Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions

• Published in: IEEE transactions on medical imaging Vol. 34; no. 11; pp. 2404 - 2414
• Main Authors: Khallaghi, Siavash, Sanchez, C. Antonio, Rasoulian, Abtin, Yue Sun, Imani, Farhad, Khojaste, Amir, Goksel, Orcun, Romagnoli, Cesare, Abdi, Hamidreza, Chang, Silvia, Mousavi, Parvin, Fenster, Aaron, Ward, Aaron, Fels, Sidney, Abolmaesumi, Purang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomechanics; Deformation; Extrapolation; Finite Element Analysis; Finite element method; Finite element model; Gaussian mixture model; Humans; Image segmentation; Imaging, Three-Dimensional - methods; Iterative closest point algorithm; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mathematical model; Mathematical models; Missing data; Normal Distribution; Prostate; Prostate - diagnostic imaging; Registration; surface registration; transrectal ultrasound; Ultrasonic imaging; Ultrasonography
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2015.2440253

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.