Cardiac CT Angiography Images Research Articles

Calculation of left ventricular ejection fraction using an 8-layer residual U-Net with deep supervision based on cardiac CT angiography images versus echocardiography: a comparative study.

Accurate segmentation of the left ventricle (LV) is an important step in assessing cardiac function. Cardiac CT angiography (CCTA) has become an important means of clinical diagnosis of cardiovascular diseases (CVDs) because of its advantages of non-invasive, short examination time and low cost. In order to obtain the segmentation of LV in CCTA scans, we propose a deep learning method based on 8-layer residual U-Net with deep supervision. In this study we compared the left ventricular ejection fraction (LVEF) calculated by deep learning (DL) method (AccuLV) from CCTA to LVEF by conventional two-dimensional echocardiography (EC). This was a retrospective cross-sectional study, and consecutive patients who had undergone CCTA and EC in our hospital from February 2021 to May 2021 were recruited. The current study included 180 patients who had undergone CCTA and EC. To obtain LVEF, we used an 8-layer residual U-Net with deep supervision to segment LV contours from CCTA images and compute LVEF (DL-LVEF). The EC and DL-LVEF measurements were compared. A 50% EC-LVEF cut-off value was used as a reference standard to assess the diagnostic performance of AccuLV in assessing LV function. The overall mean EC-LVEF and DL-LVEF values were 64.0% (52.3%, 69.0%) and 73.0% (52.3%, 77.0%), respectively. Three patient groups were studied: (I) hypertensive patients, (II) postmenopausal women, and (III) diabetes. EC-LVEF and DL-LVEF were found to be positively correlated for all of the included patients (r=0.82, P<0.001), with the detailed results for the three groups as follows: hypertensive patients (r=0.77, P<0.001), postmenopausal women (r=0.92, P<0.001) and diabetes (r=0.88, P<0.001). The diagnostic accuracy, sensitivity, and specificity of the DL method in predicting EC-LVEF <50% for all patients were 93.9%, 92.3%, and 94.3%, and for hypertensive patients were 95.4%, 93.8%, and 95.8%, for postmenopausal women were 87.0%, 100%, and 84.2%, for diabetes were 97.4%, 100%, and 96.6%. In comparison to echocardiography, which is commonly used in clinical setting, AccuLV may be a promising, fully automated tool for rapid and accurate quantification of LV function and thus for making reliable clinical decisions.

Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images.

In this paper, we propose an accurate and rapid non-rigid registration method between blood vessels in temporal 3D cardiac computed tomography angiography images of the same patient. This method provides auxiliary information that can be utilized in the diagnosis and treatment of coronary artery diseases. The proposed method consists of the following four steps. First, global registration is conducted through rigid registration between the 3D vessel centerlines obtained from temporal 3D cardiac CT angiography images. Second, point matching between the 3D vessel centerlines in the rigid registration results is performed, and the corresponding points are defined. Third, the outliers in the matched corresponding points are removed by using various information such as thickness and gradient of the vessels. Finally, non-rigid registration is conducted for hierarchical local transformation using an energy function. The experiment results show that the average registration error of the proposed method is 0.987mm, and the average execution time is 2.137s, indicating that the registration is accurate and rapid. The proposed method that enables rapid and accurate registration by using the information on blood vessel characteristics in temporal CTA images of the same patient.

Improved Centerline Extraction in Fully Automated Coronary Ostium Localization and Centerline Extraction Framework using Deep Learning.

Coronary artery extraction in cardiac CT angiography (CCTA) image volume is a necessary step for any quantitative assessment of stenoses and atherosclerotic plaque. In this work, we propose a fully automated workflow that depends on convolutional networks to extract the centerlines of the coronary arteries from CCTA image volumes, starting from identifying the ostium points and then tracking the vessel till its end based on its radius and direction. First, a regression U-Net is employed to identify the ostium points in the image volume, then these points are fed to an orientation and radius predictor CNN model to track and extract each artery till its end point. Our results show that an average of 96% of the ostium points were identified and located within less than 5mm from their true location. The coronary arteries centerlines extraction was performed with high accuracy and lower number of training parameters making it suitable for real clinical applications and continuous learning.

