Magnetic Resonance Imaging in Ischemic Stroke Models

Magnetic resonance imaging (MRI) has become a cornerstone in preclinical investigations of ischaemic stroke, offering non-invasive, longitudinal assessment of cerebral injury in experimental rodent models. By combining multiple contrast mechanisms—such as T2-weighted imaging to visualise vasogenic oedema, diffusion-weighted imaging to detect acute cytotoxic changes, and functional MRI to probe connectivity—researchers can map the spatio-temporal evolution of infarction, quantify penumbral tissue, and monitor vascular integrity. Quantitative metrics such as the apparent diffusion coefficient (ADC) and infarct volume provide robust endpoints for evaluating neuroprotective interventions and surgical occlusion techniques. Recent advances in automated lesion segmentation and machine learning have standardised data analysis, reduced observer bias and improved reproducibility across centres. The preclinical insights gained through high-field MRI not only elucidate mechanisms of neuronal death and repair but also enhance translation of candidate therapies into clinical trials, underlining the global significance of MRI in bridging the gap between bench and bedside.

Research from Nature Portfolio

Recent studies have introduced a deep learning-based framework for fully automated segmentation of ischaemic lesions in mouse T2-weighted MRI. This approach operates directly on raw scans with minimal preprocessing, yielding segmentation masks that match or exceed the consistency of expert tracings. Validation against independent datasets demonstrates robust performance across varied imaging protocols, facilitating standardised quantification of infarct volume and enabling high-throughput evaluation of therapeutic strategies.

Magnetic Resonance Imaging in Ischemic Stroke Models publication trend

The graph below shows the total number of articles in magnetic resonance imaging in ischemic stroke models across all publications each year (not limited to Nature Index journals).

Technical terms

T2-weighted imaging: MRI sequence sensitive to water content, commonly used to visualize oedema and fluid-filled spaces.

Diffusion-weighted imaging: MRI technique that measures the random motion of water molecules, enabling early detection of cytotoxic injury.

Apparent diffusion coefficient (ADC): Quantitative metric derived from diffusion-weighted imaging that reflects tissue cellularity and microstructural integrity.

Lesion segmentation: Process of delineating damaged regions on MRI, often involving manual tracing, thresholding or automated algorithms.

Infarct volume: Total volume of brain tissue irreversibly damaged by ischaemia, used as a primary endpoint in preclinical studies.

References

1. A toolkit for stroke infarct volume estimation in rodents. NeuroImage (2024).
2. Repurposing the mucolytic agent ambroxol for treatment of sub-acute and chronic ischaemic stroke. Brain Communications (2023).
3. Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images. Scientific Reports (2023).
4. Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion. Frontiers in Neuroinformatics (2017).
5. Multicenter Evaluation of Geometric Accuracy of MRI Protocols Used in Experimental Stroke. PLOS ONE (2016).
6. The use of MRI apparent diffusion coefficient (ADC) in monitoring the development of brain infarction. BMC Medical Imaging (2011).