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Epicardial adipose tissue (EAT) is emerging as a risk factor for coronary artery disease (CAD).The aim of this study was to determine the applicability and efficiency of automated EAT quantification.EAT volume was assessed both manually and automatically in 157 patients undergoing coronary CT angiography. Manual assessment consisted of a short-axis-based manual measurement, whereas automated assessment on both contrast and non-contrast-enhanced data sets was achieved through novel prototype software. Duration of both quantification methods was recorded, and EAT volumes were compared with paired samples t test. Correlation of volumes was determined with intraclass correlation coefficient; agreement was tested with Bland-Altman analysis. The association between EAT and CAD was estimated with logistic regression.Automated quantification was significantly less time consuming than automated quantification (17 ? 2 seconds vs 280 ? 78 seconds; P

Although direct volume visualization is now a standard tool for diagnosis and therapy planning for medical conditions in the brain, its application is normally restricted to radiological workstations. We propose the use of standardized digital video sequences which can be easily ported to mobile computing platforms and thereby to diverse clinical environments. The effectiveness of this approach is demonstrated in the operating room.Segmented MR data corresponding to neurovascular compression syndrome pathologies was examined with 3D visualization based on tagged volumes. CT-angiography data containing aneurysms close to the skull base was analyzed with volume visualization based on bidimensional transfer functions. Furthermore, automatic adjustment of bidimensional transfer function templates was implemented. An extension of the applied volume visualization tool made it possible to standardize the creation of pathology-specific digital video sequences.Five cases of neurovascular compression syndromes and 4 cases of aneurysms close to the skull base were examined. One-dimensional transfer function templates were successfully applied for the visualization of neurovascular compression syndromes. Automatic adjustment of transfer function templates made it possible to achieve good-quality results for visualization of aneurysms without external adjustment. The resulting digital video sequences were successfully used in the operating room.The portability of the 3D video sequences broadens their application spectrum, making them adequate not only for database purposes, but also for surgical support and cooperative environments. Furthermore, the required technical knowledge is encapsulated, making this approach more suitable for clinical applications.

Aortic valve stenosis is a serious heart disease affecting a large group of elderly people. Recently minimal invasive procedures, such as the Transcatheter Aortic Valve Implantation (TAVI), are beginning to substitute conventional surgical techniques. Current methods [1] can extract basic biomarkers for TAVI such as optimal C-arm angulations, area and diameter measurements. However as the most prevalent TAVI complications (stroke and paravalvular leakages) are correlated with calcium and leaflet interactions within the valve a more advanced solution is needed. We propose a fully integrated system to extract automatically the patient specific model of the aortic valve including the volumetric model of the aortic valve leaflets and calcium from high resolution single phase CT. Based on the volumetric model advanced clinical parameters can be derived and used for e.g. patient selection, paravalvular leakage prediction and patient stroke risk assessment. We employ robust machine learning algorithms to estimate the valve model parameters. A multi-class classification method is introduced to label regions of calcium, leaflet and blood pool within the aortic valve and extract volumetric models of the aortic valve leaflets. Extensive quantitative and qualitative experiments on 198 volumetric data sets demonstrate an accurate DICE similarity score, i.e. 0.7 for the aortic valve leaflets and 0.86 for calcium tissue. Within 6 seconds a complete patient-specific model of the aortic valve can be estimated.

In this paper we present an automatic heart segmentation system for helping the diagnosis of the coronary artery diseases (CAD). The goal is to visualize the heart from a cardiac CT image with pulmonary veins, pulmonary arteries and left atrial appendage removed so that doctors can clearly see major coronary artery trees, aorta and bypass arteries if exist. The system combines model-based detection framwork with data-driven post-refinements to create voxel-based heart mask for the visualization. The marginal space learning [6] algorithm is used to detect mesh or landmark models of different heart anatomies in the CT image. Guided by such detected models, local data-driven refinements are added to produce precise boundaries of the heart mask. The system is fully automatic and can process a 3D cardiac CT volume within 5 seconds.

