Measurement of Threshold Image Intensities on Difference of Vascular Model: Effect on Computational Fluid Dynamics for Patient-Specific Cerebral Aneurysm

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Abstract:

Threshold image intensity for patient vascular models is determined instinctively. In this study, we used the simple method of threshold resolve to evaluate the effect of threshold image intensity level on computational fluid dynamics (CFD) of patient-specific cerebral aneurysm. This investigation involved five patients with internal carotid aneurysms collected in retrospect between August 2010 and October 2012. In 3-dimensional rotational angiography with digital subtraction angiography (DSA) image data, we set five straight line probe across the parent of the internal carotid artery and deliberate the average profile curve of the image intensity along this line. To determine the threshold image intensity level objectively, we calculated the threshold coefficient Cthre using this profile curve. The effect of Cthre value on vascular model configuration and the wall shear stress (WSS) distribution of the aneurysm was evaluated. Result shows for the inlet area and volume of the vascular model decreased and WSS increased according to the Cthre value increase. In one stage, the pattern of WSS distribution changed strangely and the threshold image intensity level can result reflective effects on CFD. This finding necessary to advance a further understanding of problems in image segmentation and solving the patient-specific aneurysm problems.

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55-59

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May 2016

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© 2016 Trans Tech Publications Ltd. All Rights Reserved

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[1] Antiga, L., Piccinelli, M., Botti, L., Ene-Iordache, B., Remuzzi, A., Steinman, D.A., 2008. An image-based modeling framework for patient-specific computational hemodynamics. Medical and Biological Engineering and Computing 46, 1097–1112.

DOI: 10.1007/s11517-008-0420-1

Google Scholar

[2] Adib, M.A.H.M., Hasni, N.H.M., 2015. Effect on the reconstruction of blood vessel geometry to the thresholds image intensity level for patient aneurysm. Journal of Biominetics, Biomaterials and Biomedical Engineering, 22, 89-95.

DOI: 10.4028/www.scientific.net/jbbbe.22.89

Google Scholar

[3] Boussel, L., Rayz, V., McCulloch, C., Martin, A., Acevedo-Bolton, G., Lawton, M., Higashida, R., Smith, W.S., Young, W.L., Saloner, D., 2008. Aneurysm Growth Occurs at Region of Low Wall Shear Stress Patient-Specific Correlation of Hemodynamics and Growth in a Longitudinal Study Stroke 39, 2997-3002.

DOI: 10.1161/strokeaha.108.521617

Google Scholar

[4] Cebral, J.R., Mut, F., Weir, J., Putman, C., 2011. Quantitative Characterization of the Hemodynamic Environment in Ruptured and Unruptured Brain Aneurysms, AJNR Am J Neuroradiology 32, 145-151.

DOI: 10.3174/ajnr.a2419

Google Scholar

[5] Cebral. J., Castro, M., Burgess, J., Pergolizzi, R., Sheridan, M., Putman, C., 2005. Characterization of cerebral aneurysms for assessing risk of rupture by using patient-specific computational hemodynamics models. Am J Neuroradiol 26, 2550–2559.

Google Scholar

[6] Chang., H.H., Duckwiler, G.R., Valentine, D.J., Chu, W.C. 2009. Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for compu- tational fluid dynamics modeling. Medical Physics 36, 5612–5621.

DOI: 10.1118/1.3260841

Google Scholar

[7] Hassan, T., Timofeev, E.V., Saito, T., Shimizu, H., Ezura, M., Matsumoto, Y., Takayama, K., Tominaga, T., Takahashi, A., 2005. A proposed parent vessel geometry-based categorization of saccular intracranial aneurysms: computa- tional flow dynamics analysis of the risk factors for lesion rupture. Journal of Neurosurgery 103, 662–680.

DOI: 10.3171/jns.2005.103.4.0662

Google Scholar

[8] Hoi, Y., Meng, H., Woodward, S.H., Bendok, B.R., Hanel, R.A., Guterman, L.R., Hopkins, L.N., 2004. Effects of arterial geometry on aneurysm growth: three- dimensional computational fluid dynamics study. Journal of Neurosurgery 101, 676–681.

DOI: 10.3171/jns.2004.101.4.0676

Google Scholar

[9] Ishikawa, T., Guimaraes, L.F.R., Oshima, S., Yamane, R., 1998. Effect of non-Newtonian property of blood on flow through stenosed tube. Fluid Dynamics Research 22, 251-264.

DOI: 10.1016/s0169-5983(97)00041-5

Google Scholar

[10] Jou, L.D., Lee, D.H., Morsi, H., Mawad, M.E., 2008. Wall shear stress on ruptured and unruptured intracranial aneurysms at the internal carotid artery, AJNR Am J Neuroradiology 29, 1761-1767.

DOI: 10.3174/ajnr.a1180

Google Scholar

[11] Moore, J.A., Steinman, D.A., Ethier, C.R., 1997. Computational blood flow model- ling: errors associated with reconstructing finite element models from magnetic resonance images. Journal of Biomechanics 31, 179–184.

DOI: 10.1016/s0021-9290(97)00125-5

Google Scholar

[12] Yim, P.J., Vasbinder, B., Ho, V.B., Choyke, P.L. 2002. A deformable isosurface and vascular applications. Progress in biomedical optics and imaging 3, 1390–1397.

DOI: 10.1117/12.467104

Google Scholar