Three-dimensional reconstruction of coronary arteries can be performed during x-ray-guided interventions by gated reconstruction from a rotational coronary angiography sequence. Due to imperfect gating and cardiac or breathing motion, the heart's motion state might not be the same in all projections used for the reconstruction of one cardiac phase. The motion state inconsistency causes motion artefacts and degrades the reconstruction quality. These effects can be reduced by a projection-based 2D motion compensation method. Using maximum-intensity forward projections of an initial uncompensated reconstruction as reference, the projection data are transformed elastically to improve the consistency with respect to the heart's motion state. A fast iterative closest-point algorithm working on vessel centrelines is employed for estimating the optimum transformation. Motion compensation is carried out prior to and independently from a final reconstruction. The motion compensation improves the accuracy of reconstructed vessel radii and the image contrast in a software phantom study. Reconstructions of human clinical cases are presented, in which the motion compensation substantially reduces motion blur and improves contrast and visibility of the coronary arteries.
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.
Circular cone-beam computed tomography (CBCT) with a tangentially offset flat-panel X-ray detector offers a large CT field-of-view (FoV) with a relatively small detector. It is used in practice, e.g., for target imaging in image-guided radiotherapy or for localization and attenuation correction in SPECT/CT imaging. The X-ray projections, acquired on a circular source trajectory, each cover roughly half the CT FoV; a central overlap region is imaged by all projections. Offset-detector CBCT reconstruction requires special algorithms. For large detector offsets, previously proposed filtered-backprojection methods can lead to shading artifacts, specifically left/right intensity imbalance. Here, we propose using iterative reconstruction for offset-detector CBCT. To handle the special acquisition geometry, known iterative reconstruction algorithms are modified in terms of axial truncation compensation, redundancy weighting, and algorithm initialization. An efficient implementation using a graphics processing unit (GPU) delivers clinically feasible reconstruction times. Results from patient and phantom studies are presented, showing a clear reduction of artifacts and improvement in image quality.
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