# Clinical image analysis to build patient-specific models of acute ischemic stroke patients

## TL;DR

This paper presents a method to build patient-specific models of acute ischemic stroke patients using clinical images and simulations to improve mechanical thrombectomy outcomes.

## Contribution

A semi-automatic pipeline for creating patient-specific clot models using clinical images and histological data for mechanical thrombectomy simulations.

## Key findings

- Image analysis could distinguish red and non-red clots but not white and mixed clots.
- Histological data were used to assign clot composition and mechanical properties in simulations.
- Patient-specific models showed strong similarity with pre-interventional clinical images.

## Abstract

Mechanical thrombectomy (MT) is an emergency treatment for acute ischemic stroke (AIS) to remove a clot occluding a large cerebral vessel. Histological analysis on retrieved thrombi have shown that they are mainly composed of red blood cells (RBCs), platelets and fibrin, and the outcome of MT appears to be influenced by clot composition. Therefore, being able to predict clot composition from routine medical images used for AIS diagnosis could support the choice of interventional strategy. Along with that, finite element simulations of the MT procedure can help provide insights into the impact of the procedural choices, the vessels morphology and the clot characteristics on the MT outcome. To achieve this, a realistic representation of the involved structures is necessary. In this context, this work aimed to (i) develop a methodology for the analysis of routine radiological images aiming at inferring information about clot characteristics (position, length, and composition) and (ii) develop a semi-automatic pipeline to position the clot in the patient-specific reconstructed geometry to build a patient-specific model which could be the starting point for the in silico replica of the MT procedure. However, image analysis alone could not distinguish between white and mixed clots, while a distinction between red and non-red clots was possible. Consequently, histological analyses were used to assign the clot composition, and thus the mechanical properties, in the positioning simulation. The resulting patient-specific model showed a strong similarity with pre-interventional clinical images.

The online version contains supplementary material available at 10.1007/s13246-025-01646-7.