A self-trained algorithm based on Ostu’s method and a radial basis function (RBF) kernel support vector machine (SVM) model was developed for automatic lumen detection and quantification for the negative polarity map of SNAP magnetic resonance angiography(MRA). Based on an analysis of 15 arteries with carotid stenosis, the proposed automatic lumen segmentation algorithm demonstrated good agreement with manual lumen segmentation of SNAP MRA (intraclass correlation coefficient (ICC=0.95). The automated method also had good agreement with manual segmentation of CE-MRA (ICC = 0.90), which was comparable to the agreement between manually segmented SNAP MRA and CE-MRA (ICC = 0.93).
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords