11 March 2011 Real-time cardiac surface tracking from sparse samples using subspace clustering and maximum-likelihood linear regressors
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796213 (2011) https://doi.org/10.1117/12.877602
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
Cardiac minimal invasive surgeries such as catheter based radio frequency ablation of atrial fibrillation requires high-precision tracking of inner cardiac surfaces in order to ascertain constant electrode-surface contact. Majority of cardiac motion tracking systems are either limited to outer surface or track limited slices/sectors of inner surface in echocardiography data which are unrealizable in MIS due to the varying resolution of ultrasound with depth and speckle effect. In this paper, a system for high accuracy real-time 3D tracking of both cardiac surfaces using sparse samples of outer-surface only is presented. This paper presents a novel approach to model cardiac inner surface deformations as simple functions of outer surface deformations in the spherical harmonic domain using multiple maximal-likelihood linear regressors. Tracking system uses subspace clustering to identify potential deformation spaces for outer surfaces and trains ML linear regressors using pre-operative MRI/CT scan based training set. During tracking, sparse-samples from outer surfaces are used to identify the active outer surface deformation space and reconstruct outer surfaces in real-time under least squares formulation. Inner surface is reconstructed using tracked outer surface with trained ML linear regressors. High-precision tracking and robustness of the proposed system are demonstrated through results obtained on a real patient dataset with tracking root mean square error ≤ (0.23 ± 0.04)mm and ≤ (0.30 ± 0.07)mm for outer & inner surfaces respectively.
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Vimal Singh, Vimal Singh, Ahmed H. Tewfik, Ahmed H. Tewfik, } "Real-time cardiac surface tracking from sparse samples using subspace clustering and maximum-likelihood linear regressors", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796213 (11 March 2011); doi: 10.1117/12.877602; https://doi.org/10.1117/12.877602
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