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4D MRI Flow Coupled to Physics‐Based Fluid Simulation for Blood‐Flow Visualization
Author(s) -
de Hoon N.,
van Pelt R.,
Jalba A.,
Vilanova A.
Publication year - 2014
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12368
Subject(s) - visualization , computer science , robustness (evolution) , interpolation (computer graphics) , data assimilation , flow visualization , data quality , data visualization , data mining , blood flow , noise (video) , artificial intelligence , flow (mathematics) , computer vision , mathematics , physics , image (mathematics) , medicine , metric (unit) , biochemistry , chemistry , geometry , meteorology , economics , gene , operations management
Modern MRI measurements deliver volumetric and time‐varying blood‐flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically‐based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach.