Use of 3D rotational angiography to perform computational fluid dynamics and virtual interventions in aortic coarctation

Armstrong, Aimee K.; Zampi, Jeffrey D.; Itu, Lucian Mihai; Benson, Leland N.

doi:10.1002/ccd.28507

“…These data are promising as this approach would allow a better noninvasive assessment and could help in identifying patients who could benefit from reintervention. Similarly, in a case series, Armstrong et al [18] combined computational fluid dynamics with 3D rotational angiography and demonstrated the feasibility and validation of hemodynamic measurements. These studies demonstrate that pressure gradients and flow dynamics can be accurately modeled noninvasively.…”

Section: The Importance Of Size and Arch Geometrymentioning

confidence: 95%

Challenges in diagnosis and management of coarctation of the aorta

Chetan

¹

,

Mertens

²

2021

Current Opinion in Cardiology

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Purpose of reviewCoarctation of the aorta remains a controversial topic with uncertainties in long-term outcomes. Recent findingsRecent advances in fetal imaging including echocardiography and MRI offer novel opportunities for better detection and prediction of the need for neonatal intervention. New imaging techniques are providing novel insights about the impact of arch geometry and size on flow dynamics and pressure gradients. The importance of arch size rather than shape for optimal hemodynamics has been identified. Long-term outcome data suggest a significant increase in mortality risk in coarctation patients beyond the third decade when compared with the general population. Hypertension is highly prevalent not only in adult patients following repair of coarctation but also in normotensive patients presenting with LV diastolic dysfunction and adverse remodelling, indicating that abnormal vascular properties are important. Patients with coarctation undergoing neonatal repair are at risk for adverse neurodevelopmental outcomes and patients could benefit from timely neurocognitive evaluation and intervention.

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“…Computational modelling may also be used to predict the outcome of cardiovascular interventions, allowing for the evaluation of various treatment approaches and for the selection of the optimal treatment. A framework was proposed that combines CFD and ML based techniques for robustly and automatically personalizing aortic hemodynamic computations for the assessment of pre-and post-intervention aortic coarctation (CoA) patients from 3D rotational angiography (3DRA) data (Armstrong et al, 2019). The key features are: (i) a parameter estimation method for calibrating arterial wall properties, and inlet and outlet boundary conditions, to obtain computational results which match the patient-specific measurements, and (ii) a machine learning based pressure drop model which predicts pressure losses accurately for large variations of anatomical CoA models and flow conditions (Figure 7).…”

Section: Treatment Planning In Aortic Coarctationmentioning

confidence: 99%

“…The key features are: (i) a parameter estimation method for calibrating arterial wall properties, and inlet and outlet boundary conditions, to obtain computational results which match the patient-specific measurements, and (ii) a machine learning based pressure drop model which predicts pressure losses accurately for large variations of anatomical CoA models and flow conditions (Figure 7). The case series paper provided, besides a feasibility assessment, an initial validation of the framework against invasive measurements in three patients: a difference of less (Armstrong et al, 2019) Privacy-Preserving and Explainable AI for Cardiovascular Imaging than 5mmHg between computed and measured peak-to-peak trans-coarctation pressure drop was obtained for both the pre-and the virtual postoperative assessment.…”

Section: Treatment Planning In Aortic Coarctationmentioning

confidence: 99%

“…Figure 7. Physics based workflow for aortic coarctation assessment from 3DRA: (a) 3DRA (the arrow indicates the aortic coarctation), (b) aortic segmentation, (c) reconstructed 3D anatomical model, (d) definition of boundary conditions, (e) pre-stent hemodynamic results, (f) implantation of virtual stent, and (g) post-stent hemodynamic results(Armstrong et al, 2019) …”

mentioning

confidence: 99%

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Privacy-Preserving and Explainable AI for Cardiovascular Imaging

