Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions
Siavash Khallaghi, C. Antonio Sánchez, Abtin Rasoulian, Yue Sun, Farhad Imani, Amir Khojaste, Orçun Göksel, Cesare Romagnoli, Hamidreza Abdi, Silvia D. Chang, Parvin Mousavi, Aaron Fenster, Aaron D. Ward, Sidney Fels, Purang Abolmaesumi · IEEE Transactions on Medical Imaging · 2015
In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.