Recently, foundations rooted in physics have been laid down for the goal of simulating the propagation of a guide wire inside the vasculature. At the heart of the simulation lies the fundamental task of energy minimization. The energy comes from interaction with the vessel wall and the bending of the guide wire. For the simulation to be useful in actual training, obtaining the smallest possible optimization time is key. In this paper, we, therefore, study the influence of using different optimization techniques: a semianalytical approximation algorithm, the conjugate-gradients algorithm, and an evolutionary algorithm (EA), specifically the GLIDE algorithm. Simulation performance has been measured on phantom data. The results show that a substantial reduction in time can be obtained while the error is increased only slightly if conjugate gradients or GLIDE is used.
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http://dx.doi.org/10.1109/TMI.2006.886814 | DOI Listing |
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