Motion compensated cardiac reconstruction in computed tomography (CT) has traditionally been focused on coronary arteries. However, with the increasing number of cardiac CT scans being performed for the diagnosis and treatment planning of valvular diseases, there is a clear need for motion correction of the aortic valve region to assist with the reproducibility of aortic annulus measurements. A second pass approach for aortic valve motion compensation on retrospective ECG-gated CT scans is introduced here. The processing chain is comprised of four steps. A gated multi-phase cardiac reconstruction is first performed, followed by a gradient based filter to enhance the edges in the resulting time series of volume images. Subsequently these normalized filtered results are made to undergo an elastic registration and finally followed by a motion compensated reconstruction that includes the estimated motion vector fields. The method was applied to twelve clinical cases and tested for systolic (30% R-R interval) and diastolic (70% R-R interval) imaging of the aortic valve. This second pass approach leads to a significant reduction of motion artifacts especially in late systole.
Segmentation of organs in medical images can be successfully performed with shape-constrained deformable
models. A surface mesh is attracted to detected image boundaries by an external energy, while an internal
energy keeps the mesh similar to expected shapes. Complex organs like the heart with its four chambers can be
automatically segmented using a suitable shape variablility model based on piecewise affine degrees of freedom.
In this paper, we extend the approach to also segment highly variable vascular structures. We introduce a
dedicated framework to adapt an extended mesh model to freely bending vessels. This is achieved by subdividing
each vessel into (short) tube-shaped segments ("tubelets"). These are assigned to individual similarity transformations
for local orientation and scaling. Proper adaptation is achieved by progressively adapting distal vessel
parts to the image only after proximal neighbor tubelets have already converged. In addition, each newly activated
tubelet inherits the local orientation and scale of the preceeding one. To arrive at a joint segmentation of
chambers and vasculature, we extended a previous model comprising endocardial surfaces of the four chambers,
the left ventricular epicardium, and a pulmonary artery trunk. Newly added are the aorta (ascending and descending
plus arch), superior and inferior vena cava, coronary sinus, and four pulmonary veins. These vessels are
organized as stacks of triangulated rings. This mesh configuration is most suitable to define tubelet segments.
On 36 CT data sets reconstructed at several cardiac phases from 17 patients, segmentation accuracies of
0.61-0.80mm are obtained for the cardiac chambers. For the visible parts of the newly added great vessels,
surface accuracies of 0.47-1.17mm are obtained (larger errors are asscociated with faintly contrasted venous
structures).
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