TY - JOUR
T1 - Pose estimation of known objects during transmission tomographic image reconstruction
AU - Murphy, Ryan J.
AU - Yan, Shenyu
AU - O'Sullivan, Joseph A.
AU - Snyder, Donald L.
AU - Whiting, Bruce R.
AU - Politic, David G.
AU - Lasio, Giovanni
AU - Williamson, Jeffrey F.
N1 - Funding Information:
Manuscript received February 23, 2006; revised June 20, 2006. This work was supported in part by the Graduate Education and Research Partnership with the Boeing Foundation under Grant 34566C, in part by the National Institutes of Health (NIH) under Research Grant R01 CA75371 from the National Cancer Institute of the NIH, (J. F. Williamson, P. I.), and in part by the Whitaker Foundation. Asterisk indicates corresponding author. *R. J. Murphy is with Advanced Information Systems, General Dynamics, Ypsilanti, MI 48197 USA (e-mail: ryan.murphy@gd-ais.com).
PY - 2006/10
Y1 - 2006/10
N2 - We address the problem of image formation in transmission tomography when metal objects of known composition and shape, but unknown pose, are present in the scan subject. Using an alternating minimization (AIM) algorithm, derived from a model in which the detected data are viewed as Poisson-distributed photon counts, we seek to eliminate the streaking artifacts commonly seen in filtered back projection images containing high-contrast objects. We show that this algorithm, which minimizes the I-divergence (or equivalently, maximizes the log-likelihood) between the measured data and model-based estimates of the means of the data, converges much faster when knowledge of the high-density materials (such as brachytherapy applicators or prosthetic implants) is exploited. The algorithm incorporates a steepest descent-based method to find the position and orientation (collectively called the pose) of the known objects. This pose is then used to constrain the image pixels to their known attenuation values, or, for example, to form a mask on the "missing" projection data in the shadow of the objects. Results from two-dimensional simulations are shown in this paper. The extension of the model and methods used to three dimensions is outlined.
AB - We address the problem of image formation in transmission tomography when metal objects of known composition and shape, but unknown pose, are present in the scan subject. Using an alternating minimization (AIM) algorithm, derived from a model in which the detected data are viewed as Poisson-distributed photon counts, we seek to eliminate the streaking artifacts commonly seen in filtered back projection images containing high-contrast objects. We show that this algorithm, which minimizes the I-divergence (or equivalently, maximizes the log-likelihood) between the measured data and model-based estimates of the means of the data, converges much faster when knowledge of the high-density materials (such as brachytherapy applicators or prosthetic implants) is exploited. The algorithm incorporates a steepest descent-based method to find the position and orientation (collectively called the pose) of the known objects. This pose is then used to constrain the image pixels to their known attenuation values, or, for example, to form a mask on the "missing" projection data in the shadow of the objects. Results from two-dimensional simulations are shown in this paper. The extension of the model and methods used to three dimensions is outlined.
KW - Alternating minimization
KW - Iterative image reconstruction
KW - Metal artifact reduction
KW - Pose estimation
KW - Transmission tomography
UR - http://www.scopus.com/inward/record.url?scp=33751186099&partnerID=8YFLogxK
U2 - 10.1109/TMI.2006.880673
DO - 10.1109/TMI.2006.880673
M3 - Article
C2 - 17024842
AN - SCOPUS:33751186099
SN - 0278-0062
VL - 25
SP - 1392
EP - 1404
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 10
ER -