Ultrasound and Fluoroscopic Images Fusion by Autonomous Ultrasound Probe Detection

New minimal-invasive interventions such as transcatheter valve procedures exploit multiple imaging modalities to guide tools (fluoroscopy) and visualize soft tissue (transesophageal echocardiography (TEE)). Currently, these complementary modalities are visualized in separate coordinate systems and on separate monitors creating a challenging clinical workflow. This paper proposes a novel framework for fusing TEE and fluoroscopy by detecting the pose of the TEE probe in the fluoroscopic image. Probe pose detection is challenging in fluoroscopy and conventional computer vision techniques are not well suited. Current research requires manual initialization or the addition of fiducials. The main contribution of this paper is autonomous six DoF pose detection by combining discriminative learning techniques with a fast binary template library. The pose estimation problem is reformulated to incrementally detect pose parameters by exploiting natural invariances in the image. The theoretical co...

• Type: Conference
• Year: 2012
• Where: MICCAI
• Authors: Peter Mountney, Razvan Ionasec, Markus Kaizer, Sina Mamaghani, Wen Wu, Terrence Chen, Matthias John, Jan Boese, Dorin Comaniciu
• Keywords: Detection, Fluoroscopy, Fusion, Machine Learning, Registration, Tavi, Ultrasound, Xray