Liver Motion Estimation via Locally Adaptive Over-Segmentation Regularization
Despite significant advances in the development of deformable registration methods, motion correction of deformable organs such as the liver remain a challenging task. This is due to not only low contrast in liver imaging, but also due to the particularly
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Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK [email protected] 2 Department of Oncology, University of Oxford, UK 3 Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, UK Institute of Medical Informatics, University of L¨ ubeck, Germany
Abstract. Despite significant advances in the development of deformable registration methods, motion correction of deformable organs such as the liver remain a challenging task. This is due to not only low contrast in liver imaging, but also due to the particularly complex motion between scans primarily owing to patient breathing. In this paper, we address abdominal motion estimation using a novel regularization model that is advancing the state-of-the-art in liver registration in terms of accuracy. We propose a novel regularization of the deformation field based on spatially adaptive over-segmentation, to better model the physiological motion of the abdomen. Our quantitative analysis of abdominal Computed Tomography and dynamic contrast-enhanced Magnetic Resonance Imaging scans show a significant improvement over the state-of-the-art Demons approaches. This work also demonstrates the feasibility of segmentationfree registration between clinical scans that can inherently preserve sliding motion at the lung and liver boundary interfaces.
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Introduction
Analysis of functional abdominal imaging (e.g. dynamic magnetic resonance imaging (DCE-MRI)) and structural imaging (such as CT or MRI) is an emerging research area that can potentially lead to improved strategies for differential diagnosis and planning of personalized treatment (e.g. patient stratification) of abdominal cancer. In this work, we present a generic approach for intra-subject motion correction of time sequences, applied to both standard 4D CT acquisition, and relatively new quantitative imaging techniques such as DCE-MRI. This will ultimately provide new opportunities for tumor heterogeneity assessment for patients, with the potential of extending our understanding of human liver tumor complexity [10]. Deformable registration of time scans acquired using modalities with contrast agent is challenging due to: 1) significant amount of motion between consecutive scans including sliding motion at the lung and liver c Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part III, LNCS 9351, pp. 427–434, 2015. DOI: 10.1007/978-3-319-24574-4_51
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interface; 2) low contrast of liver tissue; and 3) local volume intensity changes due to either contrast uptake in DCE-MRI. Thus, robust image registration is an inevitable post-acquisition step to enable quantitative pharmacokinetic analysis of motion-free DCE-MRI data. Conventional motion correction algorithms use generic similarity measures such as sum-of-squared differences with a statistical prior to find an optimal transformation [5]. Alternatively, registration can be performed using a physiological image formation mo
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