ML reconstruction of cone-beam projections acquired by a fiat-panel rotational X-ray device

摘要

The steadily growing computational power of modern hardware allows use of more sophisticated reconstruction methods. We present an implementation of the maximum likelihood (ML) method, a previously studied1 method, for the case of a flat-panel rotational X-ray device. Contrary to the related principle of algebraic reconstruction (ART), the ML method takes into consideration the physical properties of X-radiation, especially the corpuscular character and the associated Poisson distribution of the measured number of photons. The basic principle is the maximization of the joint probability of all measured projections with respect to the attenuation coefficients of all voxels of the object. The application of the ML optimization procedure finally generates an iterative scheme for the update of the attenuation coefficients. For this, in each step an accurate estimation of the forward projections (FP) is mandatory. We use an approximate calculation of the footprints of single voxels based on separable trapezoids. The resulting enormous computational effort is handled by an efficient implementation on GPGPU (General-purpose computing on graphics processing units). As a first look, using data from 133 projections of a sheep head acquired by means of a flat-panel rotational angiography system, we compare the reconstruction by the ML-based method with the gold standard - the Feldkamp filtered back projection (FBP) procedure. The results reveal a clearly reduced amount of streak artifacts as well as less blurring in the statistical reconstruction method.