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Proceedings Paper

Using flow feature to extract pulsatile blood flow from 4D flow MRI images
Author(s): Zhiqiang Wang; Ye Zhao; Whitney Yu; Xi Chen; Chen Lin; Stephen F. Kralik; Gary D. Hutchins
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Paper Abstract

4D flow MRI images make it possible to measure pulsatile blood flow inside deforming vessel, which is critical in accurate blood flow visualization, simulation, and evaluation. Such data has great potential to overcome problems in existing work, which usually does not reflect the dynamic nature of elastic vessels and blood flows in cardiac cycles. However, the 4D flow MRI data is often low-resolution and with strong noise. Due to these challenges, few efforts have been successfully conducted to extract dynamic blood flow fields and deforming artery over cardiac cycles, especially for small artery like carotid. In this paper, a robust flow feature, particularly the mean flow intensity is used to segment blood flow regions inside vessels from 4D flow MRI images in whole cardiac cycle. To estimate this flow feature more accurately, adaptive weights are added to the raw velocity vectors based on the noise strength of MRI imaging. Then, based on this feature, target arteries are tracked in at different time steps in a cardiac cycle. This method is applied to the clinical 4D flow MRI data in neck area. Dynamic vessel walls and blood flows are effectively generated in a cardiac cycle in the relatively small carotid arteries. Good image segmentation results on 2D slices are presented, together with the visualization of 3D arteries and blood flows. Evaluation of the method was performed by clinical doctors and by checking flow volume rates in the vertebral and carotid arteries.

Paper Details

Date Published: 24 February 2017
PDF: 10 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331O (24 February 2017); doi: 10.1117/12.2249500
Show Author Affiliations
Zhiqiang Wang, Kent State Univ. (United States)
Ye Zhao, Kent State Univ. (United States)
Whitney Yu, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Xi Chen, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Chen Lin, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Stephen F. Kralik, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Gary D. Hutchins, Indiana Univ.-Purdue Univ. Indianapolis (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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