Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12708
Title: Restoration of intensity uniformity of bi-contrast MRI data with bayesian co-occurrence coring
Authors: Hadjidemetriou, Stathis 
Psychogios, Marios Nikos 
Lingor, Paul 
Von Eckardstein, Kajetan L. 
Papageorgiou, Ismini 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Bayesian coring estimate;Bi-contrast MRI reconstruction;Co-occurrence statistics
Issue Date: Jul-2017
Source: 21st Annual Conference on Medical Image Understanding and Analysis, 2017, Edinburgh, United Kingdom, 11-13 July
DOI: https://doi.org/10.1007/978-3-319-60964-5_54
Abstract: The reconstruction in MRI assumes a uniform radiofrequency field. However, this is violated, which leads to anatomically inconsequential intensity non-uniformities. An anatomic region can be imaged with multiple contrasts that result in different non-uniformities. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled. Their non-stationary deconvolution gives Bayesian coring estimates of the images. Further constraints for smoothness, stability, and validity of the non-uniformity estimates are also imposed. The effectiveness and accuracy of the method has been demonstrated extensively with both BrainWeb phantom images as well as with real brain anatomic data of 29 Parkinson’s disease patients.
URI: https://hdl.handle.net/20.500.14279/12708
Rights: © Springer International Publishing AG 2017.
Type: Conference Papers
Affiliation : Cyprus University of Technology 
University of Gottingen 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

CORE Recommender
Show full item record

Page view(s) 50

324
Last Week
4
Last month
21
checked on Apr 27, 2024

Google ScholarTM

Check


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.