2001
DOI: 10.1007/3-540-45468-3_54
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A Software Framework for Creating Patient Specific Geometric Models from Medical Imaging Data for Simulation Based Medical Planning of Vascular Surgery

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Cited by 116 publications
(105 citation statements)
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“…1). 20 Using the computer model, a computational fluid dynamics (CFD) analysis was performed to simulate blood flow and blood pressure. 21,22 In this analysis, additional patient-specific flow data [cine phase-contrast magnetic resonance imaging (PC-MRI)] at the supraceliac and infrarenal levels and pressure data (brachial pressure cuff measurements acquired immediately after the scan) were considered in order to model the baseline flow and pressure conditions in this patient as accurately as possible.…”
Section: Methodsmentioning
confidence: 99%
“…1). 20 Using the computer model, a computational fluid dynamics (CFD) analysis was performed to simulate blood flow and blood pressure. 21,22 In this analysis, additional patient-specific flow data [cine phase-contrast magnetic resonance imaging (PC-MRI)] at the supraceliac and infrarenal levels and pressure data (brachial pressure cuff measurements acquired immediately after the scan) were considered in order to model the baseline flow and pressure conditions in this patient as accurately as possible.…”
Section: Methodsmentioning
confidence: 99%
“…Each volume of MRA data was corrected by using proprietary software (GE Medical Systems, Milwaukee, WI) for known gradient nonlinearities during acquisition that can cause distortion in the slice direction (4). Three-dimensional, subject-specific solid models of the aorta and its major branches (celiac, superior mesenteric, and renal arteries) were created from the MRA images using custom software (41) and discretized by using a commercially available, automatic mesh generation program (MeshSim, Simmetrix, Clifton Park, NY). Figure 1 summarizes the model construction procedure from MR data to creating a finite element mesh.…”
Section: Methodsmentioning
confidence: 99%
“…Network compression was enhanced by getting close to walls. Also in the location of grafting (bypass input and output) and blockage location (area contraction) network compression was utilized more 14 . Figure 2 shows the sample of used network in following study.…”
Section: Methodsmentioning
confidence: 99%