A registration method for motion estimation in dynamic medical imaging data is proposed. Registration is performed directly on the dynamic image, thus avoiding a bias towards a specifically chosen reference time point. Both spatial and temporal smoothness of the transformations are taken into account. Optionally, cyclic motion can be imposed, which can be useful for visualization (viewing the segmentation sequentially) or model building purposes. The method is based on a 3D (2D. +. time) or 4D (3D. +. time) free-form B-spline deformation model, a similarity metric that minimizes the intensity variances over time and constrained optimization using a stochastic gradient descent method with adaptive step size estimation. The method was quantitatively compared with existing registration techniques on synthetic data and 3D. +. t computed tomography data of the lungs. This showed subvoxel accuracy while delivering smooth transformations, and high consistency of the registration results. Furthermore, the accuracy of semi-automatic derivation of left ventricular volume curves from 3D. +. t computed tomography angiography data of the heart was evaluated. On average, the deviation from the curves derived from the manual annotations was approximately 3%. The potential of the method for other imaging modalities was shown on 2D. +. t ultrasound and 2D. +. t magnetic resonance images. The software is publicly available as an extension to the registration package elastix.

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Keywords 4D, Adaptive step size, B splines, B-spline deformation, Communication channels (information theory), Computed tomography angiography, Computed tomography data, Computerized tomography, Constrained optimization, Cyclic motions, Dynamic, Dynamic images, Estimation, Imaging modality, Left ventricular, Magnetic resonance, Magnetic resonance images, Magnetic resonance imaging, Manual annotation, Medical imaging, Motion estimation, ND+t, Nonrigid registration, Optimization approach, Reference time, Registration methods, Registration techniques, Semi-automatics, Ship propellers, Similarity metrics, Smooth transformation, Splines, Stochastic gradient descent method, Stochastic models, Sub-voxel accuracy, Synthetic data, Visualization, anatomical variation, article, computed tomographic angiography, computer assisted tomography, computer program, controlled study, diagnostic accuracy, diagnostic imaging, heart left ventricle volume, human, image analysis, image intensifier, nuclear magnetic resonance imaging, priority journal, quantitative analysis, three dimensional imaging, ultrasound, visual information
Persistent URL dx.doi.org/10.1016/j.media.2010.10.003, hdl.handle.net/1765/22998