Image Registration Using Tensor Grids for Lung Ventilation Studies
In non-parametric image registration it is often not possible to work with the original resolution of the images due to high processing times and lack of memory. However, for some medical applications the information contained in the original resolution is crucial in certain regions of the image while being negligible in others. To adapt to this problem we will present an approach using tensor grids, which provide a sparser image representation and thereby allow the use of the highest image resolution locally. Applying the presented scheme to a lung ventilation estimation shows that one may considerably save on time and memory while preserving the registration quality in the regions of interest.
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- 1.Kabus S, von Berg J, Yamamoto T, et al. Lung ventilation estimation based on 4D-CT imaging. Proc MICCAI Workshop: Pulmonary Image Analysis. 2008; p. 73–81.Google Scholar
- 2.Cook TS, Tustison N, Biederer J, et al. How do registration parameters affect quantitation of lung kinematics. In: Proc MICCAI; 2007. p. 817–824.Google Scholar
- 3.Haber E, Heldmann S, Modersitzki J. Adaptive mesh refinement for non-parametric image registration. SIAM J Sci Comp. 2007;Submitted.Google Scholar
- 4.Papenberg N, Modersitzki J, Fischer B. Registrierung im Fokus. Proc BVM. 2008; p. 138–142.Google Scholar
- 5.Vandemeulebroucke J, Sarrut D, Clarysse P. The POPI-model, a point-validated pixel-based breathing thorax model; 2007. ICCR, Toronto, Canada.Google Scholar
- 6.Modersitzki J. Numerical Methods for Image Registration. OUP; 2004Google Scholar