Bayesian 3D X-ray computed tomography image reconstruction with a scaled Gaussian mixture prior model

Li Wang (1) , Nicolas Gac (1) , Ali Mohammad-Djafari (1)

1 L2S - Laboratoire des signaux et systèmes

In order to improve quality of 3D X-ray tomography reconstruction for Non Destructive Testing (NDT), we investigate in this paper hierarchical Bayesian methods. In NDT, useful prior information on the volume like the limited number of materials or the presence of homogeneous area can be included in the iterative reconstruction algorithms. In hierarchical Bayesian methods, not only the volume is estimated thanks to the prior model of the volume but also the hyper parameters of this prior. This additional complexity in the reconstruction methods when applied to large volumes (from 5123 to 81923 voxels) results in an increasing computational cost. To reduce it, the hierarchical Bayesian methods investigated in this paper lead to an algorithm acceleration by Variational Bayesian Approximation (VBA) [1] and hardware acceleration thanks to projection and back-projection operators paralleled on many core processors like GPU [2].In this paper, we will consider a Student-t prior on the gradient of the image implemented in a hierarchical way [3, 4, 1]. Operators H (forward or projection) and Ht (adjoint or back-projection) implanted in multi-GPU [2] have been used in this study. Different methods will be evalued on synthetic volume "Shepp and Logan" in terms of quality and time of reconstruction. We used several simple regularizations of order 1 and order 2. Other prior models also exists [5]. Sometimes for a discrete image, we can do the segmentation and reconstruction at the same time, then the reconstruction can be done with less projections.

Domaines: Traitement du signal et de l'image [eess.SP]

Li Wang, Nicolas Gac, Ali Mohammad-Djafari. Bayesian 3D X-ray computed tomography image reconstruction with a scaled Gaussian mixture prior model. 34th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt'14), Sep 2014, Amboise, France. pp.556-563, ⟨10.1063/1.4906022⟩. ⟨hal-01338706⟩