Publication
Title
Partially discrete magnetic resonance tomography
Author
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.
Language
English
Source (journal)
Proceedings. - Los Alamitos, Calif, 1994, currens
Source (book)
IEEE International Conference on Image Processing (ICIP), SEP 27-30, 2015, Quebec City, CANADA
Publication
New york : Ieee, 2015
Volume/pages
(2015), p. 1653-1657
ISI
000371977801154
Number
978-1-4799-8339-1
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 06.06.2016
Last edited 09.06.2017
To cite this reference