Evaluation of Iterative Sparse Object Reconstruction From Few Projections for 3-D Rotational Coronary Angiography

Abstract

A 3-D reconstruction of the coronary arteries offers great advantages in the diagnosis and treatment of cardiovascular disease, compared to 2-D X-ray angiograms. Besides improved roadmapping, quantitative vessel analysis is possible. Due to the heart's motion, rotational coronary angiography typically provides only 5-10 projections for the reconstruction of each cardiac phase, which leads to a strongly undersampled reconstruction problem. Such an ill-posed problem can be approached with regularized iterative methods. The coronary arteries cover only a small fraction of the reconstruction volume. Therefore, the minimization of the mbiL(1) norm of the reconstructed image, favoring spatially sparse images, is a suitable regularization. Additional problems are overlaid background structures and projection truncation, which can be alleviated by background reduction using a morphological top-hat filter. This paper quantitatively evaluates image reconstruction based on these ideas on software phantom data, in terms of reconstructed absorption coefficients and vessel radii. Results for different algorithms and different input data sets are compared. First results for electrocardiogram-gated reconstruction from clinical catheter-based rotational X-ray coronary angiography are presented. Excellent 3-D image quality can be achieved.

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