Abstract. In this article we propose a cardiac motion estimation technique that uses non-rigid registration to compute the dense cardiac displacement field from 2D ultrasound sequences. Our method employs a semi-local deformation model which provides controlled smoothness. We apply a multiresolution optimization strategy for better speed and robustness. To further improve the accuracy, the sequence is registered in both forward and backward directions. We calculate additional parameters from the displacement field, such as total displacement and strain. We create an artificial ultrasound sequence of one heart cycle using a motion model and use it to validate the accuracy of the algorithm. Finally, we present results on real data from normal and pathological subjects that show the clinical applicability of our method.

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The great density and structural complexity of pulmonary vessels and airways impose limitations on the generation of accurate reference standards, which are critical in training and in the validation of image processing methods for features such as pulmonary vessel segmentation or artery-vein (AV) separations. The design of synthetic computed tomography (CT) images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image is differentiated unequivocally. This work demonstrates a complete framework to generate computational anthropomorphic CT phantoms of the human lung automatically. Starting from biological and image-based knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. A dataset of 24 labeled anthropomorphic pulmonary CT phantoms were synthesized with the proposed system. Visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems show good correspondence between real and synthetic lungs (p > 0.05 with low Cohen's d effect size and AUC values), supporting the potentiality of the tool and the usefulness of the generated phantoms in the biomedical image processing field.

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