21 March 2014 A hybrid biomechanical model-based image registration method for sliding objects
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The sliding motion between two anatomic structures, such as lung against chest wall, liver against surrounding tissues, produces a discontinuous displacement field between their boundaries. Capturing the sliding motion is quite challenging for intensity-based image registration methods in which a smoothness condition has commonly been applied to ensure the deformation consistency of neighborhood voxels. Such a smoothness constraint contradicts motion physiology at the boundaries of these anatomic structures. Although various regularisation schemes have been developed to handle sliding motion under the framework of non-rigid intensity-based image registration, the recovered displacement field may still not be physically plausible. In this study, a new framework that incorporates a patient-specific biomechanical model with a non-rigid image registration scheme for motion estimation of sliding objects has been developed. The patient-specific model provides the motion estimation with an explicit simulation of sliding motion, while the subsequent non-rigid image registration compensates for smaller residuals of the deformation due to the inaccuracy of the physical model. The algorithm was tested against the results of the published literature using 4D CT data from 10 lung cancer patients. The target registration error (TRE) of 3000 landmarks with the proposed method (1.37±0.89 mm) was significantly lower than that with the popular B-spline based free form deformation (FFD) registration (4.5±3.9 mm), and was smaller than that using the B-spline based FFD registration with the sliding constraint (1.66±1.14 mm) or using the B-spline based FFD registration on segmented lungs (1.47±1.1 mm). A paired t-test showed that the improvement of registration performance with the proposed method was significant (p<0.01). The propose method also achieved the best registration performance on the landmarks near lung surfaces. Since biomechanical models captured most of the lung deformation, the final estimated deformation field was more physically plausible.
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Lianghao Han, Lianghao Han, David Hawkes, David Hawkes, Dean Barratt, Dean Barratt, "A hybrid biomechanical model-based image registration method for sliding objects", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340G (21 March 2014); doi: 10.1117/12.2043749; https://doi.org/10.1117/12.2043749

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