PURPOSE: An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy. METHODS: One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration. RESULTS: A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks. CONCLUSION: The proposed algorithm offers the possibility to incorporate additional a priori knowledge-in terms of few landmarks-provided by a human expert into a non-rigid registration process.
PURPOSE: An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy. METHODS: One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration. RESULTS: A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks. CONCLUSION: The proposed algorithm offers the possibility to incorporate additional a priori knowledge-in terms of few landmarks-provided by a human expert into a non-rigid registration process.
Authors: J N Vauthey; A Chaoui; K A Do; M M Bilimoria; M J Fenstermacher; C Charnsangavej; M Hicks; G Alsfasser; G Lauwers; I F Hawkins; J Caridi Journal: Surgery Date: 2000-05 Impact factor: 3.982
Authors: Margo Shoup; Mithat Gonen; Michael D'Angelica; William R Jarnagin; Ronald P DeMatteo; Lawrence H Schwartz; Scott Tuorto; Leslie H Blumgart; Yuman Fong Journal: J Gastrointest Surg Date: 2003 Mar-Apr Impact factor: 3.452
Authors: L Zhang; S Parrini; C Freschi; V Ferrari; S Condino; M Ferrari; D Caramella Journal: Int J Comput Assist Radiol Surg Date: 2013-07-05 Impact factor: 2.924
Authors: Christian Hansen; Stephan Zidowitz; Felix Ritter; Christoph Lange; Karl Oldhafer; Horst K Hahn Journal: Int J Comput Assist Radiol Surg Date: 2012-09-30 Impact factor: 2.924
Authors: Logan W Clements; Jarrod A Collins; Jared A Weis; Amber L Simpson; Lauryn B Adams; William R Jarnagin; Michael I Miga Journal: J Med Imaging (Bellingham) Date: 2016-03-23
Authors: D Caleb Rucker; Yifei Wu; Logan W Clements; Janet E Ondrake; Thomas S Pheiffer; Amber L Simpson; William R Jarnagin; Michael I Miga Journal: IEEE Trans Med Imaging Date: 2013-09-20 Impact factor: 10.048