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Proceedings Paper

Accuracy of non-rigid registration for local analysis of elasticity restrictions of the lungs
Author(s): Daniel Stein; Ralf Tetzlaff; Ivo Wolf; Hans-Peter Meinzer
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Paper Abstract

Diseases of the lung often begin with regionally limited changes altering the tissue elasticity. Therefore, quantification of regional lung tissue motion would be desirable for early diagnosis, treatment monitoring, and follow-up. Dynamic MRI can capture such changes, but quantification requires non-rigid registration. However, analysis of dynamic MRI data of the lung is challenging due to inherently low image signal and contrast. Towards a computer-assisted quantification for regional lung diseases, we have evaluated two Demons-based registration methods for their accuracy in quantifying local lung motion on dynamic MRI data. The registration methods were applied on masked image data, which were pre-segmented with a graph-cut algorithm. Evaluation was performed on five datasets from healthy humans with nine time frames each. As gold standard, manually defined points (between 8 and 24) on prominent landmarks (essentially vessel structures) were used. The distance between these points and the predicted landmark location as well as the overlap (Dice coefficient) of the segmentations transformed with the deformation field were calculated. We found that the Demons algorithm performed better than the Symmetric Forces Demons algorithm with respect to average landmark distance (6.5 mm ± 4.1 mm vs. 8.6 mm ± 6.1 mm), but comparable regarding the Dice coefficient (0.946 ± 0.018 vs. 0.961 ± 0.018). Additionally, the Demons algorithm computes the deformation in only 10 seconds, whereas the Symmetric Forces Demons algorithm takes about 12 times longer.

Paper Details

Date Published: 13 March 2009
PDF: 9 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72611P (13 March 2009); doi: 10.1117/12.812234
Show Author Affiliations
Daniel Stein, German Cancer Research Ctr. (Germany)
Ralf Tetzlaff, German Cancer Research Ctr. (Germany)
Ivo Wolf, German Cancer Research Ctr. (Germany)
Hans-Peter Meinzer, German Cancer Research Ctr. (Germany)

Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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