Minimally/Non-invasive surgery has become increasingly widespread because of its therapeutic benefits such as less pain, less scarring, and shorter hospital stay. However, it is very difficult to eliminate the target cancer cells selectively without damaging nearby normal tissues and vessels since the tumors inside organs cannot be visually tracked in realtime with the existing imaging devices while organs are deformed by respiration and surgical instruments. Note that realtime 2D US imaging is widely used for monitoring the minimally invasive surgery such as Radiofrequency ablation; however, it is difficult to detect target tumors except high-echogenic regions because of its noisy and limited field of view. To handle these difficulties, we present a novel framework for estimating organ motion and deformed shape during respiration from the available features of 2D US images, by means of inverse kinematics utilizing 3D CT volumes at the inhale and exhale phases. First, we generate surface meshes of the target organ and tumor as well as centerlines of vessels at the two extreme phases considering surface correspondence. Then, the corresponding tetrahedron meshes are generated by coupling the internal components for volumetric modeling. Finally, a deformed organ mesh at an arbitrary phase is generated from the 2D US feature points for estimating the organ deformation and tumor position. To show effectiveness of the proposed method, the CT scans from real patient has been tested for estimating the motion and deformation of the liver. The experimental result shows that the average errors are less than 3mm in terms of tumor position as well as the whole surface shape.

Date Published: 14 March 2013
PDF: 6 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86710Q (14 March 2013); doi: 10.1117/12.2007856

Published in SPIE Proceedings Vol. 8671:
Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes III; Ziv R. Yaniv, Editor(s)