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dc.contributor.author
Zhang, Lin
dc.contributor.author
Karani, Neerav
dc.contributor.author
Tanner, Christine
dc.contributor.author
Konukoglu, Ender
dc.date.accessioned
2019-02-06T08:28:30Z
dc.date.available
2019-02-05T09:46:38Z
dc.date.available
2019-02-05T17:34:56Z
dc.date.available
2019-02-06T08:28:30Z
dc.date.issued
2018
dc.identifier.uri
http://hdl.handle.net/20.500.11850/322976
dc.identifier.doi
10.3929/ethz-b-000322976
dc.description.abstract
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning and guidance. Navigator slices are vital for retrospective stacking of 2D data slices in this method. However, they also prolong the acquisition sessions. Temporal interpolation of navigator slices can be used to reduce the number of navigator acquisitions without degrading specificity in stacking. In this work, we propose a convolutional neural network (CNN) based method for temporal interpolation via motion field prediction. The proposed formulation incorporates the prior knowledge that a motion field underlies changes in the image intensities over time. Previous approaches that interpolate directly in the intensity space are prone to produce blurry images or even remove structures in the images. Our method avoids such problems and faithfully preserves the information in the image. Further, an important advantage of our formulation is that it provides an unsupervised estimation of bi-directional motion fields. We show that these motion fields can be used to halve the number of registrations required during 4D reconstruction, thus substantially reducing the reconstruction time.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
OpenReview
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
temporal image interpolation
en_US
dc.subject
prior knowledge in deep learning
en_US
dc.subject
motion estimation
en_US
dc.title
Temporal Interpolation via Motion Field Prediction
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
ethz.size
10 p.
en_US
ethz.version.deposit
submittedVersion
en_US
ethz.event
1st Conference on Medical Imaging with Deep Learning, MIDL 2018
en_US
ethz.event.location
Amsterdam, Netherlands
en_US
ethz.event.date
July 4-6, 2018
en_US
ethz.publication.place
s.l.
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09579 - Konukoglu, Ender / Konukoglu, Ender
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09579 - Konukoglu, Ender / Konukoglu, Ender
en_US
ethz.identifier.url
https://openreview.net/forum?id=HkgCl-3if
ethz.date.deposited
2019-02-05T09:46:50Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-02-06T08:28:38Z
ethz.rosetta.lastUpdated
2021-02-15T03:33:57Z
ethz.rosetta.versionExported
true
ethz.COinS
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