Title
Partially discrete magnetic resonance tomography
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
conferenceObject
Publication
New york :Ieee ,
Subject
Physics
Engineering sciences. Technology
Source (journal)
2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Source (book)
IEEE International Conference on Image Processing (ICIP), SEP 27-30, 2015, Quebec City, CANADA
Volume/pages
(2015) , p. 1653-1657
ISSN
1522-4880
ISBN
978-1-4799-8339-1
ISI
000371977801154
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
In Magnetic Resonance (MR) image reconstruction, under-sampled data sets lead to ill-posed reconstruction problems. To regularize these problems, prior knowledge is commonly exploited. In this work, we introduce a new type of prior knowledge, partial discreteness, where part of the image is assumed to be homogeneous and can be well represented by a constant magnitude. We propose a novel reconstruction algorithm that incorporates this prior knowledge by iteratively enforcing discreteness through Bayesian segmentation regularization. Results on simulated MR images of breast implants and MR angiography images evidence the benefits of the partial discreteness prior when compared to state-of-the-art reconstruction methods.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000371977801154&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000371977801154&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
https://repository.uantwerpen.be/docman/iruaauth/8aa00c/133608.pdf
Handle