Improving 2D-3D Registration Optimization Using Learned Prostate Motion Data
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.
KeywordsRoot Mean Square Motion Vector Prostate Biopsy Probe Position TRUS Probe
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