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IUPUI

1. Khan, Md Monsurul Islam. Image Based Computational Hemodynamics for Non-Invasive and Patient-Specific Assessment of Arterial Stenosis.

Degree: 2019, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

While computed tomographic angiography (CTA) has emerged as a powerful noninvasive option that allows for direct visualization of arterial stenosis(AS), it cant assess the hemodynamic abnormality caused by an AS. Alternatively, trans-stenotic pressure gradient (TSPG) and fractional flow reserve (FFR) are well-validated hemodynamic indices to assess the ischemic severity of an AS. However, they have significant restriction in practice due to invasiveness and high cost. To fill the gap, a new computational modality, called InVascular has been developed for non-invasive quantification TSPG and/or FFR based on patient's CTA, aiming to quantify the hemodynamic abnormality of the stenosis and help to assess the therapeutic/surgical benefits of treatment for the patient. Such a new capability gives rise to a potential of computation aided diagnostics and therapeutics in a patient-specific environment for ASs, which is expected to contribute to precision planning for cardiovascular disease treatment. InVascular integrates a computational modeling of diseases arteries based on CTA and Doppler ultrasonography data, with cutting-edge Graphic Processing Unit (GPU) parallel-computing technology. Revolutionary fast computing speed enables noninvasive quantification of TSPG and/or FFR for an AS within a clinic permissible time frame. In this work, we focus on the implementation of inlet and outlet boundary condition (BC) based on physiological image date and and 3-element Windkessel model as well as lumped parameter network in volumetric lattice Boltzmann method. The application study in real human coronary and renal arterial system demonstrates the reliability of the in vivo pressure quantification through the comparisons of pressure waves between noninvasive computational and invasive measurement. In addition, parametrization of worsening renal arterial stenosis (RAS) and coronary arterial stenosis (CAS) characterized by volumetric lumen reduction (S) enables establishing the correlation between TSPG/FFR and S, from which the ischemic severity of the AS (mild, moderate, or severe) can be identified. In this study, we quantify TSPG and/or FFR for five patient cases with visualized stenosis in coronary and renal arteries and compare the non-invasive computational results with invasive measurement through catheterization. The ischemic severity of each AS is predicted. The results of this study demonstrate the reliability and clinical applicability of InVascular.

Advisors/Committee Members: Yu, Huidan, Wagner, Diane, Zhu, Likun.

Subjects/Keywords: computational fluid dynamics; arterial stenosis; non-invasive diagnosis; lumped parameter models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khan, M. M. I. (2019). Image Based Computational Hemodynamics for Non-Invasive and Patient-Specific Assessment of Arterial Stenosis. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/19906

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Khan, Md Monsurul Islam. “Image Based Computational Hemodynamics for Non-Invasive and Patient-Specific Assessment of Arterial Stenosis.” 2019. Thesis, IUPUI. Accessed August 24, 2019. http://hdl.handle.net/1805/19906.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Khan, Md Monsurul Islam. “Image Based Computational Hemodynamics for Non-Invasive and Patient-Specific Assessment of Arterial Stenosis.” 2019. Web. 24 Aug 2019.

Vancouver:

Khan MMI. Image Based Computational Hemodynamics for Non-Invasive and Patient-Specific Assessment of Arterial Stenosis. [Internet] [Thesis]. IUPUI; 2019. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/1805/19906.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khan MMI. Image Based Computational Hemodynamics for Non-Invasive and Patient-Specific Assessment of Arterial Stenosis. [Thesis]. IUPUI; 2019. Available from: http://hdl.handle.net/1805/19906

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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