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

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

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

Data reconciliation of immersive heart inspection

IVUS images are complicated medical datasets suffering from some artifacts caused by the data acquisition method of immersive heart inspection. Data reconciliation, which removes tracing and tracking uncertainties of these datasets, is an important step for the medical application of remodeling the arteries in virtual reality to aid diagnosing and treating heart diseases. This paper provides an empirical data reconciliation method, which fuses the features of the coronary longitudinal movement with motion compensation model. It explains the distortion of the data set well and provides a method to analyze and reconcile the dataset

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

Three-Dimensional Motion Tracking of Coronary Arteries in Biplane Cineangiogram

International audienceA three-dimensional (3-D) method for tracking the coronary arteries through a temporal sequence of biplane X-ray angiography images is presented. A 3-D centerline model of the coronary vasculature is reconstructed from a biplane image pair at one time frame, and its motion is tracked using a coarse-to-fine hierarchy of motion models. Three-dimensional constraints on the length of the arteries and on the spatial regularity of the motion field are used to overcome limitations of classical two-dimensional vessel tracking methods, such as tracking vessels through projective occlusions. This algorithm was clinically validated in five patients by tracking the motion of the left coronary tree over one cardiac cycle. The root mean square reprojection errors were found to be submillimeter in 93% (54/58) of the image pairs. The performance of the tracking algorithm was quantified in three dimensions using a deforming vascular phantom. RMS 3-D distance errors were computed between centerline models tracked in the X-ray images and gold-standard centerline models of the phantom generated from a gated 3-D magnetic resonance image acquisition. The mean error was 0.69( 0.06) mm over eight temporal phases and four different biplane orientations

3D reconstruction of coronary artery using biplane angiography

In this paper, we present a new method for the 3D reconstruction and visualization of coronary arteries in biplane angiography. The proposed method performs direct reconstruction of 3D coronary artery pathways without computing the 2D or 3D vessel centerlines. A front propagation algorithm is used to reconstruct the coronary artery pathways in 3D space. Starting from one or more 3D points, the front is expanded with a propagation speed controlled by the combined image information from two 2D projections. Then the reconstructed 3D coronary artery pathways are smoothed to reflect the real situation of a smoothed vessel surface. As shown in the experiment result, the coronary artery can be successfully reconstructed from two projections of biplane angiograms.published_or_final_versio

Temporal tracking of 3D coronary arteries in projection angiograms

International audienceA method for 3D temporal tracking of a 3D coronary tree model through a sequence of biplane cineangiography images has been developed. A registration framework is formulated in which the coronary tree centerline model deforms in an external potential ¯eld de¯ned by a multiscale analysis response map computed from the angiogram images. To constrain the procedure and to improve convergence, a set of three motion models is hierarchically used: a 3D rigid-body transformation, a 3D a±ne transformation, and a 3D B-spline deformation ¯eld. This 3D motion tracking approach has signi¯cant advantages over 2D methods: (1) coherent deformation of a single 3D coronary reconstruction preserves the topology of the arterial tree; (2) constraints on arterial length and regularity, which lack meaning in 2D projection space, are directly applicable in 3D; and (3) tracking arterial segments through occlusions and crossings in the projection images is simpli¯ed with knowledge of the 3D relationship of the arteries. The method has been applied to patient data and results are presented

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