Enlargement and dysfunction of the right ventricle (RV) is a sign and outcome predictor of many cardiopulmonary diseases. Due to the complex geometry of the RV exact volumetry is cumbersome and time-consuming. We evaluated the performance of prototype software for fully automated RV segmentation and volumetry from cardiac CT data. In 50 retrospectively ECG-gated coronary CT angiography scans the endsystolic (RVVmin) and enddiastolic (RVVmax) volume of the right ventricle was calculated fully automatically by prototype software. Manual slice segmentation by two independent radiologists served as the reference standard. Measurement periods were compared for both methods. RV volumes calculated with the software were in strong agreement with the results from manual slice segmentation (Bland–Altman r = 0.95–0.98; p < 0.001; Lin’s correlation Rho = 0.87–0.96, p < 0.001) for RVVmax and RVVmin with excellent interobserver agreement between both radiologists (r = 0.97; p < 0.001). The measurement period was significantly shorter with the software (153 ± 9 s) than with manual slice segmentation (658 ± 211 s). The prototype software demonstrated very good performance in comparison to the reference standard. It promises robust RV volume results and minimizes postprocessing time.

Recently conducted clinical studies prove the utility of Coronary Computed Tomography Angiography (CCTA) as a viable alternative to invasive angiography for the detection of Coronary Artery Disease (CAD). This has lead to the development of several algorithms for automatic detection and grading of coronary stenoses. However, most of these methods focus on detecting calcified plaques only. A few methods that can also detect and grade non-calcified plaques require substantial user involvement. In this paper, we propose a fast and fully automatic system that is capable of detecting, grading and classifying coronary stenoses in CCTA caused by all types of plaques. We propose a four-step approach including a learning-based centerline verification step and a lumen cross-section estimation step using random regression forests. We show state-of-the-art performance of our method in experiments conducted on a set of 229 CCTA volumes. With an average processing time of 1.8 seconds per case after centerline extraction, our method is significantly faster than competing approaches.

Cardiac computed tomography (CT) is the primary noninvasive imaging modality to diagnose coronary artery disease. Though various methods have been proposed for coronary artery segmentation, most rely on at least one user click to provide a seed point for initialization. Automatic detection of the coronary ostia (where coronaries originate from the aorta), including both the native coronary ostia and graft ostia of the bypass coronaries, can make the whole coronary exam workflow fully automatic, therefore increasing a physician's throughput. Anatomical structures (native coronary ostia) and pathological structures (graft ostia) often require significantly different detection methods. The native coronary ostia are well constrained by the surrounding structures, therefore are detected as a global object. Detecting the graft ostia is far more difficult due to the large variation in graft position. A new searching strategy is proposed to efficiently guide the focus of analysis and, at the same time, reduce the false positive detections. Since the bypass coronaries are grafted on the ascending aorta surface, the ascending aorta is first segmented to constrain the search. The quantitative prior distribution of the graft ostia on the aorta surface is learned from a training set to significantly reduce the searching space further. Efficient local image features are extracted around each candidate point on the aorta surface to train a detector. The proposed method is computationally efficient, taking about 0.40 seconds to detect both native and graft ostia in a volume with around 512 x 512 x 200 voxels.

Computed tomographic (CT) angiography has been improved significantly with the introduction of four- to 64-section spiral CT scanners, which offer rapid acquisition of isotropic data sets. A variety of techniques have been proposed for postprocessing of the resulting images. The most widely used techniques are multiplanar reformation (MPR), thin-slab maximum intensity projection, and volume rendering. Sophisticated segmentation algorithms, vessel analysis tools based on a centerline approach, and automatic lumen boundary definition are emerging techniques; bone removal with thresholding or subtraction algorithms has been introduced. These techniques increasingly provide a quality of vessel analysis comparable to that achieved with intraarterial three-dimensional rotational angiography. Neurovascular applications for these various image postprocessing methods include steno-occlusive disease, dural sinus thrombosis, vascular malformations, and cerebral aneurysms. However, one should keep in mind the potential pitfalls of these techniques and always double-check the final results with source or MPR imaging.

Pericardial fat is discussed to be an independent risk factor for CAD. There is close relation between the amount of calcium in the coronary arteries and the amount of pericardial fat. Pericardial fat is correlated to traditional risk factors like BMI, hypertension, male gender and higher age. The