From Event: SPIE Optical Engineering + Applications, 2016
Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current
(DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the
zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior
information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper,
we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order
image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of
the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is
symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from
the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in
terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography.
Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%,
corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed
images with the constraint of the estimated DC value exhibit greatly superior image quality to that
without DC value constraint.
Yongyi Shi and Xuanqin Mou, "Dictionary learning based statistical interior reconstruction without a prior knowledge," Proc. SPIE 9967, Developments in X-Ray Tomography X, 99671N (Presented at SPIE Optical Engineering + Applications: September 01, 2016; Published: 4 October 2016); https://doi.org/10.1117/12.2236451.
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