Three-dimensional (3D) reconstruction of the coronary arteries offers great advantages in the diagnosis and treatment of cardiovascular diseases, compared to two-dimensional X-ray angiograms. Besides improved roadmapping, quantitative analysis of vessel lesions is possible. To perform 3D reconstruction, rotational projection data of the selectively contrast agent enhanced coronary arteries are acquired with simultaneous ECG recording. For the reconstruction of one cardiac phase, the corresponding projections are selected from the rotational sequence by nearest-neighbor ECG gating. This typically provides only 5-10 projections per cardiac phase. The severe angular undersampling leads to an ill-posed reconstruction problem.
In this contribution, an iterative reconstruction method is presented which employs regularizations especially suited for the given reconstruction problem. The coronary arteries cover only a small fraction of the reconstruction volume. Therefore, we formulate the reconstruction problem as a minimization of the L1-norm of the reconstructed image, which results in a spatially sparse object. Two additional regularization terms are introduced: a 3D vesselness prior, which is reconstructed from vesselness-filtered projection data, and a Gibbs smoothing prior. The regularizations favor the reconstruction of the desired object, while taking care not to over-constrain the reconstruction by too detailed a-priori assumptions. Simulated projection data of a coronary artery software phantom are used to evaluate the performance of the method. Human data of clinical cases are presented to show the method's potential for clinical application.