eprintid: 798
rev_number: 20
eprint_status: archive
userid: 35
dir: disk0/00/00/07/98
datestamp: 2011-08-12 09:37:36
lastmod: 2013-03-05 15:34:33
status_changed: 2011-08-12 09:37:36
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Tsaftaris, Sotirios A.
creators_name: Offerman, Erik
creators_name: Edelman, Robert R.
creators_name: Koktzoglou, Ioannis
creators_id: sotirios.tsaftaris@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
title: Fully automated reconstruction of ungated ghost magnetic resonance angiograms
ispublished: pub
subjects: QA76
subjects: RC
divisions: CSA
full_text_status: none
keywords: Ghost angiography; Image reconstruction; Cluster analysis
abstract: To completely automate the reconstruction process during noncardiac-gated unenhanced ghost magnetic resonance angiography (MRA).Ungated unenhanced ghost MRA of the calf was performed in 16 volunteers. K-means and fuzzy c-means (FCM) clustering algorithms using prominent image features were applied to automatically create angiograms of the calf in volunteers undergoing ungated ghost MRA. Ghost angiograms reconstructed automatically were compared to those created manually on the basis of diagnostic image quality and apparent arterial-to-background contrast-to-noise ratio (CNR). Images were also ranked by an expert user in their order of preference using an ordinal scale.Compared with the ghost angiograms created manually, ghost angiograms reconstructed automatically with the use of clustering analysis provided similar arterial-to-background CNR values. No differences in diagnostic quality or preference were identified between images reconstructed manually and automatically.We present fully automated image reconstruction algorithms for use with ungated and unenhanced ghost MRA. These automated algorithms, based on the use of k-means or FCM clustering, can be used to eliminate manual postprocessing that is time-consuming and subject to variability. 
date: 2010-03
date_type: published
publication: Journal of magnetic resonance imaging
volume: 31
number: 3
publisher: Wiley-Blackwell
place_of_pub: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; Department of Radiology, NorthShore Universi
pagerange: 655-662
id_number: 10.1002/jmri.22092
refereed: TRUE
issn: 1053-1807
official_url: http://dx.doi.org/10.1002/jmri.22092
citation:   Tsaftaris, Sotirios A. and Offerman, Erik and Edelman, Robert R. and Koktzoglou, Ioannis  Fully automated reconstruction of ungated ghost magnetic resonance angiograms.  Journal of magnetic resonance imaging, 31 (3).  pp. 655-662.  ISSN 1053-1807      (2010)