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 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.03.2010
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| ISSN: | 1053-1807, 1522-2586, 1522-2586 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. To completely automate the reconstruction process during noncardiac-gated unenhanced ghost magnetic resonance angiography (MRA).PURPOSETo 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.MATERIALS AND METHODSUngated 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.RESULTSCompared 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.CONCLUSIONWe 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. 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. |
| Author | Tsaftaris, Sotirios A. Offerman, Erik Koktzoglou, Ioannis Edelman, Robert R. |
| Author_xml | – sequence: 1 givenname: Sotirios A. surname: Tsaftaris fullname: Tsaftaris, Sotirios A. email: s-tsaftaris@northwestern.edu organization: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA – sequence: 2 givenname: Erik surname: Offerman fullname: Offerman, Erik organization: Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA – sequence: 3 givenname: Robert R. surname: Edelman fullname: Edelman, Robert R. organization: Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA – sequence: 4 givenname: Ioannis surname: Koktzoglou fullname: Koktzoglou, Ioannis organization: Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA |
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| References | Koktzoglou I, Edelman RR. Ghost magnetic resonance angiography. Magn Reson Med 2009; 61: 1515- 1519. Lim RP, Hecht EM, Xu J, et al. 3D nongadolinium-enhanced ECG-gated MRA of the distal lower extremities: preliminary clinical experience. J Magn Reson Imaging 2008; 28: 181-189. Miyazaki M, Lee VS. Nonenhanced MR angiography. Radiology 2008; 248: 20- 43. Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. SODA '07: Proc 18th Annual ACM-SIAM Symposium on Discrete Algorithms; 2007: 1027-1035. Petrovic VS, Xydeas CS. Gradient-based multiresolution image fusion. IEEE Trans Image Process 2004; 13: 228- 237. Miyazaki M, Sugiura S, Tateishi F, Wada H, Kassai Y, Abe H. Non-contrast-enhanced MR angiography using 3D ECG-synchronized half-Fourier fast spin echo. J Magn Reson Imaging 2000; 12: 776- 783. Bongartz G, Mayr M, Bilecen D. Magnetic resonance angiography (MRA) in renally impaired patients: when and how. Eur J Radiol 2008; 66: 213- 219. Grobner T. Gadolinium-a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol Dial Transplant 2006; 21: 1104- 1108. Jacob M, Unser M. Design of steerable filters for feature detection using canny-like criteria. IEEE Trans Pattern Anal Machine Intell 2004; 26: 1007- 1019. Bezdek J. Pattern Recognition with fuzzy objective function algorithms. Dordrecht, Netherlands: Kluwer Academic; 1981. Bradley WG Jr, Waluch V. Blood flow: magnetic resonance imaging. Radiology 1985; 154: 443- 450. Frangi AF, Niessen WJ, Hoogeveen RM, van Walsum T, Viergever MA. Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans Med Imaging 1999; 18: 946- 956. 2001 2006; 21 2009; 61 2000; 12 1999; 18 2004; 26 2004; 13 2008; 28 2007 2008; 248 2008; 66 2003 1981 1985; 154 Arthur D (e_1_2_6_11_2) 2007 e_1_2_6_7_2 MacKay D (e_1_2_6_8_2) 2003 e_1_2_6_9_2 e_1_2_6_4_2 e_1_2_6_3_2 e_1_2_6_6_2 e_1_2_6_5_2 e_1_2_6_12_2 e_1_2_6_13_2 e_1_2_6_2_2 e_1_2_6_10_2 e_1_2_6_14_2 e_1_2_6_15_2 |
| References_xml | – reference: Miyazaki M, Lee VS. Nonenhanced MR angiography. Radiology 2008; 248: 20- 43. – reference: Koktzoglou I, Edelman RR. Ghost magnetic resonance angiography. Magn Reson Med 2009; 61: 1515- 1519. – reference: Arthur D, Vassilvitskii S. k-means++: the advantages of careful seeding. SODA '07: Proc 18th Annual ACM-SIAM Symposium on Discrete Algorithms; 2007: 1027-1035. – reference: Frangi AF, Niessen WJ, Hoogeveen RM, van Walsum T, Viergever MA. Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans Med Imaging 1999; 18: 946- 956. – reference: Bezdek J. Pattern Recognition with fuzzy objective function algorithms. Dordrecht, Netherlands: Kluwer Academic; 1981. – reference: Petrovic VS, Xydeas CS. Gradient-based multiresolution image fusion. IEEE Trans Image Process 2004; 13: 228- 237. – reference: Lim RP, Hecht EM, Xu J, et al. 3D nongadolinium-enhanced ECG-gated MRA of the distal lower extremities: preliminary clinical experience. J Magn Reson Imaging 2008; 28: 181-189. – reference: Grobner T. Gadolinium-a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol Dial Transplant 2006; 21: 1104- 1108. – reference: Bongartz G, Mayr M, Bilecen D. Magnetic resonance angiography (MRA) in renally impaired patients: when and how. Eur J Radiol 2008; 66: 213- 219. – reference: Miyazaki M, Sugiura S, Tateishi F, Wada H, Kassai Y, Abe H. Non-contrast-enhanced MR angiography using 3D ECG-synchronized half-Fourier fast spin echo. J Magn Reson Imaging 2000; 12: 776- 783. – reference: Jacob M, Unser M. Design of steerable filters for feature detection using canny-like criteria. IEEE Trans Pattern Anal Machine Intell 2004; 26: 1007- 1019. – reference: Bradley WG Jr, Waluch V. Blood flow: magnetic resonance imaging. Radiology 1985; 154: 443- 450. – volume: 12 start-page: 776 year: 2000 article-title: Non‐contrast‐enhanced MR angiography using 3D ECG‐synchronized half‐Fourier fast spin echo publication-title: J Magn Reson Imaging – year: 1981 – volume: 66 start-page: 213 year: 2008 article-title: Magnetic resonance angiography (MRA) in renally impaired patients: when and how publication-title: Eur J Radiol – volume: 154 start-page: 443 year: 1985 article-title: Blood flow: magnetic resonance imaging publication-title: Radiology – volume: 26 start-page: 1007 year: 2004 article-title: Design of steerable filters for feature detection using canny‐like criteria publication-title: IEEE Trans Pattern Anal Machine Intell – year: 2001 – volume: 21 start-page: 1104 year: 2006 article-title: Gadolinium—a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? publication-title: Nephrol Dial Transplant – volume: 13 start-page: 228 year: 2004 article-title: Gradient‐based multiresolution image fusion publication-title: IEEE Trans Image Process – volume: 28 start-page: 181 year: 2008 end-page: 189 article-title: 3D nongadolinium‐enhanced ECG‐gated MRA of the distal lower extremities: preliminary clinical experience publication-title: J Magn Reson Imaging – start-page: 284 year: 2003 end-page: 292 – volume: 248 start-page: 20 year: 2008 article-title: Nonenhanced MR angiography publication-title: Radiology – volume: 61 start-page: 1515 year: 2009 article-title: Ghost magnetic resonance angiography publication-title: Magn Reson Med – start-page: 1027 year: 2007 end-page: 1035 article-title: k‐means++: the advantages of careful seeding publication-title: SODA '07: Proc 18th Annual ACM‐SIAM Symposium on Discrete Algorithms – volume: 18 start-page: 946 year: 1999 article-title: Model‐based quantitation of 3‐D magnetic resonance angiographic images publication-title: IEEE Trans Med Imaging – ident: e_1_2_6_2_2 doi: 10.1093/ndt/gfk062 – ident: e_1_2_6_4_2 doi: 10.1148/radiol.2481071497 – ident: e_1_2_6_3_2 doi: 10.1016/j.ejrad.2008.01.031 – ident: e_1_2_6_7_2 doi: 10.1002/1522-2586(200011)12:5<776::AID-JMRI17>3.0.CO;2-X – ident: e_1_2_6_5_2 doi: 10.1002/mrm.21943 – ident: e_1_2_6_13_2 doi: 10.1109/TIP.2004.823821 – start-page: 284 volume-title: Information Theory, Inference and Learning Algorithms year: 2003 ident: e_1_2_6_8_2 – ident: e_1_2_6_9_2 doi: 10.1007/978-1-4757-0450-1 – ident: e_1_2_6_14_2 doi: 10.1109/42.811279 – start-page: 1027 year: 2007 ident: e_1_2_6_11_2 article-title: k‐means++: the advantages of careful seeding publication-title: SODA '07: Proc 18th Annual ACM‐SIAM Symposium on Discrete Algorithms – ident: e_1_2_6_15_2 doi: 10.1002/jmri.21416 – ident: e_1_2_6_6_2 doi: 10.1148/radiology.154.2.3966131 – ident: e_1_2_6_12_2 – ident: e_1_2_6_10_2 doi: 10.1109/TPAMI.2004.44 |
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To completely automate the reconstruction process during noncardiac‐gated unenhanced ghost magnetic resonance angiography (MRA).
Materials and... To completely automate the reconstruction process during noncardiac-gated unenhanced ghost magnetic resonance angiography (MRA). Ungated unenhanced ghost MRA... To completely automate the reconstruction process during noncardiac-gated unenhanced ghost magnetic resonance angiography (MRA).PURPOSETo completely automate... |
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| SubjectTerms | 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 |
| Title | Fully automated reconstruction of ungated ghost magnetic resonance angiograms |
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