scholarly journals Using 3D Thin-Layer Model with in Vivo Patient-Specific Vessel Material Properties to Assesse Carotid Atherosclerotic Plaque Vulnerability

2019 ◽  
Vol 16 (s1) ◽  
pp. 81-82
Author(s):  
Qingyu Wang ◽  
Dalin Tang ◽  
Gador Canton ◽  
Zheyang Wu ◽  
Thomas S. Hatsukami ◽  
...  
Keyword(s):  
Thin Layer ◽  
Atherosclerotic Plaque ◽  
Material Properties ◽  
Patient Specific ◽  
Carotid Atherosclerotic Plaque ◽  
Plaque Vulnerability ◽  
Layer Model ◽  
Vessel Material

Impact of Patient-Specific In Vivo Vessel Material Properties on Carotid Atherosclerotic Plaque Stress/Strain Calculations

2019 ◽  
Vol 16 (03) ◽  
pp. 1842002 ◽  
Author(s):  
Qingyu Wang ◽  
Dalin Tang ◽  
Gador Canton ◽  
Thomas S. Hatsukami ◽  
Kristen L. Billiar ◽  
...  
Keyword(s):  
Material Properties ◽  
Carotid Plaque ◽  
Computational Models ◽  
Patient Specific ◽  
Stress And Strain ◽  
Carotid Atherosclerotic Plaque ◽  
Plaque Vulnerability ◽  
Material Models ◽  
Vessel Material

Patient-specific vessel material properties are in general lacking in image-based computational models. Carotid plaque stress and strain conditions with in vivo material and old material models were investigated (8 patients, 16 plaques). Plaque models using patient-specific in vivo vessel material properties showed significant differences from models using old material properties from the literature on stress and strain calculations. These differences demonstrated that models using in vivo material properties could improve the accuracy of stress and strain calculations which could potentially lead to more accurate plaque vulnerability assessment.

Abstract 562: Human Carotid Atherosclerosis Plaque Progression and Vessel Material Stiffness at Follow Up Had Positive Correlation: An in vivo Vessel Stiffness MRI Follow Up Study

2017 ◽  
Vol 37 (suppl_1) ◽  
Author(s):  
Qingyu Wang ◽  
Dalin Tang ◽  
Gador Canton ◽  
Jian Guo ◽  
Xiaoya Guo ◽  
...  
Keyword(s):  
Negative Correlation ◽  
Material Properties ◽  
Stretch Ratio ◽  
Patient Specific ◽  
Plaque Progression ◽  
Material Stiffness ◽  
Variation Rate ◽  
Vessel Material

It is hypothesized that artery stiffness may be associated with plaque progression. However, in vivo vessel material stiffness follow-up data is lacking in the literature. In vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) carotid plaque data were acquired from 8 patients with follow-up (18 months) with written informed consent obtained. Cine MRI and 3D thin-layer models were used to determine parameter values of the Mooney-Rivlin models for the 81slices from 16 plaques (2 scans/patient) using our established iterative procedures. Effective Young’s Modulus (YM) values for stretch ratio [1.0,1.3] were calculated for each slice for analysis. Stress-stretch ratio curves from Mooney-Rivlin models for the 16 plaques and 81 slices are given in Fig. 1. Average YM value of the 81 slices was 411kPa. Slice YM values varied from 70 kPa (softest) to 1284 kPa (stiffest), a 1734% difference. Average slice YM values by vessel varied from 109 kPa (softest) to 922 kPa (stiffest), a 746% difference. Location-wise, the maximum slice YM variation rate within a vessel was 306% (139 kPa vs. 564 kPa). Average slice YM variation rate within a vessel for the 16 vessels was 134%. Average variation of YM values from baseline (T1) to follow up (T2) for all patients was 61.0%. The range of the variation of YM values was [-28.4%, 215%]. For progression study, YM increase (YMI=YM T2 -TM T1 ) showed negative correlation with plaque progression measured by wall thickness increase (WTI), (r= -0.6802, p=0.0634). YM T2 showed strong negative correlation with WTI (r= -0.7764, p=0.0235). Correlation between YM T1 and WTI was not significant (r= -0.4353, p= 0.2811). Conclusion In vivo carotid vessel material properties have large variations from patient to patient, along the vessel segment within a patient, and from baseline to follow up. Use of patient-specific, location specific and time-specific material properties could potentially improve the accuracy of model stress/strain calculations.

Computational Plaque Vulnerability Index for Atherosclerotic Carotid Plaque Assessment Based on In Vivo MR-Image

2008 ◽  
Author(s):  
Zhongzhao Teng ◽  
Xueying Huang ◽  
Chun Yuan ◽  
Gador Canton ◽  
Fei Liu ◽  
...  
Keyword(s):  
Atherosclerotic Plaque ◽  
Carotid Plaque ◽  
Vulnerability Index ◽  
Carotid Atherosclerotic Plaque ◽  
Plaque Vulnerability ◽  
Mr Image ◽  
Noninvasive Methods ◽  
Screening And Diagnosis

Carotid atherosclerotic plaque (CAP) may rupture without warning and cause acute cardiovascular syndromes such as stroke, which is the No.3 killer in USA and a leading cause of serious disabilities. Available screening and diagnosis techniques are insufficient to identify those victims before the event occurs. Noninvasive methods to identify new and emerging biomarkers to assess plaque vulnerability and predict possible rupture before the fatal event are urgently called for.

