In this paper, a software system for supporting stenting in the vascular domain is presented. The system covers all treatment phases from diagnosis to follow-up examinations. During the preoperative phase, the system supports the physician by suggesting the date and the kind (open surgery, minimally invasive) of intervention based on segmenting the patient's CT data. Therapy planning is additionally supported by a computer-aided stent simulation. Using virtual stenting, it is possible to simulate stents of different manufacturers in the preoperative CT data of the patient. As a result, it can be decided whether a chosen stent has proper dimensions and should be used during the following intervention. The intraoperative phase is supported by visualizing the selected stent from the planning phase at the requested position. After stenting, regular follow-up examinations are necessary to detect stent migration and endoleaks. These time-consuming procedures are also supported by the developed system.

Recent advances in CT technology have made possible the acquisition of high resolution images of the heart in which important anatomical features such as the coronary vessels are visible. The state of the art in MR Imaging techniques, on the other hand, typically provides lower resolution but can facilitate the differentiation of viable (healthy) cardiac tissue from infarcted (injured) tissue. The registration of CT and MR images of the same heart permit the association of anatomy with function. As an example, given an infarcted region, the coronary vessels responsible for irrigating that region may be isolated and targeted for treatment (e.g., coronary bypass). We demonstrate a software package, PROTEUS, which is capable of registering CT and MR images. Since the manifestation of cardiac tissue in the two modalities differs so greatly we rely on features to perform the matching. Specifically, we employ a model-based approach that relies on segmented contours delineating the inner and outer borders of the left ventricle (LV). Given these contours, our technique is completely automatic.

Hybrid models are powerful tools for recovery in that they simultaneously provide a gross parametric as well as a detailed description of an object. However, it is difficult to directly employ hybrid models in the segmentation process since they are not guaranteed to locate the optimal boundaries in cross-sectional slices. Propagating 2D active contours from slice to slice, on the other hand, to delineate an object's boundaries is often effective, but may run into problems when the object's topology changes, such as at bifurcations or even in areas of high curvature. Here, we present a cooperative framework to exploit the positive aspects of both 3D hybrid model and 2D active contour approaches for segmentation and recovery. In this framework the user-defined parametric component of a 3D hybrid model provides constraints for a set of 2D segmentations performed by active contours. The same hybrid model is then fit both parametrically and locally to this segmentation. For the hybrid model fit we employ several new variations on the physically-motivated paradigm which seek to speed recovery while guaranteeing stability. A by-product of these variations is an increased generality of the method via the elimination, of some of its ad hoc parameters. We apply our cooperative framework to the recovery of branching cylindrical structures from 3D image volumes. The hybrid model we employ has a novel parametric component which is a fusion of individual cylinders. These cylinders have spines that are arbitrary space curves and cross-sections which may be any star shaped planar curve.

We demonstrate a method for calculating the blood pool volume and myocardial mass of an in-vivo human Left-Ventricle (LV) non-invasively from multi-axis MR acquisitions. Segmented contours from both long and short-axis views are fused via a global model with parametric offsets which has a default configuration resembling a typical LV. From this model mass and volume may be computed. The results of the model fit are displayed for confirmation in a virtual reality environment which allows stereo viewing, multi-planar reformatting (MPR), and user interaction for model editing among other features.

We introduce a new solid shape model formulation that includes built-in offsets from a base global component (e.g. an ellipsoid) which are functions of the global component's parameters. The offsets provide two features. First, they help to form an expected model shape which facilitates appropriate model data correspondences. Second, they scale with the base global model to maintain the expected shape even in the presence of large global deformations. We apply this model formulation to the recovery of 3-D cardiac motion from a volunteer dataset of tagged-MR images. The model instance is a variation of the hybrid volumetric ventriculoid (HVV), a deformable thick-walled ellipsoid model resembling the left ventricle (LV) of the heart. A unique aspect of of implementation is the employment of constant volume constraints when recovering the cardiac motion. In addition, we present a novel geodesic-like prismoidal tessellation of the model which provides for more stable fits.

There is increasing interest in the recovery of generalized cylinders (GCs) with curved spines. However, existing formulations of such GCs, for example those based an the Frenet-Serret frame or the tube model, suffer serious drawbacks: discontinuities, a lack of expressive power, "narrowing" in the plane normal to the spine, non-intuitive twisting behavior, and/or off-axis nonorthogonality of their local coordinate systems. We discuss some of the problems associated with the non-orthogonality of the coordinate system based on the Frenet-Serret frame. This non-orthogonality is induced by torsion effects and we show how to correct for it. We then introduce a new model, the extruded GC (EGC) model, which overcomes all the problems mentioned above. For complex axes, the EGC model is also simpler to understand and use than existing models. The EGC model is further extended by including local surface deformations. Recovery of the deformable EGC via a physically-motivated paradigm is demonstrated on pre-segmented data from a human carotid artery.>

Periodic data is often most appropriately described with a periodic model. This allows for the subsumption of tracking into model fitting and a natural method for the interpolation of data over time. We present a deformable periodic generalized cylinder (DPGC) for simultaneously modeling and tracking closed contours which exhibit a repeating motion. Results of the recovery of the projection of a beating left ventricle (BLV) from segmented ultrasound data as well as the segmentation of a BLV cross section from series of MR image slices are presented.