Taeouk Kim
Texas A&M University
5 Papers
6 Citations
Taeouk Kim is an academic researcher from Texas A&M University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 1, co-authored 2 publications.
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Papers
A hybrid echocardiography-CFD framework for ventricular flow simulations.
Mohammadali Hedayat,Tatsat R. Patel,Taeouk Kim,Marek Belohlavek,Kenneth R. Hoffmann,Iman Borazjani +5 more
TL;DR: 3D reconstruction of echo from a baseline LV and one after inducing acute myocardial ischemia (AMI) shows a larger energy loss for a LV with AMI compared to the baseline one, while flow simulations are validated against the Doppler ultrasound velocity measurements.
12
A Computational Study of Dynamic Obstruction in Type B Aortic Dissection.
Taeouk Kim,Pieter van Bakel,Nitesh Nama,Nicholas S. Burris,Himanshu J. Patel,C. Alberto Figueroa +5 more
TL;DR: In this article , the authors developed a computational model to investigate biomechanical and hemodynamical factors involved in dynamic obstruction of the true lumen (TL) in aortic dissection.
4
A learning-based, region of interest-tracking algorithm for catheter detection in echocardiography
Taeouk Kim,Mohammadali Hedayat,Veronica V. Vaitkus,Marek Belohlavek,Vinayak R. Krishnamurthy,Iman Borazjani +5 more
TL;DR: In this article , a convolutional neural network (CNN) model was trained to detect the region of interest (ROI), the interior of the left ventricle, containing the catheter tip.
2
Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks
Taeouk Kim,Mohammadali Hedayat,Veronica V. Vaitkus,Marek Belohlavek,Vinayak R. Krishnamurthy,Iman Borazjani +5 more
TL;DR: Evaluating the performance of the state-of-the-art convolutional neural networks for the segmentation of 2D echo images from 6 standard projections of the LV showed that both CNN models achieve higher performance on LV segmentation than the level-set method.
Three-Dimensional Characterization of Aortic Root Motion by Vascular Deformation Mapping
Taeouk Kim,Nic S. Tjahjadi,Xuehuan He,Joost A. van Herwaarden,Himanshu J. Patel,Nicholas S. Burris,C. Alberto Figueroa +6 more
TL;DR: In this article , a vascular deformation mapping (VDM) was used to extract 3D aortic root motion from dynamic computed tomography angiography images and compared for four different subject groups: non-aneurysmal, TAA, Marfan, and repair.