Fully automated reconstruction of ungated ghost magnetic resonance angiograms

Purpose: To completely automate the reconstruction process during noncardiac‐gated unenhanced ghost magnetic resonance angiography (MRA). Materials and Methods: Ungated unenhanced ghost MRA of the calf was performed in 16 volunteers. K‐means and fuzzy c‐means (FCM) clustering algorithms using promin...

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Vydáno v:Journal of magnetic resonance imaging Ročník 31; číslo 3; s. 655 - 662
Hlavní autoři: Tsaftaris, Sotirios A., Offerman, Erik, Edelman, Robert R., Koktzoglou, Ioannis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.03.2010
Témata:
Adult
Algorithms
Cardiac-Gated Imaging Techniques
cluster analysis
Female
ghost angiography
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
image reconstruction
Leg - anatomy & histology
Leg - blood supply
Magnetic Resonance Angiography - methods
Male
Middle Aged
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Young Adult
ISSN:1053-1807, 1522-2586, 1522-2586
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Shrnutí:Purpose: To completely automate the reconstruction process during noncardiac‐gated unenhanced ghost magnetic resonance angiography (MRA). Materials and Methods: 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. Results: 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. Conclusion: 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. J. Magn. Reson. Imaging 2010;31:655–662. © 2010 Wiley‐Liss, Inc.
Bibliografie:ark:/67375/WNG-PZN0KQ89-8
istex:F9533BF9DDD01F6E75C6D9F2C9C04FFD2FC5A395
The Grainger Foundation and The Auxiliary of NorthShore University HealthSystem
ArticleID:JMRI22092
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.22092