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Improving 2D-3D Registration Optimization Using Learned Prostate Motion Data
Tharindu De Silva1,2, Derek W. Cool1,3, Jing Yuan1, Cesare Romognoli1,3, Aaron Fenster1,2,4, and Aaron D. Ward2,4
1Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, Canada
2Biomedical Engineering Graduate Program, The University of Western Ontario, Canada
tdesilva@robarts.ca
aaron.ward@uwo.ca
3Department of Medical Imaging, The University of Western Ontario, Canada
4Department of Medical Biophysics, The University of Western Ontario, Canada
Abstract. Prostate motion due to transrectal ultrasound (TRUS) probe pressure and patient movement causes target misalignments during 3D TRUS-guided biopsy. Several solutions have been proposed to perform 2D-3D registration for motion compensation. To improve registration accuracy and robustness, we developed and evaluated a registration algorithm whose optimization is based on learned prostate motion characteristics relative to different tracked probe positions and prostate sizes. We performed a principal component analysis of previously observed motions and utilized the principal directions to initialize Powell’s direction set method during optimization. Compared with the standard initialization, our approach improved target registration error to 2.53±1.25 mm after registration. Multiple initializations along the major principal directions improved the robustness of the method at the cost of additional execution time of 1.5 s. With a total execution time of 3.2 s to perform motion compensation, this method is amenable to useful integration into a clinical 3D guided prostate biopsy workflow.
LNCS 8150, p. 124 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013