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dc.contributor.advisorPolina Golland.en_US
dc.contributor.authorLiao, Ruizhien_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2017-10-18T15:10:07Z
dc.date.available2017-10-18T15:10:07Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111921
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 29-32).en_US
dc.description.abstractTime-course analysis in medical image series often suffers from serious motion. Registration provides voxel correspondences among images, and is commonly employed for correcting motion in medical images. Yet, the registration procedure fails when aligning volumes that are substantially different from template. We present a robust method to correct for motion and deformations in MRI time series. We make a Markov assumption on the nature of deformations to take advantage of the temporal smoothness in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use for in-utero MRI time series alignment improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations present in in-utero MRI time series. We also demonstrate that our method can be used for cardiac cine MRI. By propagating segmentation labels of one volume to the other frames in the cine MRI through deformation estimated by our method, 4D (3D+time) cardiac MRI series can be segmented.en_US
dc.description.statementofresponsibilityby Ruizhi Liao.en_US
dc.format.extent32 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleTemporal registration for MRI time seriesen_US
dc.title.alternativeTemporal registration for magnetic resonance imaging time seriesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1005706127en_US


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