Puiu¹,

Vizitiu²,

Nita³

et al. 2021

STUD INFORM CONTROL

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Medical imaging provides valuable input for managing cardiovascular disease (CVD), ranging from risk assessment to diagnosis, therapy planning and follow-up. Artificial intelligence (AI) based medical image analysis algorithms provide nowadays state-of-the-art results in CVD management, mainly due to the increase in computational power and data storage capacities. Various challenges remain to be addressed to speed-up the adoption of AI based solutions in routine CVD management. Although medical imaging and in general health data are abundant, the access and transfer of such data is difficult to realize due to ethical considerations. Hence, AI algorithms are often trained on relatively small datasets, thus limiting their robustness, and potentially leading to biased or skewed results for certain patient or pathology sub-groups. Furthermore, explainability and interpretability have become core requirements for AI algorithms, to ensure that the rationale behind output inference can be revealed. The paper focuses on recent developments related to these two challenges, discusses the clinical impact of proposed solutions, and provides conclusions for further research and development. It also presents examples related to the diagnosis of stable coronary artery disease, a whole-body circulation model for the assessment of structural heart disease, and to the diagnosis and treatment planning of aortic coarctation, a congenital heart disease.

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“…This method provided us with a better way of understanding airway morphology and physiology [17][18][19]. CFD is also widely used in the prediction and analysis of coronary artery stenosis, myocardial infarction, salivolithiasis, and ureteral diseases, as well as several other conditions [20][21][22][23]. The growing number of CFD studies on the nasal passages has led to a better understanding of the nasal airway.…”

Section: Introductionmentioning

confidence: 99%

Computational Fluid Dynamics Analysis of Nasal Airway Changes after Treatment with C-Expander

Wang

¹

,

Liu

²

,

Li

³

et al. 2021

Applied Bionics and Biomechanics

1

0

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The use of the C-expander is an effective treatment modality for maxillary skeletal deficiencies which can cause ailments and significantly reduce life expectancy in late adolescents and young adults. However, the morphological and dynamic effects on the nasal airway have not been reported. The main goal of this study was to evaluate the nasal airway changes after the implementation of a C-expander. A sample of nine patients (8 females, 1 male, age range from 15 to 29 years) was included. The morphology parameters and nasal airway ventilation parameters of pretreatment and posttreatment were measured. All study data were normally distributed. A paired t -test was used to evaluate the changes before and after treatment. After expansion, the mean and standard deviation values of intercanine maxillary width (CMW) and intermolar maxillary width (MMW) increased from 35.75 ± 2.48 mm and 54.20 ± 3.17 mm to 37.87 ± 2.26 mm ( P < 0.05 ) and 56.65 ± 3.10 mm ( P < 0.05 ), respectively. The nasal cavity volume increased from 20320.00 ± 3468.25 mm3 to 23134.70 ± 3918.84 mm3 ( P < 0.05 ). The nasal pressure drop decreased from 36.34 ± 3.99 Pa to 30.70 ± 3.17 Pa ( P < 0.05 ), while the value of the maximum velocity decreased from 6.50 ± 0.31 m/s to 5.85 ± 0.37 m/s ( P < 0.05 ). Nasal resistance dropped remarkably from 0.16 ± 0.14 Pa/ml/s to 0.08 ± 0.06 Pa/ml/s ( P < 0.05 ). The use of C-expander can effectively broaden the area and volume of the nasal airway, having a positive effect in the reduction of nasal resistance and improvement of nasal airway ventilation. For patients suffering from maxillary width deficiency and respiratory disorders, a C-expander may be an alternative method to treat the disease.

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Use of 3D rotational angiography to perform computational fluid dynamics and virtual interventions in aortic coarctation

Cited by 19 publications

References 16 publications

Challenges in diagnosis and management of coarctation of the aorta

Challenges in diagnosis and management of coarctation of the aorta

Privacy-Preserving and Explainable AI for Cardiovascular Imaging

Computational Fluid Dynamics Analysis of Nasal Airway Changes after Treatment with C-Expander

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