3 March 2017 Breathing motion compensated registration of laparoscopic liver ultrasound to CT
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Laparoscopic Ultrasound (LUS) is regularly used during laparoscopic liver resection to locate critical vascular structures. Many tumours are iso-echoic, and registration to pre-operative CT or MR has been proposed as a method of image guidance. However, factors such as abdominal insufflation, LUS probe compression and breathing motion cause deformation of the liver, making this task far from trivial. Fortunately, within a smaller local region of interest a rigid solution can suffice. Also, the respiratory cycle can be expected to be consistent. Therefore, in this paper we propose a feature-based local rigid registration method to align tracked LUS data with CT while compensating for breathing motion. The method employs the Levenberg-Marquardt Iterative Closest Point (LMICP) algorithm, registers both on liver surface and vessels and requires two LUS datasets, one for registration and another for breathing estimation. Breathing compensation is achieved by fitting a 1D breathing model to the vessel points. We evaluate the algorithm by measuring the Target Registration Error (TRE) of three manually selected landmarks of a single porcine subject. Breathing compensation improves accuracy in 77% of the measurements. In the best case, TRE values below 3mm are obtained. We conclude that our method can potentially correct for breathing motion without gated acquisition of LUS and be integrated in the surgical workflow with an appropriate segmentation.
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João Ramalhinho, João Ramalhinho, Maria Robu, Maria Robu, Stephen Thompson, Stephen Thompson, Philip Edwards, Philip Edwards, Crispin Schneider, Crispin Schneider, Kurinchy Gurusamy, Kurinchy Gurusamy, David Hawkes, David Hawkes, Brian Davidson, Brian Davidson, Dean Barratt, Dean Barratt, Matthew J. Clarkson, Matthew J. Clarkson, } "Breathing motion compensated registration of laparoscopic liver ultrasound to CT", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101352V (3 March 2017); doi: 10.1117/12.2254488; https://doi.org/10.1117/12.2254488

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