

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Authors and Affiliations

  1. Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada

    Pascal Theriault-Lauzier

  2. Division of Cardiology, McGill University Health Centre, Montreal, QC, Canada

    Pascal Theriault-Lauzier, Hind Alsosaimi, Negareh Mousavi, Jean Buithieu, Marco Spaziano, Giuseppe Martucci, James Brophy & Nicolo Piazza

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  1. Pascal Theriault-Lauzier
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  2. Hind Alsosaimi
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  3. Negareh Mousavi
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  4. Jean Buithieu
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  5. Marco Spaziano
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  8. Nicolo Piazza
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Corresponding author

Correspondence to Pascal Theriault-Lauzier.

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Conflict of interest

Pascal Theriault-Lauzier is a consultant for Circle CVI and receives royalties from sales of their software. Nicolo Piazza is a consultant and proctor for Medtronic and receives royalties from sales of Circle CVI software. Giuseppe Martucci is a proctor for Medtronic. All other authors have no conflict of interest to declare.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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This article does not contain any studies involving animals performed by any of the authors.

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P. Theriault-Lauzier formally at the Division of Cardiology, McGill University, Montreal, QC, Canada.

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Theriault-Lauzier, P., Alsosaimi, H., Mousavi, N. et al. Recursive multiresolution convolutional neural networks for 3D aortic valve annulus planimetry. Int J CARS 15, 577–588 (2020). https://doi.org/10.1007/s11548-020-02131-0

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  • DOI: https://doi.org/10.1007/s11548-020-02131-0

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