Accuracy assessment of layer decomposition using simulated angiographic image sequences

Layer decomposition is a promising method for obtaining accurate densitometric profiles of diseased coronary artery segments. This method decomposes coronary angiographic image sequences into moving densitometric layers undergoing translation, rotation, and scaling. In order to evaluate the accuracy of this technique, we have developed a technique for embedding realistic simulated moving stenotic arteries in real clinical coronary angiograms. We evaluate the accuracy of layer decomposition in two ways. First, we compute tracking errors as the distance between the true and estimated motion of a reference point in the arterial lesion. We find that noise-weighted phase correlation and layered background subtraction are superior to cross correlation and fixed mask subtraction, respectively. Second, we compute the correlation coefficient between the true vessel profile and the raw and processed images in the region of the stenosis. We find that layer decomposition significantly improves the correlation coefficient.

Disease specific intelligent pre-fetch and hanging protocol for diagnostic neuroradiology workstations

Clinical data sets for neuroradiological cases can be quite large. A typical brain tumor patient at UCLA will undergo 8-10 separate studies over a 2 year period, each study will produce 60-100 magnetic resonance (MR) images. Gathering and sorting through a patient s imaging events during the course of treatment can be both overwhelming and time consuming. The purpose of this research is to develop an intelligent pre-fetch and hanging protocol that automatically gathers the relevant prior examinations from a picture archiving, and communication systems (PACS) archive and sends the pertinent historical images to the diagnostic display station where the new examination is subsequently read out. The intelligent hanging protocol describes the type of layout and sequence for image display. We have developed a classification scheme to organize the pertinent patient information to selectively pre-fetch and intelligently present the images to review brain tumor cases for a diagnostic neuroradiology workstation.

Simulating coronary arteries in x-ray angiograms

Clinical validation of quantitative coronary angiography (QCA) algorithms is difficult due to the lack of a simple alternative method for accurately measuring in vivo vessel dimensions. We address this problem by embedding simulated coronary artery segments with known geometry in clinical angiograms. Our vessel model accounts for the profile of the vessel, x-ray attenuation in the original background, and noise in the imaging system. We have compared diameter measurements of our computer simulated arteries with measurements of an x-ray Telescopic-Shaped Phantom (XTSP) with the same diameters. The results show that for both uniform and anthropomorphic backgrounds there is good agreement in the measured diameters of XTSP compared to the simulated arteries (Pearson's correlation coefficient 0.99). In addition, the difference in accuracy and precision of the true diameter measures compared to the XTSP and simulated artery diameters was small (mean absolute error across all diameters was < or = 0.11 mm +/- 0.09 mm).