Compressed Sensing Based 3D Tomographic Reconstruction for Rotational Angiography

International audience; In this paper, we address three-dimensional tomographic re-construction of rotational angiography acquisitions. In clinical routine, angular subsampling commonly occurs, due to the technical limitations of C-arm systems or possible improper injection. Standard methods such as ltered backprojection yield a reconstruction that is deteriorated by subsampling artifacts, which potentially hampers medical interpretation. Recent developments of compressed sensing have demonstrated that it is possible to signi cantly improve reconstruction of subsampled datasets by generating sparse approximations through '1-penalized minimization. Based on these results, we present an extension of the iterative ltered backprojection that includes a sparsity constraint called soft background subtraction. This approach is shown to provide subsampling artifact reduction when reconstructing sparse objects, and more interestingly, when reconstructing sparse objects over a non-sparse background. The relevance of our approach is evaluated in cone beam geometry on real clinical data.