Daniel Rinck

Dual-source computed tomography: advances of improved temporal resolution in coronary plaque imaging.

Objectives:The aim of this study was to quantify image quality gains of a moving coronary plaque phantom using dual-source computed tomography (DSCT) providing 83 milliseconds temporal resolution in direct comparison to 64 slice single-source multidetector CT (MDCT) with a temporal resolution of 165 milliseconds. Materials and Methods:Three cardiac vessel phantoms with fixed 50% stenosis and changing plaque configurations were mounted on a moving device simulating cardiac motion. Scans were performed at a simulated heart frequency of 60 to 120 bpm. Image quality assessment was performed in different anatomic orientations inside a thoracic phantom. Results:A significant improvement of image quality using the DSCT could be found (P = 0.0002). Relevant factors influencing image quality aside from frequency (P = 0.0002) are plaque composition (P < 0.0001), as well as orientation (P < 0.0001). Conclusion:Scanning with 83 milliseconds temporal resolution improved image quality of coronary plaque at higher heart frequencies.

Automatic heart isolation for CT coronary visualization using graph-cuts

We describe a means to automatically and efficiently isolate the outer surface of the entire heart in computer tomography (CT) cardiac scans. Isolating the entire heart allows the coronary vessels on the surface of the heart to be easily visualized despite the proximity of surrounding organs such as the ribs and pulmonary blood vessels. Numerous techniques have been described for segmenting the left ventricle of the heart in images from various types of medical scanners but rarely has the entire heart been segmented. We make use of graph-cuts to do the segmentation and introduce a novel means of initiating and constraining the graph-cut technique for heart isolation. The technique has been tested on 70 patient data sets. Results are compares with hand labeled results

Automatic segmentation of the left ventricle and computation of diagnostic parameters using regiongrowing and a statistical model

The manual segmentation and analysis of high-resolution multi-slice cardiac CT datasets is both labor intensive and time consuming. Therefore it is necessary to supply the cardiologist with powerful software tools to segment the myocardium and compute the relevant diagnostic parameters. In this work we present a semi-automatic cardiac segmentation approach with minimal user interaction. It is based on a combination of an adaptive slice-based regiongrowing and a modified Active Shape Model (ASM). Starting with a single manual click point in the ascending aorta, the aorta, the left atrium and the left ventricle get segmented with the slice-based adaptive regiongrowing. The approximate position of the aortic and mitral valve as well as the principal axes of the left ventricle (LV) are determined. To prevent the regiongrowing from draining into neighboring anatomical structures via CT artifacts, we implemented a draining control by examining a cubic region around the currently processed voxel. Additionally, we use moment-based parameters to integrate simple anatomical knowledge into the regiongrowing process. Using the results of the preceding regiongrowing process, a ventricle-centric and normalized coordinate system is established which is used to adapt a previously trained ASM to the image, using an iterative multi-resolution approach. After fitting the ASM to the image, we can use the generated model-points to create an exact surface model of the left ventricular myocardium for visualization and for computing the diagnostically relevant parameters, like the ventricular blood volume and the myocardial wall thickness.

Shape-based segmentation and visualization techniques for evaluation of atherosclerotic plaques in coronary artery disease

Multi-slice computed tomography (MSCT) has developed strongly in the emerging field of cardiovascular imaging. The manual analysis of atherosclerotic plaques in coronary arteries is a very time consuming and labor intensive process and today only qualitative analysis is possible. In this paper we present a new shape-based segmentation and visualization technique for quantitative analysis of atherosclerotic plaques in coronary artery disease. The new technique takes into account several aspects of the vascular anatomy. It uses two surface representations, one for the contrast filled vessel lumen and also one for the vascular wall. The deviation between these two surfaces is defined as plaque volume. These surface representations can be edited by the user manually. With this kind of representation it is possible to calculate sub plaque volumes (such as: lipid rich core, fibrous tissue, calcified tissue) inside this suspicious area. Also a high quality 3D visualization, using Open Inventor is possible.

Segmentation of the left and right cardiac ventricle using a combined bi-temporal statistical model

The manual segmentation and analysis of high-resolution multislice cardiac CT datasets is both labor intensive and time consuming. Therefore it is necessary to supply the cardiologist with powerful software tools to segment the myocardium as well as the cardiac cavities and to compute the relevant diagnostic parameters. In this paper we present an automatic cardiac segmentation procedure with minimal user interaction. It is based on a combined bi-temporal statistical model of the left and right ventricle using the principal component analysis (PCA) as well as the independent component analysis (ICA) to model global and local shape variation. To train the model we used manually drawn end-diastolic as well as end-systolic contours of the right epi- and of the left and right endocardium to create triangular surfaces of training datasets. These surfaces were used to build a mean triangular surface model of the left and right ventricle for the end-diastolic and end-systolic heart phase and to compute the PCA and ICA decorrelation matrices which are used in a point distribution model (PDM) to model the global and local shape variations. In contrast to many previous attempts of model based cardiac segmentation we do not create separate models for the left and the right ventricle and for different heart phases, but instead create one single parameter vector containing the information of both ventricles and both heart phases. This enables us to use the correlation between the phases and between left and right side to create a model which is more robust and less sensitive e.g. to poor contrast at the right ventricle.

Anatomical-based segmentation with stenosis bridging and gap closing in atherosclerotic cardiac MSCT

In the diagnosis of coronary artery disease, 3D-multi-slice computed tomography (MSCT) has recently become more and more important. In this work, an anatomical-based method for the segmentation of atherosclerotic coronary arteries in MSCT is presented. This technique is able to bridge severe stenosis, image artifacts or even full vessel occlusions. Different anatomical structures (aorta, blood-pool of the heart chambers, coronary arteries and their orifices) are detected successively to incorporate anatomical knowledge into the algorithm. The coronary arteries are segmented by a simulated wave propagation method to be able to extract anatomically spatial relations from the result. In order to bridge segmentation breaks caused by stenosis or image artifacts, the spatial location, its anatomical relation and vessel curvature-propagation are taken into account to span a dynamic search space for vessel bridging and gap closing. This allows the prevention of vessel misidentifications and improves segmentation results significantly. The robustness of this method is proven on representative medical data sets.

Comprehensive Evaluation of an Image Segmentation Technique for Measuring Tumor Volume from CT Images

Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation. In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200 tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can provide complementary information regarding the quality of the segmentation results. The reproducibility was measured by the variation of the volume measurements from 10 independent segmentations. The effect of disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale evaluation of segmentation techniques for other clinical applications.