DiscoClots

Nonlinear mechanical characterisation of blood clots (DiscoClots)

Description:

Acute Ischemic stroke is one of a leading cause of death and long-term disability globally. It is caused when a blood clot is stuck in an artery, restricting the flow of blood and oxygen. Mechanical thrombectomy (MT) has become the standard of care. In MT, the clot is physically removed using the help of a catheter-based device, yet success remains highly variable and strongly dependent on the mechanical behaviors of the clot itself. Clots are heterogeneous, viscoelastic and composition-dependent material, and the constitutive laws that govern their behaviour are still poorly understood. This gap limits new device development and procedural planning on using the right type of device. 

Scientific Machine Learning (SciML) has emerged as a powerful framework for discovering governing equations directly from the data (experimental as well as synthetic). It combines the interpretability of physics-based models with the capabilities of data-driven methods. The aim of this project is to prepare clot analogues from human blood, perform mechanical testing on the analogues to characterize their behaviour under different types of deformation and develop a SciML based framework to discover the underlying constitutive law from the data generated. The long-term goal of this project is to improve device design and guide the surgeon on choosing the right device for the right clot type

Lead Investigator: Yash Bhandakkar

Funded by:

Taighde Éireann – Research Ireland #13/RC/2073_P2 and Horizon Europe #101126640