Abstract

It remains of theoretical and practical interests in image reconstruction from data collected over limited-angular ranges (LARs) in tomographic imaging such as X-ray computed tomography (CT) imaging. The problem of image reconstruction from LAR data is well-recognized to be challenging because it is generally highly ill-conditioned. In the talk, I will discuss some of our recent work on optimization-based image reconstruction from LAR data in standard single-energy CT (SECT), which involves the inversion of a linear data model relating data and image of interest. Another topic of active research in the CT-imaging area is the algorithm development for image reconstruction in multi-energy CT (MECT) (also referred to as multi-spectral CT) in which multiple sets of data are collected with multiple effective scan-system spectra. Dual-energy CT (DECT), a special case of MECT , acquires two data sets with two distinct spectra. In MECT /DECT, a data model nonlinearly relates data and image of interest, thus leading to a reconstruction problem distinctively different from that in SECT . While image reconstruction from full-angular-range data has been studied extensively in DECT , there remains limited effort in developing image reconstruction from LAR data in MECT /DECT. As such, I will also discuss some of our recent work on image reconstruction from LAR data in MECT /DECT.