Preliminary attempt on maximum likelihood tomosynthesis reconstruction of DEI data

Tomosynthesis is a three-dimension reconstruction method that can remove the effect of superim- position with limited angle projections. It is especially promising in mammography where radiation dose is concerned. In this paper, we propose a maximum likelihood tomosynthesis reconstruction algorithm （ML-TS） on the apparent absorption data of diffraction enhanced imaging （DEI）. The motivation of this contribution is to develop a tomosynthesis algorithm in low-dose or noisy circumstances and make DEI get closer to clinic application. The theoretical statistical models of DEI data in physics are analyzed and the proposed algorithm is validated with the experimental data at the Beijing Synchrotron Radiation Facility （BSRF）. The results of ML-TS have better contrast compared with the well known ＇shift-and-add＇ algorithm and FBP algorithm.     

An ART iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging

X-ray diffraction enhanced imaging （DEI） has extremely high sensitivity for weakly absorbing low- Z samples in medical and biological fields. In this paper, we propose an Algebra Reconstruction Technique （ART） iterative reconstruction algorithm for computed tomography of diffraction enhanced imaging （DEI-CT）. An Ordered Subsets （OS） technique is used to accelerate the ART reconstruction. Few-view reconstruction is also studied, and a partial differential equation （PDE） type filter which has the ability of edge-preserving and denoising is used to improve the image quality and eliminate the artifacts. The proposed algorithm is validated with both the numerical simulations and the experiment at the Beijing synchrotron radiation facility （BSRF）.