Atherosclerosis is a leading cause of mortality in industrialized countries. In addition to “traditional” systemic risk factors for atherosclerosis, the geometry and motion of coronary arteries may contribute to individual susceptibility to the development and progression of disease in these vessels. To be able to test this, we have developed a high-speed (∼40 frames per second) microscope-based stereo-imaging system to quantify the motion of epicardial coronary arteries of mice. Using near-infrared nontargeted quantum dots as an imaging contrast agent, we synchronously acquired paired images of a surgically exposed murine heart, from which the three-dimensional geometry of the coronary arteries was reconstructed. The reconstructed geometry was tracked frame by frame through the cardiac cycle to quantify the in vivo motion of the vessel, from which displacements, curvature, and torsion parameters were derived. Illustrative results for a C57BL/6J mouse are presented.
Proc. SPIE. 5373, Medical Imaging 2004: Ultrasonic Imaging and Signal Processing
KEYWORDS: Signal to noise ratio, Image segmentation, Interference (communication), Arteries, Image registration, Image quality, Intravascular ultrasound, In vivo imaging, Motion measurement, 3D image processing
Atherosclerotic lesions have been shown to have different mechanical properties than the non-diseased artery. Calculating vessel wall strain from cross-sectional vessel wall motions allows for the measurement of local stiffness. In this paper, a robust method is developed to track cross-sectional displacements of an artery wall using two different intravascular ultrasound (IVUS) images acquired at two different pressure levels respectively. First, the vessel wall region in one image is segmented semi-automatically by refining two spline-based contours to the locations of inner and outer vessel wall borders. Then the ringlike wall region in one image is registered to its counterpart in the other image in polar coordinates. The registration is performed by minimizing an energy function of the 2D motion field based on a spline-deformable-model. Both intensity and gradient information of the images are used to construct the energy function so that an accurate registration can be achieved. Registration accuracy was tested on simulated motions using IVUS images of a human coronary artery and a porcine carotid. The wall displacement fields calculated from real motion images are also demonstrated.
Most of the quantitative measures from Intravascular Ultrasound (IVUS) images vary with the cardiac cycle. Although ECG-gated acquisition can prevent the pulsations from influencing the measurements, it may extend the acquisition time, and furthermore, very few IVUS systems currently in clinical use incorporate ECG-gated function. In this paper, we present a practical method to retrieve cardiac phase information directly from in vivo clinical IVUS image sequences. In an IVUS image that contains a cross-section of coronary artery, there are three regions annularly distributed from the center of the image - catheter, lumen, and part of the vessel wall. The catheter region exhibits virtually no change from frame to frame during the catheter pullback. While the lumen is a dark region, the vessel wall region appears bright. The change in lumen size and position that accompanies the pulse causes the image intensity of the IVUS images to exhibit a periodic variation along the pullback path. By extracting this signal attributed to the cardiac cycle, a subsequence of frames during pullback at the same phase of the cardiac cycle can be selected. The method was tested by the IVUS images of both a coronary phantom and a patient.
In this paper, we present a new method to segment the walls of coronary arteries in IVUS (Intravascular Ultrasound) images based on a deformable model, which integrates both edge and region information. The whole image is supposed to have three regions - lumen, vessel wall, and adventitia plus surroundings, which are separated by two closed contours - the inner and outer boundaries. Our method has two steps: firstly, the outer vessel wall boundary is detected by minimizing an energy function of the contrast along it; secondly, by minimizing another energy function that considers the different gray level distributions of the lumen and the vessel wall, and the contrast along the edge between these two regions, the inner vessel wall is located. Dynamic programming is adopted to implement this method. Experimental results show that contrast information is a good feature for boundary detection in IVUS images.
The motion of coronary arteries has attracted more and more attention because of its possible effects on the development of atherosclerosis and potential clinical application for diagnosis of cardiovascular disease. Angiography is the best clinical modality so far to extract this information spatially and temporally. In this paper, a new method, which combines 'snakes' and a template matching technique, is proposed to track vessel segments in angiographic image sequences. By considering both global and local motion, the vessel can be tracked well in a cardiac cycle.