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MRI based diffusion and perfusion predictive model to estimate stroke evolution
S E Rose1, J B Chalk, M P Griffin
1Centre For Magnetic Resonance, University of Queensland, Brisbane, Queensland 4072, Australia. Stephen.Rose@cmr.uq.edu.au
Abstract:
In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/- 0.19 and 1.20 +/- 0.36. For recovered tissue the mean values were 0.99 +/- 0.25 and 1.87 +/- 0.71, respectively. There was a significant difference between these two regions for both measures (p < 0.003 and p < 0.001, respectively). Mean absolute measures of CBF (ml/100g/min) and CBV (ml/100g) for the total infarcted territory were 33.9 +/- 9.7 and 4.2 +/- 1.9. For recovered MTT tissue, the mean values were 41.5 +/- 7.2 and 5.3 +/- 1.2, respectively. A significant difference was also found for these regions (p < 0.009 and p < 0.036, respectively). The mean measures of sensitivity, specificity, positive and negative predictive values for modeling infarct evolution for the validation patient data were 0.72 +/- 0.05, 0.97 +/- 0.02, 0.68 +/- 0.07 and 0.97 +/- 0.02. We propose that this automated strategy may allow possible guided therapeutic intervention to stroke patients and evaluation of efficacy of novel stroke compounds in clinical drug trials.
Insights
This study introduces an automated method using MR imaging to predict stroke infarct evolution. The strategy accurately identifies tissue likely to infarct or recover, aiding in potential therapeutic interventions.
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.
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