A Combined In Vivo, In Vitro, In Silico Approach for Patient-Specific Haemodynamic Studies of Aortic Dissection

• Published in: Annals of biomedical engineering Vol. 48; no. 12; pp. 2950 - 2964
• Main Authors: Bonfanti, Mirko, Franzetti, Gaia, Homer-Vanniasinkam, Shervanthi, Díaz-Zuccarini, Vanessa, Balabani, Stavroula
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2020; Springer Nature B.V
• Subjects: Aged; Aorta; Aortic dissection; Aortic Dissection - diagnostic imaging; Aortic Dissection - physiopathology; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Blood flow; Boundary conditions; Classical Mechanics; Computational fluid dynamics; Computer applications; Coronary Circulation; Customization; Dissection; Flow velocity; Fluid dynamics; Hemodynamics; Humans; Hydrodynamics; In vivo methods and tests; Male; Mathematical models; Medical equipment; Medical innovations; Models, Cardiovascular; Numerical prediction; Original; Original Article; Particle image velocimetry; Patient-Specific Modeling; Patients; Tomography, X-Ray Computed; Velocity distribution; Waveforms; Aortic dissection; Particle image velocimetry; Computational fluid dynamics; Patient-specific; Pulsatile flow; Blood flow
• ISSN: 0090-6964; 1573-9686; 1573-9686
• DOI: 10.1007/s10439-020-02603-z

The optimal treatment of Type-B aortic dissection (AD) is still a subject of debate, with up to 50% of the cases developing late-term complications requiring invasive intervention. A better understanding of the patient-specific haemodynamic features of AD can provide useful insights on disease progression and support clinical management. In this work, a novel in vitro and in silico framework to perform personalised studies of AD, informed by non-invasive clinical data, is presented. A Type-B AD was investigated in silico using computational fluid dynamics (CFD) and in vitro by means of a state-of-the-art mock circulatory loop and particle image velocimetry (PIV). Both models not only reproduced the anatomical features of the patient, but also imposed physiologically-accurate and personalised boundary conditions. Experimental flow rate and pressure waveforms, as well as detailed velocity fields acquired via PIV, are extensively compared against numerical predictions at different locations in the aorta, showing excellent agreement. This work demonstrates how experimental and numerical tools can be developed in synergy to accurately reproduce patient-specific AD blood flow. The combined platform presented herein constitutes a powerful tool for advanced haemodynamic studies for a range of vascular conditions, allowing not only the validation of CFD models, but also clinical decision support, surgical planning as well as medical device innovation.