Automated quantification of epicardial adipose tissue (EAT) in coronary CT angiography; comparison with manual assessment and correlation with coronary artery disease.

BACKGROUND: Epicardial adipose tissue (EAT) is emerging as a risk factor for coronary artery disease (CAD). OBJECTIVE: The aim of this study was to determine the applicability and efficiency of automated EAT quantification. METHODS: 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. RESULTS: Automated quantification was significantly less time consuming than automated quantification (17 ± 2 seconds vs 280 ± 78 seconds; P < .0001). Although manual EAT volume differed significantly from automated EAT volume (75 ± 33 cm(³) vs 95 ± 45 cm(³); P < .001), a good correlation between both assessments was found (r = 0.76; P < .001). For all methods, EAT volume was positively associated with the presence of CAD. Stronger predictive value for the severity of CAD was achieved through automated quantification on both contrast-enhanced and non-contrast-enhanced data sets. CONCLUSION: Automated EAT quantification is a quick method to estimate EAT and may serve as a predictor for CAD presence and severity.

Fully automated right ventricular volumetry from ECG-gated coronary CT angiography data: evaluation of prototype software.

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.

Detection, grading and classification of coronary stenoses in computed tomography angiography.

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.

Efficient detection of native and bypass coronary ostia in cardiac CT volumes: anatomical vs. pathological structures.

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.

Patient-specific modeling and quantification of the aortic and mitral valves from 4-D cardiac CT and TEE.

As decisions in cardiology increasingly rely on noninvasive methods, fast and precise image processing tools have become a crucial component of the analysis workflow. To the best of our knowledge, we propose the first automatic system for patient-specific modeling and quantification of the left heart valves, which operates on cardiac computed tomography (CT) and transesophageal echocardiogram (TEE) data. Robust algorithms, based on recent advances in discriminative learning, are used to estimate patient-specific parameters from sequences of volumes covering an entire cardiac cycle. A novel physiological model of the aortic and mitral valves is introduced, which captures complex morphologic, dynamic, and pathologic variations. This holistic representation is hierarchically defined on three abstraction levels: global location and rigid motion model, nonrigid landmark motion model, and comprehensive aortic-mitral model. First we compute the rough location and cardiac motion applying marginal space learning. The rapid and complex motion of the valves, represented by anatomical landmarks, is estimated using a novel trajectory spectrum learning algorithm. The obtained landmark model guides the fitting of the full physiological valve model, which is locally refined through learned boundary detectors. Measurements efficiently computed from the aortic-mitral representation support an effective morphological and functional clinical evaluation. Extensive experiments on a heterogeneous data set, cumulated to 1516 TEE volumes from 65 4-D TEE sequences and 690 cardiac CT volumes from 69 4-D CT sequences, demonstrated a speed of 4.8 seconds per volume and average accuracy of 1.45 mm with respect to expert defined ground-truth. Additional clinical validations prove the quantification precision to be in the range of inter-user variability. To the best of our knowledge this is the first time a patient-specific model of the aortic and mitral valves is automatically estimated from volumetric sequences.

New techniques in CT angiography.

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.

Standardized 3D documentation for neurosurgery.

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.