Automatic 3D Reconstruction of Coronary Artery Centerlines from Monoplane X-ray Angiogram Images

We present a new method for the fully automatic 3D reconstruction of the coronary artery centerlines, using two X-ray angiogram projection images from a single rotating monoplane acquisition system. During the first stage, the input images are smoothed using curve evolution techniques. Next, a simple yet efficient multiscale method, based on the information of the Hessian matrix, for the enhancement of the vascular structure is introduced. Hysteresis thresholding using different image quantiles, is used to threshold the arteries. This stage is followed by a thinning procedure to extract the centerlines. The resulting skeleton image is then pruned using morphological and pattern recognition techniques to remove non-vessel like structures. Finally, edge-based stereo correspondence is solved using a parallel evolutionary optimization method based on f symbiosis. The detected 2D centerlines combined with disparity map information allow the reconstruction of the 3D vessel centerlines. The proposed method has been evaluated on patient data sets for evaluation purposes

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated

Neural network-based coronary dominance classification of RCA angiograms

Background. Cardiac dominance classification is essential for SYNTAX score estimation, which is a tool used to determine the complexity of coronary artery disease and guide patient selection toward optimal revascularization strategy. Objectives. Cardiac dominance classification algorithm based on the analysis of right coronary artery (RCA) angiograms using neural network Method. We employed convolutional neural network ConvNext and Swin transformer for 2D image (frames) classification, along with a majority vote for cardio angiographic view classification. An auxiliary network was also used to detect irrelevant images which were then excluded from the data set. Our data set consisted of 828 angiographic studies, 192 of them being patients with left dominance. Results. 5-fold cross validation gave the following dominance classification metrics (p=95%): macro recall=93.1%, accuracy=93.5%, macro F1=89.2%. The most common case in which the model regularly failed was RCA occlusion, as it requires utilization of LCA information. Another cause for false prediction is a small diameter combined with poor quality cardio angiographic view. In such cases, cardiac dominance classification can be complex and may require discussion among specialists to reach an accurate conclusion. Conclusion. The use of machine learning approaches to classify cardiac dominance based on RCA alone has been shown to be successful with satisfactory accuracy. However, for higher accuracy, it is necessary to utilize LCA information in the case of an occluded RCA and detect cases where there is high uncertainty

Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

Reconstruction of coronary arteries from X-ray angiography: A review.

Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research

Four-dimensional imaging of thoracic target volumes in conformal radiotherapy

The goal of conformal radiotherapy (CRT) is to deliver the prescribed dose to a volume that closely conforms to the three-dimensional (3D) target volume while the dose to adjacent healthy tissues or organs at risk is minimized. Because the position of the target volume can change substantially both within and between radiation treatment fractions the fourth dimension, namely time, needs to be addressed as well. The consideration of time in the 3D treatment process is referred to as fourdimensional (4D) radiotherapy. Variations in the target volume position with time are mainly due to organ motion and patient and beam set-up deviations. Changes in the target volume position that occur within a treatment fraction are referred to as intra-fraction variation. Respiratory and cardiac motion are the main contributors to intra-fraction positional variations of thoracic and abdominal target volumes. In routine clinical practice thoracic and abdominal tumors are irradiated while the patient breathes freely. To account for target volume variations in size, shape and position and patient and beam set-up deviations, an empirical 3D margin is added to the clinical target volume to obtain the planning target volume (1, 2). The 3D margin is often derived from respiratory motion measurements in patients representative of the general population. Such a margin is not tailored to the individual patient and will therefore be suboptimal in most cases. Alternatively, the tumor motion in a specific patient can be determined as part of the treatment planning procedure. Fluoroscopy is most widely used for this purpose. However, tumors are often poorly visualized using this imaging modality. In addition, fluoroscopic data cannot directly be related to the treatment planning computed tomography (CT) data

The computation of blood flow waveforms from digital X-ray angiographic data

This thesis investigates a novel technique for the quantitative measurement of pulsatile blood flow waveforms and mean blood flow rates using digital X-ray angiographic data. Blood flow waveforms were determined following an intra-arterial injection of contrast material. Instantaneous blood velocities were estimated by generating a 'parametric image' from dynamic X-ray angiographic images in which the image grey-level represented contrast material concentration as a function of time and true distance in three dimensions along a vessel segment. Adjacent concentration-distance profiles in the parametric image of iodine concentration versus distance and time were shifted along the vessel axis until a match occurred. A match was defined as the point where the mean sum of the squares of the differences between the two profiles was a minimum. The distance translated per frame interval gave the instantaneous contrast material bolus velocity. The technique initially was validated using synthetic data from a computer simulation of angiographic data which included the effect of pulsatile blood flow and X-ray quantum noise. The data were generated for a range of vessels from 2 mm to 6 mm in diameter. Different injection techniques and their effects on the accuracy of blood flow measurements were studied. Validation of the technique was performed using an experimental phantom of blood circulation, consisting of a pump, flexible plastic tubing, the tubular probe of an electromagnetic flowmeter and a solenoid to simulate a pulsatile flow waveform which included reverse flow. The technique was validated for both two- and three-dimensional representations of the blood vessel, for various flow rates and calibre sizes. The effects of various physical factors were studied, including the distance between injection and imaging sites and the length of artery analysed. Finally, this method was applied to clinical data from femoral arteries and arteries in the head and neck