Real-time liver motion estimation via combined surface imaging and single x-ray imaging using a deep learning-based approach (Surf-X) (Conference Presentation)

Hua-Chieh Shao; Yunxiang Li; Jing Wang; Steve Jiang; You Zhang

doi:10.1117/12.2653987

3 April 2023 Real-time liver motion estimation via combined surface imaging and single x-ray imaging using a deep learning-based approach (Surf-X) (Conference Presentation)

Hua-Chieh Shao, Yunxiang Li, Jing Wang, Steve Jiang, You Zhang

Author Affiliations +

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124660O (2023) https://doi.org/10.1117/12.2653987
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

We developed a deep learning (DL)-based framework, Surf-X, to estimate real-time 3D liver motion. Surf-X synergizes two imaging modalities, optical surface imaging and x-ray imaging, to track the 3D liver motion. By incorporating prior knowledge of motion learnt from patient-specific 4D-CTs, Surf-X progressively solves the liver motion in two steps: firstly from an optical surface image via learnt internal-external correlations; and secondly from directly-observed motion on an x-ray projection. Surf-X combines the complementary information from surface and x-ray imaging and solves liver motion more accurately and robustly than either modality alone, all at a temporal resolution of <100 milliseconds.

Conference Presentation

Citation Download Citation

Hua-Chieh Shao, Yunxiang Li, Jing Wang, Steve Jiang, and You Zhang "Real-time liver motion estimation via combined surface imaging and single x-ray imaging using a deep learning-based approach (Surf-X) (Conference Presentation)", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124660O (3 April 2023); https://doi.org/10.1117/12.2653987

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