Pose estimation of known objects during transmission tomographic image reconstruction

Ryan J. Murphy, Shenyu Yan, Joseph A. O'Sullivan, Donald L. Snyder, Bruce R. Whiting, David G. Politic, Giovanni Lasio, Jeffrey F. Williamson

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Pages (from-to)1392-1404
Number of pages13
JournalIEEE Transactions on Medical Imaging
Issue number10
StatePublished - Oct 2006


  • Alternating minimization
  • Iterative image reconstruction
  • Metal artifact reduction
  • Pose estimation
  • Transmission tomography


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