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Noble, N.M.I.* ; Hill, D.L.G.* ; Breeuwer, M.* ; Schnabel, J.A.* ; Hawkes, D.J.* ; Gerritsen, F.A.* ; Razavi, R.*

Myocardial Delineation via Registration in a Polar Coordinate System.

Acad. Radiol. 10, 1349-1358 (2003)
DOI
Rationale and Objectives. Cardiovascular disease is the number one cause of premature death in the western world. Analysis of cardiac function provides clinically useful diagnostic and prognostic information; however, manual analysis of function via delineation is prohibitively time consuming. This article describes a technique for analysis of dynamic magnetic resonance images of the left ventricle using a non-rigid registration algorithm. A manually delineated contour of a single phase was propagated through the dynamic sequence. Materials and Methods. Short-axis cine magnetic resonance images were resampled into polar coordinates before all the time frames were aligned using a non-rigid registration algorithm. The technique was tested on 10 patient data sets, a total of 1,052 images were analyzed. Results. Results of this approach were investigated and compared with manual delineation at all phases in the cardiac cycle, and with registration performed in a Cartesian coordinate system. The results correlated very well with manually delineated contours. Conclusion. A novel approach to the registration and subsequent delineation of cardiac magnetic resonance images has been introduced. For the endocardium, the polar resampling technique correlated well with manual delineation, and better than for images registered without radial resampling in a Cartesian coordinate system. For the epicardium, the difference was not as apparent with both techniques correlating well.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Cardiac ; Magnetic Resonance (mr) ; Myocardium ; Registration ; Segmentation
ISSN (print) / ISBN 1076-6332
e-ISSN 1878-4046
Journal Academic radiology
Quellenangaben Volume: 10, Issue: 12, Pages: 1349-1358 Article Number: , Supplement: ,
Publishing Place Reston, VA
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)