Image-based Computational Approaches for Personalized Cardiovascular Medicine: Improving Clinical Applicability and Reliability through Medical Imaging and Experimental Data

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Original Research
23 February 2023
Modeling flow in an in vitro anatomical cerebrovascular model with experimental validation
Saurabh Bhardwaj
4 more and 
Keefe B. Manning
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Acute ischemic stroke (AIS) is a leading cause of mortality that occurs when an embolus becomes lodged in the cerebral vasculature and obstructs blood flow in the brain. The severity of AIS is determined by the location and how extensively emboli become lodged, which are dictated in large part by the cerebral flow and the dynamics of embolus migration which are difficult to measure in vivo in AIS patients. Computational fluid dynamics (CFD) can be used to predict the patient-specific hemodynamics and embolus migration and lodging in the cerebral vasculature to better understand the underlying mechanics of AIS. To be relied upon, however, the computational simulations must be verified and validated. In this study, a realistic in vitro experimental model and a corresponding computational model of the cerebral vasculature are established that can be used to investigate flow and embolus migration and lodging in the brain. First, the in vitro anatomical model is described, including how the flow distribution in the model is tuned to match physiological measurements from the literature. Measurements of pressure and flow rate for both normal and stroke conditions were acquired and corresponding CFD simulations were performed and compared with the experiments to validate the flow predictions. Overall, the CFD simulations were in relatively close agreement with the experiments, to within ±7% of the mean experimental data with many of the CFD predictions within the uncertainty of the experimental measurement. This work provides an in vitro benchmark data set for flow in a realistic cerebrovascular model and is a first step towards validating a computational model of AIS.

2,706 views
9 citations
2,188 views
9 citations
Original Research
05 January 2023

Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. AAAs often remain asymptomatic until they are either close to rupturing or they cause pressure to the spine and/or other organs. Fast progression has been linked to future clinical outcomes. Therefore, a reliable and efficient system to quantify geometric properties and growth will enable better clinical prognoses for aneurysms. Different imaging systems can be used to locate and characterize an aneurysm; computed tomography (CT) is the modality of choice in many clinical centers to monitor later stages of the disease and plan surgical treatment. The lack of accurate and automated techniques to segment the outer wall and lumen of the aneurysm results in either simplified measurements that focus on few salient features or time-consuming segmentation affected by high inter- and intra-operator variability. To overcome these limitations, we propose a model for segmenting AAA tissues automatically by using a trained deep learning-based approach. The model is composed of three different steps starting with the extraction of the aorta and iliac arteries followed by the detection of the lumen and other AAA tissues. The results of the automated segmentation demonstrate very good agreement when compared to manual segmentation performed by an expert.

4,155 views
13 citations
1,709 views
8 citations
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3,214 views
4 citations
Evolution of the descending aorta false lumen surface due to thrombus formation in (A) simplified model and (B) original model. Time points presented are after (a) 6, (b) 9, (c) 12, (d) 15, and (e) 20 cardiac cycles. Figure adapted from Armour et al. (2020b).
Original Research
26 October 2022
Shear-driven modelling of thrombus formation in type B aortic dissection
Alireza Jafarinia
3 more and 
Thomas Hochrainer

Background: Type B aortic dissection (TBAD) is a dangerous pathological condition with a high mortality rate. TBAD is initiated by an intimal tear that allows blood to flow between the aortic wall layers, causing them to separate. As a result, alongside the original aorta (true lumen), a false lumen (FL) develops. TBAD compromises the whole cardiovascular system, in the worst case resulting in complete aortic rupture. Clinical studies have shown that dilation and rupture of the FL are related to the failure of the FL to thrombose. Complete FL thrombosis has been found to improve the clinical outcomes of patients with chronic TBAD and is the desired outcome of any treatment. Partial FL thrombosis has been associated with late dissection-related deaths and the requirement for re-intervention, thus the level of FL thrombosis is dominant in classifying the risk of TBAD patients. Therefore, it is important to investigate and understand under which conditions complete thrombosis of the FL occurs.

Method: Local FL hemodynamics play an essential role in thrombus formation and growth. In this study, we developed a simplified phenomenological model to predict FL thrombosis in TBAD under physiological flow conditions. Based on an existing shear-driven thrombosis model, a comprehensive model reduction study was performed to improve computational efficiency. The reduced model has been implemented in Ansys CFX and applied to a TBAD case following thoracic endovascular aortic repair (TEVAR) to test the model. Predicted thrombus formation based on post-TEVAR geometry at 1-month was compared to actual thrombus formation observed on a 3-year follow-up CT scan.

Results: The predicted FL status is in excellent agreement with the 3-year follow-up scan, both in terms of thrombus location and total volume, thus validating the new model. The computational cost of the new model is significantly lower than the previous thrombus model, with an approximate 65% reduction in computational time. Such improvement means the new model is a significant step towards clinical applicability.

Conclusion: The thrombosis model developed in this study is accurate and efficient at predicting FL thrombosis based on patient-specific data, and may assist clinicians in choosing individualized treatments in the future.

2,835 views
10 citations

Background: A clinical study comparing the hemodynamic outcomes of transcatheter mitral valve replacement (TMVR) with vs. without Laceration of the Anterior Mitral leaflet to Prevent Outflow Obstruction (LAMPOON) has never been designed nor conducted.

Aims: To quantify the hemodynamic impact of LAMPOON in TMVR using patient-specific computational (in silico) models.

Materials: Eight subjects from the LAMPOON investigational device exemption trial were included who had acceptable computed tomography (CT) data for analysis. All subjects were anticipated to be at prohibitive risk of left ventricular outflow tract (LVOT) obstruction from TMVR, and underwent successful LAMPOON immediately followed by TMVR. Using post-procedure CT scans, two 3D anatomical models were created for each subject: (1) TMVR with LAMPOON (performed procedure), and (2) TMVR without LAMPOON (virtual control). A validated computational fluid dynamics (CFD) paradigm was then used to simulate the hemodynamic outcomes for each condition.

Results: LAMPOON exposed on average 2 ± 0.6 transcatheter valve cells (70 ± 20 mm2 total increase in outflow area) which provided an additional pathway for flow into the LVOT. As compared to TMVR without LAMPOON, TMVR with LAMPOON resulted in lower peak LVOT velocity, lower peak LVOT gradient, and higher peak LVOT effective orifice area by 0.4 ± 0.3 m/s (14 ± 7% improvement, p = 0.006), 7.6 ± 10.9 mmHg (31 ± 17% improvement, p = 0.01), and 0.2 ± 0.1 cm2 (17 ± 9% improvement, p = 0.002), respectively.

Conclusion: This was the first study to permit a quantitative, patient-specific comparison of LVOT hemodynamics following TMVR with and without LAMPOON. The LAMPOON procedure achieved a critical increment in outflow area which was effective for improving LVOT hemodynamics, particularly for subjects with a small neo-left ventricular outflow tract (neo-LVOT).

3,226 views
3 citations
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