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Respiratory Motion Estimation from Slowly Rotating X-Ray Projections

dc.contributor.authorZeng, Rongpingen_US
dc.contributor.authorFessler, Jeffrey A.en_US
dc.contributor.authorBalter, James M.en_US
dc.date.accessioned2011-08-18T18:20:59Z
dc.date.available2011-08-18T18:20:59Z
dc.date.issued2004-04-15en_US
dc.identifier.citationRongping Zeng; Fessler, J.A.; Balter, J. (2004). "Respiratory Motion Estimation from Slowly Rotating X-Ray Projections."IEEE International Symposium on Biomedical Imaging: Nano to Macro 1: 480-483. <http://hdl.handle.net/2027.42/85878>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85878
dc.description.abstractAs radiotherapy has become increasingly conformal, geometric uncertainties caused by breathing and organ motion have become an important issue. Accurate motion estimates may lead to improved treatment planning and dose calculation in radiation therapy. However, respiratory motion is difficult to study by conventional X-ray CT imaging since object motion causes inconsistent projection views leading to artifacts in reconstructed images. We propose to estimate the parameters of a nonrigid motion model from a set of projection views of the thorax that are acquired using a slowly rotating cone-beam CT scanner, such as a radiotherapy simulator. We use a conventionally reconstructed 3D thorax image, acquired by breath-hold CT, as a reference volume. We represent respiratory motion using a flexible parametric nonrigid motion model based on B-splines. The motion parameters are estimated by optimizing a regularized cost function that includes the squared error between the measured projection views and the reprojections of the deformed reference image. Preliminary 2D simulation results show that there is good agreement between the estimated motion and the true motion.en_US
dc.publisherIEEEen_US
dc.titleRespiratory Motion Estimation from Slowly Rotating X-Ray Projectionsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumEECS / Radiation Oncology Dept.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/85878/1/Fessler197.pdf
dc.identifier.doi10.1109/ISBI.2004.1398579en_US
dc.identifier.sourceIEEE International Symposium on Biomedical Imaging: Nano to Macroen_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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