Stabilized ultrasound imaging of a moving object using 2D B-mode images and convolutional neural network

Tian Xie; Mahya Shahbazi; Yixuan Wu; Russell H. Taylor; Emad M. Boctor

doi:10.1117/12.2550198

16 March 2020 Stabilized ultrasound imaging of a moving object using 2D B-mode images and convolutional neural network

Tian Xie, Mahya Shahbazi, Yixuan Wu, Russell H. Taylor, Emad M. Boctor

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113150C (2020) https://doi.org/10.1117/12.2550198
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

We present a co-robotic ultrasound imaging system that tracks lesions undergoing physiological motions, e.g., breathing, using 2D B-mode images. The approach embeds the in-plane and out-of-plane transformation estimation in a proportional joint velocity controller to minimize the 6-degree-of-freedom (DoF) transformation error. Specifically, we propose a new method to estimate the out-of-plane translation using a convolutional neural network based on speckle decorrelation. The network is trained on anatomically featureless gray-scale B-mode images and is generalized to different tissue phantoms. The tracking algorithm is validated in simulation with mimicked respiratory motions, which demonstrates the feasibility of stabilizing biopsy through ultrasound guidance.

Conference Presentation

Citation Download Citation

Tian Xie, Mahya Shahbazi, Yixuan Wu, Russell H. Taylor, and Emad M. Boctor "Stabilized ultrasound imaging of a moving object using 2D B-mode images and convolutional neural network", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150C (16 March 2020); https://doi.org/10.1117/12.2550198

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