Fast Groupwise 4D Deformable Image Registration for Irregular Breathing Motion Estimation
Tumor heterogeneity can be assessed quantitatively by analyzing dynamic contrast-enhanced imaging modalities potentially leading to improvement in the diagnosis and treatment of cancer, for example of the lung. However, the acquisition of standard lung sequences is often compromised by irregular breathing motion artefacts, resulting in unsystematic errors when estimating tissue perfusion parameters. In this work, we illustrate implicit deformable image registration that integrates the Demons algorithm using the local correlation coefficient as a similarity measure, and locally adaptive regularization that enables incorporation of both spatial sliding motions and irregular temporal motion patterns. We also propose a practical numerical approximation of the regularization model to improve both computational time and registration accuracy, which are important when analyzing long clinical sequences. Our quantitative analysis of 4D lung Computed Tomography and Computed Tomography Perfusion scans from clinical lung trial shows significant improvement over state-of-the-art pairwise registration approaches.
We acknowledge funding from the CRUK/EPSRC Cancer Imaging Centre in Oxford. The ATOM trial is sponsored by the University of Oxford and coordinated by the Oncology Clinical Trials Office. It is supported by the Howat Foundation, Oxford Cancer Imaging Centre, Cancer Research UK, National Institute of Health Research, Oxford Biomedical Research Centre and the ECMC. BWP acknowledges Oxford NIHR Biomedical Research Centre (Rutherford Fund).
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