Optimal sensor placement for predictive cardiac motion modeling
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Recent improvements in the resolution of Positron Emission Tomography (PET) imaging have not translated into equivalent advances in diagnostic accuracy. Due to long acquisition times involved, the functional imaging modality is hampered by motion artefacts due to respiratory motion. In this paper, two methods for correcting reconstructed PET images through a list-mode re-binning process are investigated. The first method rebins the list-mode data according to a globally defined 3D affine transformation. The second is a novel approach that combines multiple independent 2D affine transforms in order to exploit the specific properties of 2D tomographic reconstruction. Each affine transformation method is applied to the respiratory gated sequence of line-of-response events prior to image reconstruction, thus compensating for any respiratory motion. The deformation models are derived from a non-rigid 3D/3D registration model applied to retrospectively gated MRI acquired during free-breathing. The motion correction schemes are validated using a simulation framework with respiratory gated MRI scans of 10 subjects to generate the required activity maps for estimating emission sinograms. This allows the ground truth solution to be derived so that the motion corrected reconstruction can be compared quantitatively. It is shown that the higher degrees of freedom of the 2D affine motion compensation model is superior to the 3D affine transform provided one incorporates weak material constraints to avoid ill conditioning and can tolerate the lower SNR that 2D reconstruction implies.