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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Diffeomorphic Density Registration in Thoracic Computed Tomography
verfasst von : Caleb Rottman, Ben Larson, Pouya Sabouri, Amit Sawant, Sarang Joshi
Erschienen in: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016
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Abstract
Accurate motion estimation in thoracic computed tomography (CT) plays a crucial role in the diagnosis and treatment planning of lung cancer. This paper provides two key contributions to this motion estimation. First, we show we can effectively transform a CT image of effective linear attenuation coefficients to act as a density, i.e. exhibiting conservation of mass while undergoing a deformation. Second, we propose a method for diffeomorphic density registration for thoracic CT images. This algorithm uses the appropriate density action of the diffeomorphism group while offering a weighted penalty on local tissue compressibility. This algorithm appropriately models highly compressible areas of the body (such as the lungs) and incompressible areas (such as surrounding soft tissue and bones).