Browsing by Author "Restrepo, Carlos"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Compressive Spectral X-Ray CT Reconstruction via Deep Learning(IEEE Transactions on Computational Imaging, 2022-10-20) Zhang, Tong; Zhao, Shengjie; Ma, Xu; Restrepo, Carlos; Arce, Gonzalo R.Compressive spectral X-ray imaging (CSXI) uses coded illumination projections to reconstruct tomographic images at multiple energy bins. Different K-edge materials are used to modulate the spectrum of the X-ray source at various angles, thereby capturing the projections containing spectral attenuation information. It is a cost-effective and low-dose sensing approach; however, the image reconstruction is a nonlinear and ill-posed problem. Current methods of solving the inverse problem are computationally expensive and require extensive iterations. This paper proposes a deep learning model consisting of a set of convolutional neural networks to reconstruct the CSXI spectral images, which correspond to inpainting the subsampled sinograms, recovering the monoenergetic sinograms, and removing the artifacts from a fast but low-quality analytical reconstruction. Numerical experiments show that the proposed method significantly improves the quality of reconstructed image compared with that attained by the state-of-the-art reconstruction methods. Moreover, it significantly reduces the time-required for CSXI reconstruction.Item Design of structured illumination via multi-objective optimization in dynamic X-ray tomosynthesis(University of Delaware, 2023) Restrepo, CarlosDynamic coded X-ray tomosynthesis (CXT) uses a set of encoded X-ray sources to interrogate objects lying on a moving conveyor mechanism. The sample is reconstructed from encoded measurements received by uniform linear array detectors. This work introduces a multi-objective optimization (MO) method for structured illuminations, balancing reconstruction quality and radiation dose in dynamic CXT systems. The MO framework is established based on a dynamic sensing geometry with binary coding masks. The Strength Pareto Evolutionary Algorithm 2 (SPEA-2) is used to solve the MO problem by jointly optimizing the coding masks, locations of X-ray sources, and exposure moments. Computational experiments are implemented to assess the proposed MO strategy. Additionally, an analysis based on singular value decomposition was carried out to examine the condition number of the resulting sampling matrices. To ensure that the reconstruction framework does not have dependencies on a particular sample movement, two different movement sequences were employed. The results show that the proposed strategy can obtain a set of Pareto optimal solutions with different levels of radiation dose and better reconstruction quality than benchmark settings under diverse sampling conditions.