MRI based diffusion and perfusion predictive model to estimate stroke evolution

Area of Science:

• Neurology
• Medical Imaging
• Computational Biology

Background:

• Stroke is a leading cause of disability, necessitating accurate prediction of infarct evolution for timely treatment.
• Current methods for predicting infarct progression can be time-consuming and may lack precision.
• Magnetic Resonance (MR) imaging offers detailed insights into brain tissue status during acute stroke.

Purpose of the Study:

• To develop and validate a novel automated strategy for predicting infarct evolution in acute stroke patients.
• To assess the utility of MR diffusion and perfusion imaging in quantifying tissue viability and hemodynamic function.
• To establish a predictive model for infarct growth that can guide therapeutic decisions and clinical trial evaluations.

Main Methods:

• An automated strategy was developed using MR diffusion and perfusion (DWI/PI) images from acute stroke patients.
• Regions-of-interest (ROIs) for initial diffusion lesions and abnormal hemodynamic function (mean transit time - MTT) were automatically extracted.
• Quantitative measures including cerebral blood flow (CBF), cerebral blood volume (CBV), and their ratio measures (r(a)CBF, r(a)CBV) were calculated.
• A parametric normal classifier algorithm was employed to predict infarct growth based on these quantitative measures.

Main Results:

• Significant differences in r(a)CBF and r(a)CBV were observed between eventually infarcted and recovered MTT tissue (p < 0.003 and p < 0.001).
• Mean absolute CBF and CBV also differed significantly between infarcted and recovered tissues (p < 0.009 and p < 0.036).
• The automated strategy demonstrated high performance on validation data, with sensitivity of 0.72 and specificity of 0.97 for modeling infarct evolution.

Conclusions:

• The automated MR imaging strategy effectively predicts infarct evolution in acute stroke.
• This methodology provides quantitative insights into tissue hemodynamic status, differentiating between salvageable and non-salvageable brain tissue.
• The proposed approach holds potential for guiding therapeutic interventions in stroke patients and evaluating novel stroke therapies in clinical trials.