DocumentCode
794649
Title
An Eulerian PDE approach for computing tissue thickness
Author
Yezzi, Anthony J., Jr. ; Prince, Jerry L.
Author_Institution
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Volume
22
Issue
10
fYear
2003
Firstpage
1332
Lastpage
1339
Abstract
We outline an Eulerian framework for computing the thickness of tissues between two simply connected boundaries that does not require landmark points or parameterizations of either boundary. Thickness is defined as the length of correspondence trajectories, which run from one tissue boundary to the other, and which follow a smooth vector field constructed in the region between the boundaries. A pair of partial differential equations (PDEs) that are guided by this vector field are then solved over this region, and the sum of their solutions yields the thickness of the tissue region. Unlike other approaches, this approach does not require explicit construction of any correspondence trajectories. An efficient, stable, and computationally fast solution to these PDEs is found by careful selection of finite differences according to an up-winding condition. The behavior and performance of our method is demonstrated on two simulations and two magnetic resonance imaging data sets in two and three dimensions. These experiments reveal very good performance and show strong potential for application in tissue thickness visualization and quantification.
Keywords
biological tissues; biomedical MRI; finite difference methods; medical image processing; partial differential equations; thickness measurement; vectors; Eulerian PDE approach; correspondence trajectories length; magnetic resonance imaging; magnetic resonance imaging data sets; medical diagnostic imaging; numerical methods; tissue region thickness; tissue thickness computation; tissue thickness quantification; tissue thickness visualization; up-winding condition; Alzheimer´s disease; Biomedical imaging; Cardiac disease; Computational modeling; Data visualization; Finite difference methods; Image analysis; Magnetic resonance imaging; Myocardium; Partial differential equations; Algorithms; Anatomy, Cross-Sectional; Anthropometry; Cartilage, Articular; Cerebral Cortex; Computer Simulation; Connective Tissue; Heart; Humans; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Biological; Pattern Recognition, Automated; Phantoms, Imaging; Tibia;
fLanguage
English
Journal_Title
Medical Imaging, IEEE Transactions on
Publisher
ieee
ISSN
0278-0062
Type
jour
DOI
10.1109/TMI.2003.817775
Filename
1233930
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