Penalized Likelihood Emission Image Reconstruction with Uncertain Boundary Information
dc.contributor.author | Titus, Stephen R. | en_US |
dc.contributor.author | Hero, Alfred O. III | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2011-08-18T18:20:53Z | |
dc.date.available | 2011-08-18T18:20:53Z | |
dc.date.issued | 1997-04-21 | en_US |
dc.identifier.citation | Titus, S.R. Hero, A.O., III ; Fessler, J.A. (1997). "Penalized Likelihood Emission Image Reconstruction with Uncertain Boundary Information."IEEE InternationalConference on Acoustics, Speech, and Signal Processing 4: 2813-2816. <http://hdl.handle.net/2027.42/85848> | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85848 | |
dc.description.abstract | In this paper, a method is introduced for incorporating perfectly registered MRI boundary information into a penalized likelihood emission reconstruction scheme. The boundary curve is modeled as a periodic spline whose coefficients are estimated from the MRI image. The resulting boundary estimate is mapped to a spatially variant set of Gibbs weights. When incorporated into a quadratic roughness penalty, these weights improve emission reconstruction bias/variance performance by preventing smoothing across the estimated boundary. Finally, we derive a new penalty function that accounts for the uncertainty inherent in the boundary estimates. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Penalized Likelihood Emission Image Reconstruction with Uncertain Boundary Information | en_US |
dc.type | article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | EECS | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85848/1/Fessler145.pdf | |
dc.identifier.doi | 10.1109/ICASSP.1997.595374 | en_US |
dc.identifier.source | IEEE InternationalConference on Acoustics, Speech, and Signal Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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