Name
Title | ||
|---|---|---|
A new model-based technique for enhanced small-vessel measurements in X-ray ciné-angiograms. |
Abstract | ||
|---|---|---|
Arterial diameter estimation from X-ray ciné angiograms is important for quantifying coronary artery disease (CAD) and for evaluating therapy. However, diameter measurement in vessel cross sections < or =1.0 mm is associated with large measurement errors. We present a novel diameter estimator which reduces both magnitude and variability of measurement error. We use a parametric nonlinear imaging model for X-ray ciné angiography and estimate unknown model parameters directly from the image data. Our technique allows us to exploit additional diameter information contained within the intensity profile amplitude, a feature which is overlooked by existing methods. This method uses a two-step procedure: the first step estimates the imaging model parameters directly from the angiographic frame and the second step uses these measurements to estimate the diameter of vessels in the same image. In Monte-Carlo simulation over a range of imaging conditions, our approach consistently produced lower estimation error and variability than conventional methods. With actual X-ray images, our estimator is also better than existing methods for the diameters examined (0.4-4.0 mm). These improvements are most significant in the range of narrow vessel widths associated with severe coronary artery disease. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/42.845182 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
measurement errors,atherosclerosis,0.4 to 4.0 mm,x-ray cine-angiograms,monte carlo methods,quantitative coronary angiography,intensity profile amplitude,diameter measurement,medical diagnostic imaging,enhanced small-vessel measurements,x-ray angiographic imaging,physiological models,therapy evaluation,coronary artery disease quantification,model-based technique,small-diameter vessels,angiocardiography,medical image processing,model-based estimation | Biomedical engineering,Artificial intelligence,Amplitude,Angiography,CAD,Computer vision,Monte Carlo method,Parametric statistics,Radiology,Observational error,Mathematics,Angiocardiography,Estimator | Journal |
Volume | Issue | ISSN |
19 | 3 | 0278-0062 |
Citations | PageRank | References |
13 | 1.20 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raymond C Chan | 1 | 52 | 7.35 |
| William C. Karl | 2 | 196 | 20.88 |
| Robert S. Lees | 3 | 13 | 1.20 |