Object-Based 3-D Reconstruction of Arterial Trees from Magnetic Resonance Angiograms
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.contributor.author | Macovski, Albert | en_US |
dc.date.accessioned | 2011-08-18T18:20:52Z | |
dc.date.available | 2011-08-18T18:20:52Z | |
dc.date.issued | 1991-03 | en_US |
dc.identifier.citation | Fessler, J.A.; Macovski, A. (1991). "Object-Based 3-D Reconstruction of Arterial Trees from Magnetic Resonance Angiograms." IEEE Transactions on Medical Imaging 10(1): 25-39. <http://hdl.handle.net/2027.42/85841> | en_US |
dc.identifier.issn | 0278-0062 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85841 | |
dc.description.abstract | By exploiting a priori knowledge of arterial shape and smoothness, subpixel accuracy reconstructions are achieved from only four noisy projection images. The method incorporates a priori knowledge of the structure of branching arteries into a natural optimality criterion that encompasses the entire arterial tree. An efficient optimization algorithm for object estimation is presented, and its performance on simulated, phantom, and in vivo magnetic resonance angiograms is demonstrated. It is shown that accurate reconstruction of bifurcations is achievable with parametric models. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Object-Based 3-D Reconstruction of Arterial Trees from Magnetic Resonance Angiograms | 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.affiliationother | Department of Electrical Engineering, Information Systems Laboratory, Stanford University. Stanford, CA 95305. | en_US |
dc.identifier.pmid | 18222797 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85841/1/Fessler111.pdf | |
dc.identifier.doi | 10.1109/42.75608 | en_US |
dc.identifier.source | IEEE Transactions on Medical Imaging | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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