TY - JOUR
T1 - Fast iterative reconstructions for animal CT
AU - Huang, H. M.
AU - Hsiao, I. T.
AU - Jan, M. L.
PY - 2009
Y1 - 2009
N2 - For iterative x-ray computed tomography (CT) reconstruction, the convex algorithm combined with ordered subset (OSC) [1] is a relatively fast algorithm and has shown its potential for low-dose situations. But it needs one forward projection and two backprojections per iteration. Unlike convex algorithm, the gradient algorithm only requires one forward projection and one backprojection per iteration. Here, we applied ordered subsets of projection data to a modified gradient algorithm. In order to further reduce computation time, the new algorithm, the ordered subset gradient (OSG) algorithm, can be adjusted with a step size. We also implemented another OS-type algorithm called OSTR. The OSG algorithm is compared with OSC algorithm and OSTR algorithm using three-dimensional simulated helical cone-beam CT data. The performance is evaluated in terms of log-likelihood, contrast recovery, and bias-variance studies. Results show that images of OSG has compatible visual image quality to those of OSC and OSTR, but in the resolution and bias-variance studies, OSG seems to reach stable values with faster speed. In particular, OSTR has better recovery in a smoother region, but both OSG and OSC have better recovery in the high-frequency regions. Moreover, in terms of log likelihood with respect to computation time, OSG has faster convergence rate than that of OSC and similar to that of OSTR. We conclude that OSG has potential to provide comparable image quality and is more computationally efficient, and thus could be suitable for low-dose, helical cone-beam CT image reconstruction.
AB - For iterative x-ray computed tomography (CT) reconstruction, the convex algorithm combined with ordered subset (OSC) [1] is a relatively fast algorithm and has shown its potential for low-dose situations. But it needs one forward projection and two backprojections per iteration. Unlike convex algorithm, the gradient algorithm only requires one forward projection and one backprojection per iteration. Here, we applied ordered subsets of projection data to a modified gradient algorithm. In order to further reduce computation time, the new algorithm, the ordered subset gradient (OSG) algorithm, can be adjusted with a step size. We also implemented another OS-type algorithm called OSTR. The OSG algorithm is compared with OSC algorithm and OSTR algorithm using three-dimensional simulated helical cone-beam CT data. The performance is evaluated in terms of log-likelihood, contrast recovery, and bias-variance studies. Results show that images of OSG has compatible visual image quality to those of OSC and OSTR, but in the resolution and bias-variance studies, OSG seems to reach stable values with faster speed. In particular, OSTR has better recovery in a smoother region, but both OSG and OSC have better recovery in the high-frequency regions. Moreover, in terms of log likelihood with respect to computation time, OSG has faster convergence rate than that of OSC and similar to that of OSTR. We conclude that OSG has potential to provide comparable image quality and is more computationally efficient, and thus could be suitable for low-dose, helical cone-beam CT image reconstruction.
KW - Data processing methods
KW - Image reconstruction in medical imaging
UR - http://www.scopus.com/inward/record.url?scp=71049117194&partnerID=8YFLogxK
U2 - 10.1088/1748-0221/4/06/P06017
DO - 10.1088/1748-0221/4/06/P06017
M3 - 文章
AN - SCOPUS:71049117194
SN - 1748-0221
VL - 4
JO - Journal of Instrumentation
JF - Journal of Instrumentation
IS - 6
M1 - P06017
ER -