Sparsity-constrained three-dimensional image reconstruction for C-arm angiography.

X-ray C-arm is an important imaging tool in interventional radiology, road-mapping and radiation therapy because it provides accurate descriptions of vascular anatomy and therapeutic end point. In common interventional radiology, the C-arm scanner produces a set of two-dimensional (2D) X-ray projection data obtained with a detector by rotating the scanner gantry around the patient. Unlike conventional fluoroscopic imaging, three-dimensional (3D) C-arm computed tomography (CT) provides more accurate cross-sectional images, which are helpful for therapy planning, guidance and evaluation in interventional radiology. However, 3D vascular imaging using the conventional C-arm fluoroscopy encounters some geometry challenges. Inspired by the theory of compressed sensing, we developed an image reconstruction algorithm for conventional angiography C-arm scanners. The main challenge in this image reconstruction problem is the projection data limitations. We consider a small number of views acquired from a short rotation orbit with offset scan geometry. The proposed method, called sparsity-constrained angiography (SCAN), is developed using the alternating direction method of multipliers, and the results obtained from simulated and real data are encouraging. SCAN algorithm provides a framework to generate 3D vascular images using the conventional C-arm scanners in lower cost than conventional 3D imaging scanners.