T
Thomas Redel
Researcher at Siemens
Publications - 164
Citations - 1767
Thomas Redel is an academic researcher from Siemens. The author has contributed to research in topics: Projection (set theory) & Data set. The author has an hindex of 20, co-authored 164 publications receiving 1660 citations.
Papers
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Journal ArticleDOI
Tetrahedral vs. polyhedral mesh size evaluation on flow velocity and wall shear stress for cerebral hemodynamic simulation.
Martin Spiegel,Thomas Redel,Y. Jonathan Zhang,Tobias Struffert,Joachim Hornegger,Robert G. Grossman,Arnd Doerfler,Christof Karmonik +7 more
TL;DR: CFD results for the two most common aneurysm types are compared for polyhedral- vs. tetrahedral-based meshes and indicate a benefit of polyhedral meshes in respect to convergence speed and more homogeneous WSS patterns.
Patent
Method for acquiring and evaluating vascular examination data
Estelle Camus,Thomas Redel +1 more
TL;DR: In this article, the authors proposed a method for acquiring and evaluating vascular examination data, comprising: acquisition of IVUS images of a vessel to be examined using an IVUS catheter; simultaneous acquisition of angiography data of the OCT catheter having at least one marker; and determination of contours of the structures of the vessel under examination based on the OCT images.
Patent
Method for tomographically displaying a cavity by optical coherence tomography (OCT) and an OCT device for carrying out the method
TL;DR: In this article, a method for tomographically displaying a cavity by optical coherence tomography (OCT) and to an OCT device, wherein the path length of a measuring light beam in the catheter can change as a result of a movement of a catheter and brings about a change in the display scale, was described.
Patent
Three-dimensional co-registration between intravascular and angiographic data
TL;DR: In this paper, a method and appertaining system permit a co-registration between points in a 3D model of a vessel and vascular images obtained by an imaging catheter within the vessel at the respective points.
Patent
Synthetic data-driven hemodynamic determination in medical imaging
Lucian Mihai Itu,Tiziano Passerini,Saikiran Rapaka,Puneet Sharma,Chris Schwemmer,Max Schoebinger,Thomas Redel,Dorin Comaniciu +7 more
TL;DR: In this article, a machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data.