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Updated: Apr 25, 2026
Middle Cerebral Artery Occlusion Allowing Reperfusion via Common Carotid Artery Repair in Mice
Published on: January 23, 2019
Infarct volume prediction using apparent diffusion coefficient maps during middle cerebral artery occlusion and soon
Raúl Tudela1, Guadalupe Soria2, Isabel Pérez-De-Puig3
1CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain.
Abstract:
Middle cerebral artery occlusion (MCAO) in rodents causes brain infarctions of variable sizes that depend on multiple factors, particularly in models of ischemia/reperfusion. This is a major problem for infarct volume comparisons between different experimental groups since unavoidable variability can induce biases in the results and imposes the use of large number of subjects. MRI can help to minimize these difficulties by ensuring that the severity of ischemia is comparable between groups. Furthermore, several studies showed that infarct volumes can be predicted with MRI data obtained soon after ischemia onset. However, such predictive studies require multiparametric MRI acquisitions that cannot be routinely performed, and data processing using complex algorithms that are often not available. The aim here was to provide a simplified method for infarct volume prediction using apparent diffusion coefficient (ADC) data in a model of transient MCAO in rats. ADC images were obtained before, during MCAO and after 60 min of reperfusion. Probability histograms were generated using ADC data obtained either during MCAO, after reperfusion, or both combined. The results were compared to real infarct volumes, i.e.T2 maps obtained at day 7. Assessment of the performance of the estimations showed better results combining ADC data obtained during occlusion and at reperfusion. Therefore, ADC data alone can provide sufficient information for a reasonable prediction of infarct volume if the MRI information is obtained both during the occlusion and soon after reperfusion. This approach can be used to check whether drug administration after MRI acquisition can change infarct volume prediction.
Insights
Predicting stroke size in rats is simplified using apparent diffusion coefficient (ADC) MRI data. Combining ADC during middle cerebral artery occlusion and reperfusion offers a reliable method for estimating infarct volume.
Area of Science:
- Neuroscience
- Medical Imaging
- Stroke Research
Background:
- Middle cerebral artery occlusion (MCAO) in rodents causes variable infarct sizes, complicating comparisons and requiring large subject numbers.
- Magnetic resonance imaging (MRI) can standardize ischemia severity, but predictive studies often need complex, multiparametric acquisitions.
- Existing methods for infarct volume prediction are resource-intensive and not routinely available.
Purpose of the Study:
- To develop a simplified method for predicting infarct volume using apparent diffusion coefficient (ADC) data.
- To assess the utility of ADC data obtained during and after transient MCAO in rats for infarct prediction.
- To establish a more accessible approach for infarct volume estimation in preclinical stroke models.
Main Methods:
- Transient middle cerebral artery occlusion (MCAO) was induced in rats.
- Apparent diffusion coefficient (ADC) MRI data were acquired before, during MCAO, and after 60 minutes of reperfusion.
- Probability histograms of ADC data were generated using data from occlusion, reperfusion, or both, and compared to T2 maps at day 7 for infarct volume validation.
Main Results:
- Combining ADC data acquired during MCAO and reperfusion yielded better infarct volume prediction accuracy.
- Apparent diffusion coefficient (ADC) data alone, when acquired at both time points, provide sufficient information for reasonable infarct volume prediction.
- The simplified method demonstrated good performance in estimating infarct volumes.
Conclusions:
- Apparent diffusion coefficient (ADC) MRI data obtained during occlusion and shortly after reperfusion can reliably predict infarct volume in a rat MCAO model.
- This simplified approach reduces the need for complex multiparametric MRI and advanced algorithms.
- The method offers a practical tool for assessing stroke severity and evaluating the effects of interventions on infarct volume post-MRI acquisition.
