Affiliations: Biomedical Imaging Division, VT-WFU School of
Biomedical Engineering and Sciences, Wake Forest University,
Winston-Salem, NC, USA | Department of Biomedical Engineering, Division of
Radiologic Sciences, Wake Forest University, Winston-Salem, NC, USA | Institute of Image Processing and Pattern Recognition,
Xi'an Jiaotong University, Xi'an, Shaanxi, China
Note: [] Corresponding author: Hengyong Yu, Biomedical Imaging Division,
VT-WFU School of Biomedical Engineering and Sciences, Wake Forest University
Health Sciences, Winston-Salem, NC, 27157, USA. E-mail: [email protected]
Abstract: Fast and accurate image reconstruction is the ultimate goal of
iterative methods for limited-angle, few-view, interior problems, etc.
Recently, a finite-detector-based projection model was proposed for iterative
CT reconstructions, which was called area integral model (AIM) and has shown a
high spatial resolution but with a high computational complexity. On the other
hand, the distance-driven model (DDM) is the state-of-the-art technology to
model forward projection and backprojection, which has shown a low
computational complexity but relative low spatial resolution than AIM-based
method. Inspired by the DDM, here we propose an improved distance-driven model
(IDDM), which has a similar computational complexity with the DDM-based method
and comparative spatial resolution with the AIM-based method. In an
ordered-subset simultaneous algebraic reconstruction technique (OS-SART)
framework, the AIM, IDDM and DDM are implemented and evaluated using a sinogram
from a phantom experiment on a Discovery CT750 HD scanner. The results show
that the computational cost of DDM- and IDDM-based methods is similar, which is
6 to 13~times faster than the AIM-based method assuming the same number of
iterations. The spatial resolution of AIM- and IDDM-based method is comparable,
which is better than DDM-based method in terms of full-width-of-half-maximum (FWHM).
Keywords: Computed tomography (CT), image reconstruction, area integral model, distance-driven model, OS-SART
