Research Papers

Divergence Compensatory Optical Flow Method for Blood Velocimetry

[+] Author and Article Information
Zifeng Yang

Department of Mechanical and Materials
Wright State University,
3640 Colonel Glenn Highway,
Dayton, OH 45435
e-mail: zifeng.yang@wright.edu

Hongtao Yu

Department of Mechanical and Materials
Wright State University,
Dayton, OH 45435
e-mail: yu.41@wright.edu

George P. Huang

Fellow ASME
Department of Mechanical and Materials
Wright State University,
Dayton, OH 45435
e-mail: george.huang@wright.edu

Bryan Ludwig

Boonshoft School of Medicine,
Wright State University,
Dayton, OH 45435;
Division of NeuroInterventional Surgery,
Department of Neurology,
Premier Health—Clinical Neuroscience Institute,
Wright State University,
30 E. Apple Street,
Dayton, OH 45409
e-mail: brludwig@PremierHealth.com

1Corresponding author.

Manuscript received August 23, 2016; final manuscript received April 6, 2017; published online April 25, 2017. Assoc. Editor: Keefe B. Manning.

J Biomech Eng 139(6), 061005 (Apr 25, 2017) (14 pages) Paper No: BIO-16-1350; doi: 10.1115/1.4036484 History: Received August 23, 2016; Revised April 06, 2017

Detailed blood velocity map in the vascular system can be obtained by applying the optical flow method (OFM) in processing fluoroscopic digital subtracted catheter angiographic images; however, there are still challenges with the accuracy of this method. In the present study, a divergence compensatory optical flow method (DC-OFM), in which a nonzero divergence of velocity is assumed due to the finite resolution of the image, was explored and applied to the digital subtraction angiography (DSA) images of blood flow. The objective of this study is to examine the applicability and evaluate the accuracy of DC-OFM in assessing the blood flow velocity in vessels. First, an Oseen vortex flow was simulated on the standard particle image to generate an image pair. Then, the DC-OFM was applied on the particle image pair to recover the velocity field for validation. Second, DSA images of intracranial arteries were used to examine the accuracy of the current method. For each set of images, the first image is the in vivo DSA image, and the second image is generated by superimposing a given flow field. The recovered velocity map by DC-OFM agrees well with the exact velocity for both the particle images and the angiographic images. In comparison with the traditional OFM, the present method can provide more accurate velocity estimation. The accuracy of the velocity estimation can also be improved by implementing preprocess techniques including image intensification, Gaussian filtering, and “image-shift.”

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Grahic Jump Location
Fig. 3

(a) Filtered original PIV particle image with γ=13.4, Δt=0.02s and (b) created particle image after superimposing the Oseen vortex with Δt=0.02 s

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Fig. 2

(a) Original DSA image containing an unruptured aneurysm and (b) cropped image of the aneurysm with additional phantom parent arteries

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Fig. 1

(a) DSA image of the cerebral arteries with a coiled aneurysm and (b) cropped image focusing on a curving section of ICA

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Fig. 4

Map of the velocity distribution recovered from the particle image pair: (a) OFM results of velocity field for γ=13.4, Δt=0.02s, (b) exact velocity field, (c) streamlines of OFM results, and (d) exact streamlines

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Fig. 5

Comparison of the x-component of velocities across the vortex cores extracted from the velocity distribution using different methods

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Fig. 6

Smoothed particle images and corresponding velocity distribution results (a) image with mask size and standard deviation of s1=12,σ1=4.8,s2=5,σ2=2.0, and with γ=2.9, (b) velocity results corresponding to case (a), (c) image with mask size and standard deviation of s1=8,σ1=3.2,s2=3,σ2=1.2 with γ=6.1, (d) velocity results corresponding to case (c), (e) image with mask size and standard deviation of s1=5,σ1=2.0,s2=3,σ2=1.2 with γ=9.3, (f) velocity results corresponding to case (e), (g) image with mask size and standard deviation of s1=3,σ1=1.2 with γ=17.2, and (h) velocity results corresponding to case (g)

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Fig. 7

Velocity results for image pairs generated by varied time interval with a fixed γ=13.4 : (a) Δt  = 0.2 s, (b) Δt  = 0.1 s, (c) Δt  = 0.05 s, and (d) Δt  = 0.01 s

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Fig. 8

(a) Cropped DSA image focusing on the ICA, (b) CFD mesh for blood flow simulation, and (c) CFD simulation results of the flow field used for generating the synthetic image

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Fig. 9

(a) Cropped DSA image focusing on the aneurysm (black lines were added to generate the phantom artery boundary to enable the CFD simulation), (b) CFD mesh, and (c) CFD simulation results of the flow field

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Fig. 10

(a) Cropped DSA image of the curving ICA, (b) smoothed intensified image, and (c) the second image generated by superimposing the flow field into the image (b)

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Fig. 11

(a) Exact velocity distribution from CFD, (b) recovered velocity using the present DC-OFM method, (c) error distribution of velocity magnitude, and (d) velocity profile comparison at five cross sections

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Fig. 12

(a) DSA image of the cerebral arteries with an uncoiled aneurysm, (b) cropped image focusing on the aneurysm, and (c) intensified aneurysm image (β=2.3)

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Fig. 13

(a) Exact velocity distribution in the aneurysm from CFD, (b) recovered velocity distribution by using DC-OFM, (c) error distribution of velocity magnitude, and (d) comparison of velocity profiles at five cross sections




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