Optimization Techniques for Semi-Automated 3D Rigid Registration in Multimodal Image-Guided Deep Brain Stimulation

Abstract Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device the brain to deliver electrical impulses. Image between computed tomography (CT) and cone beam (CBCT) involves fusing images with specific field of view (FOV) visualize individual electrode contacts. This contains important information about location segmented contacts that can reduce time required for programming. We performed semi-automated multimodal different FOV CT CBCT due tiny structures necessitate high accuracy registration. In this work, we present an optimization workflow multi-modal using combination similarity metrics, interpolators, optimizers. Optimization-based rigid (RIR) common method registering images. The selection appropriate interpolators metrics crucial success optimization-based process.We rely on quantitative measures compare their performance. Registration was datasets algorithm written Python Insight Segmentation Toolkit (ITK). Several combinations were used, including mean square difference (MSD), mutual (MI), correlation nearest neighbors (NN), linear (LI), B-Spline (SPI), respectively. as metric, interpolation, GD optimizer performs best optimizing 3D RIR algorithm, enhancing visualization Patients undergoing therapy may ultimately benefit from this.