Motion estimation and correction in cardiac CT angiography images using convolutional neural networks

Cardiac motion artifacts frequently reduce the interpretability of coronary computed tomography angiography (CCTA) images and potentially lead to misinterpretations or preclude the diagnosis of coronary artery disease (CAD). In this paper, a novel motion compensation approach dealing with Coronary Motion estimation by Patch Analysis in CT data (CoMPACT) is presented. First, the required data for supervised learning is generated by the Coronary Motion Forward Artifact model for CT data (CoMoFACT) which introduces simulated motion to 19 artifact-free clinical CT cases with step-and-shoot acquisition protocol. Second, convolutional neural networks (CNNs) are trained to estimate underlying 2D motion vectors from 2.5D image patches based on the coronary artifact appearance. In a phantom study with computer-simulated vessels, CNNs predict the motion direction and the motion magnitude with average test accuracies of 13.37°±1.21° and 0.77 ± 0.09 mm, respectively. On clinical data with simulated motion, average test accuracies of 34.85°±2.09° and 1.86 ± 0.11 mm are achieved, whereby the precision of the motion direction prediction increases with the motion magnitude. The trained CNNs are integrated into an iterative motion compensation pipeline which includes distance-weighted motion vector extrapolation. Alternating motion estimation and compensation in twelve clinical cases with real cardiac motion artifacts leads to significantly reduced artifact levels, especially in image data with severe artifacts. In four observer studies, mean artifact levels of 3.08 ± 0.24 without MC and 2.28 ± 0.29 with CoMPACT MC are rated in a five point Likert scale.

Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier.

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). In the proposed method, a 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN is trained using manually annotated centerlines in training images. No image preprocessing is required, so that the process is guided solely by the local image values around the tracker's location. The CNN was trained using a training set consisting of 8 CCTA images with a total of 32 manually annotated centerlines provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation was performed within the CAT08 challenge using a test set consisting of 24 CCTA test images in which 96 centerlines were extracted. The extracted centerlines had an average overlap of 93.7% with manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21 mm to reference centerline points. Based on these results the method ranks third among 25 publicly evaluated methods in CAT08. In a second test set consisting of 50 CCTA scans acquired at our institution (UMCU), an expert placed 5448 markers in the coronary arteries, along with radius measurements. Each marker was used as a seed point to extract a single centerline, which was compared to the other markers placed by the expert. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans from the MICCAI 2014 Challenge on Automatic Coronary Calcium Scoring (orCaScore), fully automatic seeding and centerline extraction was evaluated using a segment-wise analysis. This showed that the algorithm is able to fully-automatically extract on average 92% of clinically relevant coronary artery segments. Finally, the limits of agreement between reference and automatic artery radius measurements were found to be below the size of one voxel in both the CAT08 dataset and the UMCU dataset. Extraction of a centerline based on a single seed point required on average 0.4 ± 0.1s and fully automatic coronary tree extraction required around 20s. The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries based on information derived directly from the image data. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.

Assessment of radiation safety in cardiac CT angiography.

