Intraluminal Thrombus Characteristics in AAA Patients: Non-Invasive Diagnosis Using CFD.
Djelloul BelkacemiMiloud Tahar AbbesMohammad Al-RawiAhmed M Al-JumailySofiane BacheneBoualem LaribiPublished in: Bioengineering (Basel, Switzerland) (2023)
Abdominal aortic aneurysms (AAA) continue to pose a high mortality risk despite advances in medical imaging and surgery. Intraluminal thrombus (ILT) is detected in most AAAs and may critically impact their development. Therefore, understanding ILT deposition and growth is of practical importance. To assist in managing these patients, the scientific community has been researching the relationship between intraluminal thrombus (ILT) and hemodynamic parameters wall shear stress (WSS) derivatives. This study analyzed three patient-specific AAA models reconstructed from CT scans using computational fluid dynamics (CFD) simulations and a pulsatile non-Newtonian blood flow model. The co-localization and relationship between WSS-based hemodynamic parameters and ILT deposition were examined. The results show that ILT tends to occur in regions of low velocity and time-averaged WSS (TAWSS) and high oscillation shear index (OSI), endothelial cell activation potential (ECAP), and relative residence time (RRT) values. ILT deposition areas were found in regions of low TAWSS and high OSI independently of the nature of flow near the wall characterized by transversal WSS (TransWSS). A new approach is suggested which is based on the estimation of CFD-based WSS indices specifically in the thinnest and thickest ILT areas of AAA patients; this approach is promising and supports the effectiveness of CFD as a decision-making tool for clinicians. Further research with a larger patient cohort and follow-up data are needed to confirm these findings.
Keyphrases
- end stage renal disease
- ejection fraction
- blood flow
- newly diagnosed
- chronic kidney disease
- randomized controlled trial
- decision making
- computed tomography
- healthcare
- mass spectrometry
- palliative care
- acute coronary syndrome
- case report
- magnetic resonance imaging
- endothelial cells
- machine learning
- atrial fibrillation
- patient reported
- electronic health record
- artificial intelligence
- big data
- coronary artery bypass
- pet ct