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Article

A Machine Learning-Based Roll Angle Prediction for Intracardiac Echocardiography Catheter during Bi-Plane Fluoroscopy

1
Dalio Institute of Cardiovascular Imaging, Department of Radiology, Weill Cornell Medicine, New York, NY 10021, USA
2
Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(6), 3483; https://doi.org/10.3390/app13063483
Submission received: 1 February 2023 / Revised: 28 February 2023 / Accepted: 7 March 2023 / Published: 9 March 2023
(This article belongs to the Section Biomedical Engineering)

Abstract

Catheterization is a procedure used to diagnose and treat various cardiovascular diseases. Intracardiac echocardiography (ICE) is an emerging imaging modality that has gained popularity in these procedures due to its ability to provide high-resolution images of the heart and its surrounding structures in a minimally invasive manner. However, given its limited field of view, its orientation within the heart is difficult to judge simply from observing the acquired images. Therefore, ICE catheter tracking, which requires six degrees of freedom, would be useful to better guide interventionalists during a procedure. This work demonstrates a machine learning-based approach that has been trained to predict the roll angle of an ICE catheter using landmark scalar values extracted from bi-plane fluoroscopy images. The model consists of two fully connected deep neural networks that were trained on a dataset of bi-plane fluoroscopy images acquired from a 3D printed heart phantom. The results showed high accuracy in roll angle prediction, suggesting the ability to achieve 6 degrees of freedom tracking using bi-plane fluoroscopy that can be integrated into future navigation systems embedded into the c-arm, integrated within an AR/MR headset, or in other commercial navigation systems.
Keywords: 6-DOF tracking; ICE catheter; fluoroscopy; augmented/mixed reality; machine learning; prediction 6-DOF tracking; ICE catheter; fluoroscopy; augmented/mixed reality; machine learning; prediction

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MDPI and ACS Style

Annabestani, M.; Caprio, A.; Wong, S.C.; Mosadegh, B. A Machine Learning-Based Roll Angle Prediction for Intracardiac Echocardiography Catheter during Bi-Plane Fluoroscopy. Appl. Sci. 2023, 13, 3483. https://doi.org/10.3390/app13063483

AMA Style

Annabestani M, Caprio A, Wong SC, Mosadegh B. A Machine Learning-Based Roll Angle Prediction for Intracardiac Echocardiography Catheter during Bi-Plane Fluoroscopy. Applied Sciences. 2023; 13(6):3483. https://doi.org/10.3390/app13063483

Chicago/Turabian Style

Annabestani, Mohsen, Alexandre Caprio, S. Chiu Wong, and Bobak Mosadegh. 2023. "A Machine Learning-Based Roll Angle Prediction for Intracardiac Echocardiography Catheter during Bi-Plane Fluoroscopy" Applied Sciences 13, no. 6: 3483. https://doi.org/10.3390/app13063483

APA Style

Annabestani, M., Caprio, A., Wong, S. C., & Mosadegh, B. (2023). A Machine Learning-Based Roll Angle Prediction for Intracardiac Echocardiography Catheter during Bi-Plane Fluoroscopy. Applied Sciences, 13(6), 3483. https://doi.org/10.3390/app13063483

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