Determination of Human Carotid Atherosclerotic Plaque Material Properties Non-Invasively Using In Vivo Cine and 3D Magnetic Resonance Imaging and Image-Based Modeling Techniques

2011 ◽  
Author(s):  
Haofei Liu ◽  
Gador Canton ◽  
Chun Yuan ◽  
Marina Ferguson ◽  
Chun Yang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Atherosclerotic Plaque ◽  
Material Properties ◽  
Computational Models ◽  
Plaque Rupture ◽  
Patient Specific ◽  
Material Strength ◽  
Modeling Techniques ◽  
Human Carotid

Atherosclerotic plaque rupture is believed to be associated with high critical stress exceeding plaque cap material strength. In vivo magnetic resonance image (MRI)-based computational models have been introduced to calculate critical plaque stress and assess plaque vulnerability [1–5]. However, accuracy of computational stress predictions is heavily dependent on the data used by the models. Patient-specific plaque material properties are desirable for accurate stress predictions but are not currently available. In this paper, non-invasive in vivo Cine and 3D multicontrast MRI data and modeling techniques were combined to obtain patient-specific plaque material properties to improve model prediction accuracies. A 2D human carotid plaque model was used to demonstrate impact of material stiffness on computational stress predictions.

Human Carotid Atherosclerotic Plaque Growth Function and Progression Simulation Using Meshless GFD and Statistical Methods Based on Multi-Year In Vivo MRI

2008 ◽  
Author(s):  
Chun Yang ◽  
Joseph D. Petruccelli ◽  
Zhongzhao Teng ◽  
Chun Yuan ◽  
Gador Canton ◽  
...  
Keyword(s):  
Atherosclerotic Plaque ◽  
Wall Thickness ◽  
Vessel Wall ◽  
Plaque Rupture ◽  
Patient Specific ◽  
Tracking Data ◽  
Plaque Progression ◽  
Vessel Wall Thickness ◽  
Plaque Growth

Atherosclerotic plaque rupture and progression have been the focus of intensive investigations in recent years. The mechanisms governing plaque progression and rupture process are not well understood. Using computational models based on patient-specific multi-year in vivo MRI data, our recent results indicated that 18 out of 21 patients studied showed significant negative correlation between plaque progression measured by vessel wall thickness increase (WTI) and plaque wall (structural) stress (PWS) [1]. In this paper, a computational procedure based on meshless generalized finite difference (MGFD) method and serial magnetic resonance imaging (MRI) data was introduced to simulate plaque progression. Participating patients were scanned three times (T1, T2, and T3, at intervals of approximately 18 months) to obtain plaque progression data. Vessel wall thickness (WT) changes were used as the measure for plaque progression. Starting from T2 plaque geometry, plaque progression was simulated by solving the solid model and adjusting wall thickness using plaque growth functions iteratively until time T3 is reached. Numerically simulated plaque progression showed very good agreement with actual plaque geometry at T3 given by MRI data. We believe this is the first time plaque progression simulation results based on multi-year patient-tracking data are reported. Multi-year tracking data and MRI-based progression simulation add time dimension to plaque vulnerability assessment and will improve prediction accuracy.

scholarly journals Relationship between ADAMTS4 and carotid atherosclerotic plaque vulnerability in humans

2018 ◽  
Vol 67 (4) ◽  
pp. 1120-1126 ◽  
Author(s):  
Honglin Dong ◽  
Tian Du ◽  
Shyamal Premaratne ◽  
Cynthia X. Zhao ◽  
Qinqin Tian ◽  
...  
Keyword(s):  
Atherosclerotic Plaque ◽  
Carotid Atherosclerotic Plaque ◽  
Plaque Vulnerability

scholarly journals Can we obtain in vivo transmural mean hoop stress of the aortic wall without knowing patient-specific material properties and residual deformations?

2018 ◽  
Author(s):  
Minliang Liu ◽  
Liang Liang ◽  
Haofei Liu ◽  
Ming Zhang ◽  
Caitlin Martin ◽  
...  
Keyword(s):  
Material Properties ◽  
Hoop Stress ◽  
Aortic Wall ◽  
Patient Specific ◽  
Membrane Stress ◽  
Penalty Approach ◽  
Thin Walled ◽  
Specific Material ◽  
Residual Deformations

AbstractIt is well known that residual deformations/stresses alter the mechanical behavior of arteries, e.g. the pressure-diameter curves. In an effort to enable personalized analysis of the aortic wall stress, approaches have been developed to incorporate experimentally-derived residual deformations into in vivo loaded geometries in finite element simulations using thick-walled models. Solid elements are typically used to account for “bending-like” residual deformations. Yet, the difficulty in obtaining patient-specific residual deformations and material properties has become one of the biggest challenges of these thick-walled models. In thin-walled models, fortunately, static determinacy offers an appealing prospect that allows for the calculation of the thin-walled membrane stress without patient-specific material properties. The membrane stress can be computed using forward analysis by enforcing an extremely stiff material property as penalty treatment, which is referred to as the forward penalty approach. However, thin-walled membrane elements, which have zero bending stiffness, are incompatible with the residual deformations, and therefore, it is often stated as a limitation of thin-walled models. In this paper, by comparing the predicted stresses from thin-walled models and thick-walled models, we demonstrate that the transmural mean hoop stress is the same for the two models and can be readily obtained from in vivo clinical images without knowing the patient-specific material properties and residual deformations. Computation of patient-specific mean hoop stress can be greatly simplified by using membrane model and the forward penalty approach, which may be clinically valuable.

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