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This paper describes a method for automatic contour detection in long-axis cardiac MRI using an adaptive virtual exploring robot. The robot is a simulated trained virtual autonomous tri-cycle that is initially positioned in a binary representation of the left ventricle (LV) and finds the contours during navigation through the ventricle. The method incorporates global and local prior shape knowledge of the LV in order to adapt the navigational parameters. Together with kinematic constraints, the robot is able to avoid concave regions such as papillary muscles and navigate through narrow corridors such as the apex. Validation was performed on in-vivo multiphase long-axis cardiac MRI images of 11 subjects. Results showed good correlation between the quantitative parameters, computed from manual and automatic segmentation: for end-diastolic volume (EDV) r=0.91, for end-systolic volume (ESV) r=0.93, ejection fraction (EF) r=0.77, and LV mass (LVM) r=0.80. [ABSTRACT FROM AUTHOR]

In this paper, a new technique (SPASM) based on a 3D-ASM is presented for automatic segmentation of cardiac MRI image data sets consisting of multiple planes with different orientations, and with large undersampled regions. SPASM was applied to sparsely sampled and radially oriented cardiac LV image data. Performance of SPASM has been compared to results from other methods reported in literature. The accuracy of SPASM is comparable to these other methods, but SPASM uses considerably less image data. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for lung registration and cardiac motion prediction, based on a 3D geometric model of the left lung. Feature points, describing a shape of this anatomical object, are automatically extracted from acquired tomographic images. The "goodness-of-fit" measure is assessed at each step in the iterative scheme until spatial alignment between the model and subject's specific data is achieved. We applied the proposed methods to register the 3D lung surfaces of 5 healthy volunteers of thoracic MRI acquired in different respiratory phases. We also utilized this approach to predict the spatial displacement of the human heart due to respiration. The obtained results demonstrate a promising registration performance. [ABSTRACT FROM AUTHOR]