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.

Reconstruction of coronary arteries from X-ray angiography: A review

“…Since it is not possible to embed prior information into analytical reconstruction algorithms, iterative reconstruction algorithms with some kind of sparsity prior have been proposed. In (Li et al, 2004;Hansis et al, 2008c;Liu et al, 2014a), the forward projection equations are used as constraints and L1 norm of the reconstruction is minimized. Similarly, (Wu et al, 2011) minimized total variation (TV) norm of the reconstruction instead of L1 norm.…”

“…Instead of directly solving the forward projection equations, L1 norm of the reconstruction is minimized with the forward project equations used as constraints (Li et al, 2002(Li et al, , 2004Hansis et al, 2008c). By introducing a quadratic perturbation term, the minimization problem can be approximated and efficiently solved via an iterative scheme, which is akin to the conventional ART (Li et al, 2004).…”

“…By introducing a quadratic perturbation term, the minimization problem can be approximated and efficiently solved via an iterative scheme, which is akin to the conventional ART (Li et al, 2004). Intuitively, nonzero constraint of original ART is relaxed and the solution space is enriched by addition of new subspaces (Hansis et al, 2008c). This effect is mainly due to a voxelwise thresholding in the new formulation.…”

“…Therefore, the algorithm is called thresholded ART (TART). Hansis et al (Hansis et al, 2008c) proposed simultaneous TART (START) technique following the similar changes to convert ART to SART. Recently, Liu et al (Liu et al, 2014a) combined START with a novel background removal technique (Section 4.3.6).…”

Total Variation Regularization Method for 3D Rotational Coronary Angiography

“…3D image information offers great advantage for quantitative analysis of vessel properties. Moreover, the successive 3D reconstructions can be used to determine the temporal dynamics of the arteries [1].…”