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A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
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MRI-based parameter inference for cerebral perfusion modelling in health and ischaemic stroke
T I Józsa1, J Petr2, S J Payne3
1Centre for Computational Engineering Sciences, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK.
Computers in Biology and Medicine
|October 14, 2023
Summary
This study developed an automated method for cerebral blood flow (CBF) modeling using neuroimaging data. This approach enables group-level simulations for acute ischemic stroke, aiding in treatment development.
Area of Science:
- Medical Imaging
- Computational Neuroscience
- Stroke Research
Background:
- Cerebral perfusion modeling predicts treatment impact on cerebral blood flow (CBF) in stroke.
- Current models are limited to few patient-specific cases, hindering group-level analysis.
- Physiological variability necessitates group-level investigations for stroke treatment efficacy.
Purpose of the Study:
- Establish automated parameter inference for perfusion modeling using neuroimaging data.
- Enable group-level CBF simulations for acute ischemic stroke research.
- Facilitate in silico clinical trials for stroke treatment development.
Main Methods:
- Utilized MRI data from 75 healthy senior adults.
- Computed brain geometries from T1-weighted MRI and determined hemodynamic parameters from ASL perfusion MRI.
- Conducted perfusion simulations in healthy and acute ischemic stroke cases.
Main Results:
- Validated anatomical fitness of brain geometries with strong correlations for grey and white matter volumes.
- Verified hemodynamic parameter tuning by comparing simulated and reference total brain blood flow.
- Simulated infarct volume in acute stroke cases was 197±25 ml, aligning with literature values.
Conclusions:
- The automated parameter inference method enables group-level CBF simulations.
- This approach provides a foundation for in silico clinical stroke trials.
- Assists in the development of medical devices and drugs for stroke treatment.
