TY - JOUR
T1 - Model-based tomographic reconstruction of objects containing known components
AU - Stayman, J. Webster
AU - Otake, Yoshito
AU - Prince, Jerry L.
AU - Khanna, A. Jay
AU - Siewerdsen, Jeffrey H.
N1 - Funding Information:
Manuscript received April 17, 2012; received April 17, 2012; accepted May 02, 2012. Date of publication May 16, 2012; date of current version September 27, 2012. This work was supported in part by the National Institute of Health under Grant CA-127444. Asterisk indicates corresponding author. *J. W. Stayman is with the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205 USA (e-mail: web.stayman@jhu. edu). Y. Otake and J. H. Siewerdsen are with the Departments of Biomedical Engineering and Computer Science, Johns Hopkins University, Baltimore, MD 21205 USA. J. L. Prince is with the Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA. A. J. Khanna is with the Department of Orthopaedic Surgery, Johns Hopkins University, Baltimore, MD 21205 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMI.2012.2199763
PY - 2012
Y1 - 2012
N2 - The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the target anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.
AB - The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the target anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.
KW - CT reconstruction
KW - implant imaging
KW - metal artifact reduction
KW - penalized-likelihood estimation
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U2 - 10.1109/TMI.2012.2199763
DO - 10.1109/TMI.2012.2199763
M3 - Article
C2 - 22614574
AN - SCOPUS:84867089685
SN - 0278-0062
VL - 31
SP - 1837
EP - 1848
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 10
M1 - 6200873
ER -