This study investigates the radiation dose and image quality of patients not receiving β-blockers for cardiac CT angiography (CCTA) with or without the optimization of electrocardiographic (ECG) pulsing window. The differences in patient characteristics are also characterized.Normal-weight and obese patients (n = 154) with heart rates between 65 and 80 beats per minutes (bpm) during the prospective axial scanning were enrolled retrospectively. The ECG pulsing windows were set at 50% to 75% (Group A) or 60% to 75% (Group B) of the R-R interval for patients with heart rate variability higher than or not exceeding ±5 bpm, respectively. The effective doses of individual patient were estimated from the dose length product of the CCTA scan. Two radiologists independently reviewed the images and applied a 4-point Likert scale for image quality assessment. The patients’ characteristics were compared along with the patients’ effective doses between groups.The optimized pulsing window significantly reduced the average radiation dose for normal-weight and obese patients by 33% and 27%, respectively. The CCTA image quality of patients in Group A was not different overall from those obtained from Group B. Nondiabetic obese patients were more likely to be accepted for the use of the optimized pulsing window. Unlike obese patients, normal-weight patients revealed no characteristic difference between Groups A and B.This study indicates an equivalent efficacy of using optimized pulsing windows for reducing the radiation dose for patients without β-blocker administration between different body weight groups. Nevertheless, gender and diabetic status became prominent characteristics in the obese group when matching up with the optimized pulsing window.

Automatic Extraction of Ascending Aorta and Ostium in Cardiac CT Angiography Images

Automatic Extraction of Ascending Aorta and Ostium in Cardiac CT Angiography Images

The effect of heart rate and vessel angulation on coronary artery stenosis evaluation from cardiac CT angiography

Introduction Cardiac CT angiography (CCTA) is considered a valuable tool for non-invasive assessment of coronary artery stenoses in patients with low to intermediate risk for coronary artery disease. Purpose To investigate the effect of heart rate and artery orientation on the accuracy of stenosis quantification from CCTA studies. Materials and methods A QRM CT cardiac phantom equipped with cardiac motion simulator was subjected to standard prospective and retrospective CCTA image acquisitions on a modern 128-slice CT scanner. CCTA image series were separately produced for three coronary artery phantoms representing stenosis of 25%, 50% and 75% with heart rate varying from 40 to 120 bpm (in 10 bpm steps) and vessel orientation varying from 0° to 40° (in 10° steps) with respect to z-axis. Stenoses were evaluated by an experienced radiologist using a dedicated cardiac CT software for coronary artery assessment and analysis. Results Stenosis quantification accuracy was found to be considerably deteriorated for increasing heart rate (p Conclusion High inaccuracies in coronary artery may occur for increased heart rate during the CCTA scan acquisition with the effect being more prominent for increased vessel angulations. To suppress stenosis evaluation inaccuracy below 15%, beam heart rate during the examination should be maintained

The frontiers of nuclear cardiology research

The frontiers of nuclear cardiology research

Segmentation of the heart and great vessels in CT images using a model-based adaptation framework

Recently, model-based methods for the automatic segmentation of the heart chambers have been proposed. An important application of these methods is the characterization of the heart function. Heart models are, however, increasingly used for interventional guidance making it necessary to also extract the attached great vessels. It is, for instance, important to extract the left atrium and the proximal part of the pulmonary veins to support guidance of ablation procedures for atrial fibrillation treatment. For cardiac resynchronization therapy, a heart model including the coronary sinus is needed. We present a heart model comprising the four heart chambers and the attached great vessels. By assigning individual linear transformations to the heart chambers and to short tubular segments building the great vessels, variable sizes of the heart chambers and bending of the vessels can be described in a consistent way. A configurable algorithmic framework that we call adaptation engine matches the heart model automatically to cardiac CT angiography images in a multi-stage process. First, the heart is detected using a Generalized Hough Transformation. Subsequently, the heart chambers are adapted. This stage uses parametric as well as deformable mesh adaptation techniques. In the final stage, segments of the large vascular structures are successively activated and adapted. To optimize the computational performance, the adaptation engine can vary the mesh resolution and freeze already adapted mesh parts. The data used for validation were independent from the data used for model-building. Ground truth segmentations were generated for 37 CT data sets reconstructed at several cardiac phases from 17 patients. Segmentation errors were assessed for anatomical sub-structures resulting in a mean surface-to-surface error ranging 0.50-0.82mm for the heart chambers and 0.60-1.32mm for the parts of the great vessels visible in the images.