OBJECTIVE: Computer assisted planning of liver surgery based on preoperative computed tomography (CT) or magnetic resonance imaging (MRI) data can be an important aid to operability decisions and visualization of individual patients' 3D anatomy. A navigation system based on intraoperative 3D ultrasound may help the surgeon to precisely localize vessels, vascular territories or tumors. The preoperative planning must be transferred to the intraoperative ultrasound data and thus to the patient on the operating table. Due to deformations of the liver between planning and surgery, a fast non-rigid registration method is needed. MATERIALS AND METHODS: A feature-based non-rigid registration approach based on the centerlines of the portal veins has been developed. The combination of an iterative closest point (ICP) approach and Multilevel B-Spline transformations offers a fast registration method. The vessels are segmented and their centerlines extracted from preoperative CT/MRI and intraoperative 3D Powerdoppler ultrasound data. Anatomical corresponding points on the centerlines of both modalities are determined in each iteration of the ICP algorithm. The search for corresponding points is restricted to a given search radius and the direction of the vessels is incorporated. RESULTS: The algorithm has been evaluated on two transcutaneous and one intraoperative clinical ultrasound data set from three different patients. Only a very few vessel segments were not assigned correctly compared to manual assignments. Using non-rigid transformations improved the root mean square target registration error of the vessels by approximately 3-5 mm. CONCLUSIONS: The proposed registration method is fast enough for clinical application in liver surgery. Initial accuracy results are promising and must be further evaluated, particularly in the operating room.
OBJECTIVE: Computer assisted planning of liver surgery based on preoperative computed tomography (CT) or magnetic resonance imaging (MRI) data can be an important aid to operability decisions and visualization of individual patients' 3D anatomy. A navigation system based on intraoperative 3D ultrasound may help the surgeon to precisely localize vessels, vascular territories or tumors. The preoperative planning must be transferred to the intraoperative ultrasound data and thus to the patient on the operating table. Due to deformations of the liver between planning and surgery, a fast non-rigid registration method is needed. MATERIALS AND METHODS: A feature-based non-rigid registration approach based on the centerlines of the portal veins has been developed. The combination of an iterative closest point (ICP) approach and Multilevel B-Spline transformations offers a fast registration method. The vessels are segmented and their centerlines extracted from preoperative CT/MRI and intraoperative 3D Powerdoppler ultrasound data. Anatomical corresponding points on the centerlines of both modalities are determined in each iteration of the ICP algorithm. The search for corresponding points is restricted to a given search radius and the direction of the vessels is incorporated. RESULTS: The algorithm has been evaluated on two transcutaneous and one intraoperative clinical ultrasound data set from three different patients. Only a very few vessel segments were not assigned correctly compared to manual assignments. Using non-rigid transformations improved the root mean square target registration error of the vessels by approximately 3-5 mm. CONCLUSIONS: The proposed registration method is fast enough for clinical application in liver surgery. Initial accuracy results are promising and must be further evaluated, particularly in the operating room.
Authors: Sean Jy-Shyang Chen; Ingerid Reinertsen; Pierrick Coupé; Charles X B Yan; Laurence Mercier; D Rolando Del Maestro; D Louis Collins Journal: Int J Comput Assist Radiol Surg Date: 2012-03-24 Impact factor: 2.924
Authors: Karl J Oldhafer; Gregor A Stavrou; Guido Prause; Heinz-Otto Peitgen; Tim C Lueth; Stefan Weber Journal: Langenbecks Arch Surg Date: 2009-03-12 Impact factor: 3.445