The introduced tool enables image fusion of CTCA and CMR data sets and allows for correct superposition of the coronary arteries derived from CTCA onto the corresponding myocardial segments derived from CMR. The method facilitates the comprehensive assessment of the functionally relevant CAD by the exact allocation of culprit coronary stenoses to corresponding myocardial defects at a low radiation dose.

3D fusion of low-dose CTCA and functional CMR is feasible and accurate, and adds, at a low radiation dose, diagnostic value for the assessment of hemodynamically relevant CAD as compared with side-by-side analysis alone. This technique can be clinically useful for the following: planning of surgical or interventional procedures in patients having a high prevalence of CAD and for improved topographic assignment of coronary stenoses to corresponding myocardial perfusion defects.

This paper describes an interactive approach to the identification of coronary arteries in 3D angiography images. The approach is based on a novel multiple hypothesis tracking methodology which is complemented with a standard minimal path search, and it allows for a complete segmentation with little manual labor. When evaluated using the 3D CT angiography data supplied with the MICCAI’08 workshop 3D Segmentation in the Clinic: A Grand Challenge II, 98% of the target coronary arteries could be segmented in about 5 minutes per data set with the same spatial accuracy achieved in manual segmentations by human experts.

No abstract

The analysis of myocardial tissue with contrast-enhanced MR yields multiple parameters, which can be used to classify the examined tissue. Perfusion images are often distorted by motion, while late enhancement images are acquired with a different size and resolution. Therefore, it is common to reduce the analysis to a visual inspection, or to the examination of parameters related to the 17-segment-model proposed by the American Heart Association (AHA). As this simplification comes along with a considerable loss of information, our purpose is to provide methods for a more accurate analysis regarding topological and functional tissue features. In order to achieve this, we implemented registration methods for the motion correction of the perfusion sequence and the matching of the late enhancement information onto the perfusion image and vice versa. For the motion corrected perfusion sequence, vector images containing the voxel enhancement curves' semi-quantitative parameters are derived. The resulting vector images are combined with the late enhancement information and form the basis for the tissue examination. For the exploration of data we propose different modes: the inspection of the enhancement curves and parameter distribution in areas automatically segmented using the late enhancement information, the inspection of regions segmented in parameter space by user defined threshold intervals and the topological comparison of regions segmented with different settings. Results showed a more accurate detection of distorted regions in comparison to the AHA-model-based evaluation.