Compressed sensing subtracted rotational angiography with multiple sparse penalty

International audienceDigital Subtraction Rotational Angiography (DSRA) is a clinical protocol that allows three-dimensional (3D) visualization of vasculature during minimally invasive procedures. C-arm systems that are used to generate 3D reconstructions in interventional radiology have limited sampling rate and thus, contrast resolution. To address this particular subsampling problem, we propose a novel iterative reconstruction algorithm based on compressed sensing. To this purpose, we exploit both spatial and temporal sparsity of DSRA. For computational efficiency, we use a proximal implementation that accommodates multiple '1-penalties. Experiments on both simulated and clinical data confirm the relevance of our strategy for reducing subsampling streak artifacts

Fezzân

Fezzân et Phazania (P. Trousset) Bien que le nom actuel du Fezzân qui désigne une des trois régions de la Libye contemporaine tire son origine de celui de la Phazania antique, les deux toponymes ne s’appliquent pas en réalité à la même aire géographique et ce glissement spatial depuis l’Antiquité doit être attribué aux géographes arabes. Pour Ibn Hawkal et pour Al Idrisi, par exemple, le Fāzāz (Fezzân) « où sont les villes de Djarma et de Tasawa », est bien à l’emplacement que nous lui connai..

EmboASSIST a new software to help endovascular treatment of brain AVMs

International audienceEmboASSIST (GE Healthcare) is a new 3D visualization software dedicated to assist AVM embolization. It provides a one-click 3D segmentation of vascular anatomy from CBCT acquisition then dynamically track feeders and simulate virtual injections. These segmented feeders can be displayed on live fluoroscopy facilitating micro catheter navigation. Moreover, 3D MR acquisition may be automatically registered with CBCT acquisition and also displayed on live fluoroscopy

Sparsity constraints and dedicated acquisition protocols for improved Digital Subtraction Rotational Angiography

Digital Subtraction Rotational Angiography (DSRA) allows reconstruction of three-dimensional vascular structures from two spins: the contrast is acquired after injecting vessels with a contrast medium, whereas the mask is acquired in the absence of injection. The vessels are then detected by subtraction of the mask from the contrast. Standard DSRA protocol samples the same set of equiangular-spaced positions for both spins. Due to technical limitations of C-arm systems, streak artifacts degrade the quality of all three reconstructed volumes. Recent developments of compressed sensing have demonstrated that it is possible to recover a signal that is sparse in some basis under limited sampling conditions. In this paper, we propose to improve the reconstruction quality of non-sparse volumes when there exists a sparse combination of these volumes. To this purpose, we develop an extension of iterative filtered backprojection that jointly reconstructs the mask and contrast volumes via '1-minimization of sparse priors. A dedicated protocol based upon interleaving both spins is shown to further benefit from the sparsity assumptions, while using the same total number of measurements. Our approach is evaluated in parallel geometry on simulated phantom data

Sparsity constraints and dedicated acquisition protocols for improved Digital Subtraction Rotational Angiography

Digital Subtraction Rotational Angiography (DSRA) allows reconstruction of three-dimensional vascular structures from two spins: the contrast is acquired after injecting vessels with a contrast medium, whereas the mask is acquired in the absence of injection. The vessels are then detected by subtraction of the mask from the contrast. Standard DSRA protocol samples the same set of equiangular-spaced positions for both spins. Due to technical limitations of C-arm systems, streak artifacts degrade the quality of all three reconstructed volumes. Recent developments of compressed sensing have demonstrated that it is possible to recover a signal that is sparse in some basis under limited sampling conditions. In this paper, we propose to improve the reconstruction quality of non-sparse volumes when there exists a sparse combination of these volumes. To this purpose, we develop an extension of iterative filtered backprojection that jointly reconstructs the mask and contrast volumes via '1-minimization of sparse priors. A dedicated protocol based upon interleaving both spins is shown to further benefit from the sparsity assumptions, while using the same total number of measurements. Our approach is evaluated in parallel geometry on simulated phantom data

Compressed Sensing Based 3D Tomographic Reconstruction for Rotational Angiography

International audienceIn 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