Removing streak artifacts from ECG-gated reconstructions using deconvolution.

BACKGROUND: 4D cardiac computed tomography aims at reconstructing the beating heart from a series of 2D projections and the simultaneously acquired electrocardiogram. Each cardiac phase is reconstructed by exploiting the subset of projections acquired during this particular cardiac phase only. In these conditions, the Feldkamp, Davis and Kress method (FDK) generates large streak artifacts in the reconstructed volumes, hampering the medical interpretation. These artifacts can be substantially reduced by deconvolution methods. OBJECTIVE: The aim of this paper is to compare two 4D cardiac CT reconstruction methods based on deconvolution, and to evaluate their practical benefits on two applications: cardiac micro CT and human cardiac C-arm CT. METHODS: The first evaluated method builds upon inverse filtering. It has been proposed recently and demonstrated on 4D cardiac micro CT. The second one is an iterative deconvolution method, and turns out equivalent to an ECG-gated Iterative Filtered Back Projection (ECG-gated IFBP). RESULTS: Results are presented on simulated data in 2D parallel beam, 2D fan beam and 3D cone beam geometries. CONCLUSIONS: Both methods are efficient on the cardiac micro CT simulations, but insufficient to handle 4D human cardiac C-Arm CT simulations. Overall, ECG-gated IFPB largely outperforms the inverse filtering method.

Cardiac C-arm computed tomography using a 3D + time ROI reconstruction method with spatial and temporal regularization.

PURPOSE: Reconstruction of the beating heart in 3D + time in the catheter laboratory using only the available C-arm system would improve diagnosis, guidance, device sizing, and outcome control for intracardiac interventions, e.g., electrophysiology, valvular disease treatment, structural or congenital heart disease. To obtain such a reconstruction, the patient's electrocardiogram (ECG) must be recorded during the acquisition and used in the reconstruction. In this paper, the authors present a 4D reconstruction method aiming to reconstruct the heart from a single sweep 10 s acquisition. METHODS: The authors introduce the 4D RecOnstructiOn using Spatial and TEmporal Regularization (short 4D ROOSTER) method, which reconstructs all cardiac phases at once, as a 3D + time volume. The algorithm alternates between a reconstruction step based on conjugate gradient and four regularization steps: enforcing positivity, averaging along time outside a motion mask that contains the heart and vessels, 3D spatial total variation minimization, and 1D temporal total variation minimization. RESULTS: 4D ROOSTER recovers the different temporal representations of a moving Shepp and Logan phantom, and outperforms both ECG-gated simultaneous algebraic reconstruction technique and prior image constrained compressed sensing on a clinical case. It generates 3D + time reconstructions with sharp edges which can be used, for example, to estimate the patient's left ventricular ejection fraction. CONCLUSIONS: 4D ROOSTER can be applied for human cardiac C-arm CT, and potentially in other dynamic tomography areas. It can easily be adapted to other problems as regularization is decoupled from projection and back projection.

Multiresolution parametric estimation of transparent motions and denoising of fluoroscopic images.

We describe a novel multiresolution parametric framework to estimate transparent motions typically present in X-Ray exams. Assuming the presence if two transparent layers, it computes two affine velocity fields by minimizing an appropriate objective function with an incremental Gauss-Newton technique. We have designed a realistic simulation scheme of fluoroscopic image sequences to validate our method on data with ground truth and different levels of noise. An experiment on real clinical images is also reported. We then exploit this transparent-motion estimation method to denoise two layers image sequences using a motion-compensated estimation method. In accordance with theory, we show that we reach a denoising factor of 2/3 in a few iterations without bringing any local artifacts in the image sequence.