title: A hybrid biomechanical intensity based deformable image registration of lung 4DCT.
abstract: Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm. A hybrid DIR algorithm is proposed based on, a biomechanical model-based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used. Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone. The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm.
authors.affiliation: Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada.
authors.forename: Navid
authors.initials: N
authors.lastname: Samavati
authors.forename: Michael
authors.initials: M
authors.lastname: Velec
authors.forename: Kristy
authors.initials: K
authors.lastname: Brock
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012119:Respiration
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