TY - GEN
T1 - Joint-MAP reconstruction/segmentation for transmission tomography using mixture-models as priors
AU - Hsiao, Ing Tsung
AU - Rangarajan, Anand
AU - Gindi, Gene
PY - 1999
Y1 - 1999
N2 - A Bayesian method, including a pointwise prior comprising mixtures of gamma distributions, is applied to the problem of transmission tomography. A joint MAP (maximum a posteriori) procedure is proposed wherein the reconstruction itself, as well as all pointwise parameters, are calculated simultaneously. It uses an algorithm that successively refines the estimate of the mixture parameters and the reconstruction. The approach aims to solve the problem of low counts statistics in transmission tomography. Initial simulation results with anecdotal reconstruction show that the gamma mixture model likely outperforms the ML (maximum likelihood) method and FBP (filtered-backprojection) algorithm.
AB - A Bayesian method, including a pointwise prior comprising mixtures of gamma distributions, is applied to the problem of transmission tomography. A joint MAP (maximum a posteriori) procedure is proposed wherein the reconstruction itself, as well as all pointwise parameters, are calculated simultaneously. It uses an algorithm that successively refines the estimate of the mixture parameters and the reconstruction. The approach aims to solve the problem of low counts statistics in transmission tomography. Initial simulation results with anecdotal reconstruction show that the gamma mixture model likely outperforms the ML (maximum likelihood) method and FBP (filtered-backprojection) algorithm.
UR - http://www.scopus.com/inward/record.url?scp=0032596914&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:0032596914
SN - 0780350227
T3 - IEEE Nuclear Science Symposium and Medical Imaging Conference
SP - 1689
EP - 1693
BT - IEEE Nuclear Science Symposium and Medical Imaging Conference
PB - IEEE
T2 - Proceedings of the 1998 IEEE Nuclear Science Symposium Conference Record
Y2 - 8 November 1998 through 14 November 1998
ER -