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Presentation + Paper
3 March 2017 Enabling image fusion for a CT guided needle placement robot
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Purpose: This study presents development and integration of hardware and software that enables ultrasound (US) and computer tomography (CT) fusion for a FDA-approved CT-guided needle placement robot. Having real-time US image registered to a priori-taken intraoperative CT image provides more anatomic information during needle insertion, in order to target hard-to-see lesions or avoid critical structures invisible to CT, track target motion, and to better monitor ablation treatment zone in relation to the tumor location. Method: A passive encoded mechanical arm is developed for the robot in order to hold and track an abdominal US transducer. This 4 degrees of freedom (DOF) arm is designed to attach to the robot end-effector. The arm is locked by default and is released by a press of button. The arm is designed such that the needle is always in plane with US image. The articulated arm is calibrated to improve its accuracy. Custom designed software (OncoNav, NIH) was developed to fuse real-time US image to a priori-taken CT. Results: The accuracy of the end effector before and after passive arm calibration was 7.07mm ± 4.14mm and 1.74mm ±1.60mm, respectively. The accuracy of the US image to the arm calibration was 5mm. The feasibility of US-CT fusion using the proposed hardware and software was demonstrated in an abdominal commercial phantom. Conclusions: Calibration significantly improved the accuracy of the arm in US image tracking. Fusion of US to CT using the proposed hardware and software was feasible.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Reza Seifabadi, Sheng Xu, Fereshteh Aalamifar, Gnanasekar Velusamy, Kaliyappan Puhazhendi, and Bradford J. Wood "Enabling image fusion for a CT guided needle placement robot", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101350S (3 March 2017);

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