Data Assimilation for Full 4D PC‐MRI Measurements: Physics‐Based Denoising and Interpolation

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dc.contributor.authorde Hoon, N. H. L. C.en_US
dc.contributor.authorJalba, A.C.en_US
dc.contributor.authorFarag, E.S.en_US
dc.contributor.authorvan Ooij, P.en_US
dc.contributor.authorNederveen, A.J.en_US
dc.contributor.authorEisemann, E.en_US
dc.contributor.authorVilanova, A.en_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-10-06T16:54:05Z
dc.date.available2020-10-06T16:54:05Z
dc.date.issued2020
dc.description.abstractPhase‐Contrast Magnetic Resonance Imaging (PC‐MRI) surpasses all other imaging methods in quality and completeness for measuring time‐varying volumetric blood flows and has shown potential to improve both diagnosis and risk assessment of cardiovascular diseases. However, like any measurement of physical phenomena, the data are prone to noise, artefacts and has a limited resolution. Therefore, PC‐MRI data itself do not fulfil physics fluid laws making it difficult to distinguish important flow features. For data analysis, physically plausible and high‐resolution data are required. Computational fluid dynamics provides high‐resolution physically plausible flows. However, the flow is inherently coupled to the underlying anatomy and boundary conditions, which are difficult or sometimes even impossible to adequately model with current techniques. We present a novel methodology using data assimilation techniques for PC‐MRI noise and artefact removal, generating physically plausible flow close to the measured data. It also allows us to increase the spatial and temporal resolution. To avoid sensitivity to the anatomical model, we consider and update the full 3D velocity field. We demonstrate our approach using phantom data with various amounts of induced noise and show that we can improve the data while preserving important flow features, without the need of a highly detailed model of the anatomy.en_US
dc.description.number6
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14088
dc.identifier.issn1467-8659
dc.identifier.pages496-512
dc.identifier.urihttps://doi.org/10.1111/cgf.14088
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14088
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectFlow Visualization
dc.subjectVisualization
dc.subjectMedical Imaging
dc.subjectVisualization
dc.subjectNatural Phenomena
dc.subjectModelling
dc.titleData Assimilation for Full 4D PC‐MRI Measurements: Physics‐Based Denoising and Interpolationen_US
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