Spatiotemporal Dynamic Simulation of Acute Perfusion/Diffusion Ischemic Stroke Lesions Evolution : A Pilot Study Derived from Longitudinal MR Patient Data

The spatiotemporal evolution of stroke lesions, from acute injury to final tissue damage, is complex. Diffusion-weighted (DWI) and perfusion-weighted (PWI) imaging is commonly used to detect early ischemic changes and attempts to distinguish between permanently damaged and salvageable tissues. To da...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2013-01, Vol.2013 (2013), p.1-13
Main Authors: Rekik, Islem, Allassonnière, Stéphanie, Durrleman, Stanley, Carpenter, Trevor, Wardlaw, Joanna
Format: Article
Language:English
Subjects:
Aged
Brain - pathology
Brain Ischemia - complications
Brain Ischemia - diagnosis
Computational Biology
Computer Science
Computer Simulation
Diagnosis, Computer-Assisted - statistics & numerical data
Diffusion Magnetic Resonance Imaging - statistics & numerical data
Female
Humans
Imaging, Three-Dimensional - statistics & numerical data
Longitudinal Studies
Male
Medical Imaging
Models, Neurological
Pilot Projects
Stroke - diagnosis
Stroke - etiology
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Summary:The spatiotemporal evolution of stroke lesions, from acute injury to final tissue damage, is complex. Diffusion-weighted (DWI) and perfusion-weighted (PWI) imaging is commonly used to detect early ischemic changes and attempts to distinguish between permanently damaged and salvageable tissues. To date, 2D and 3D measures of diffusion/perfusion regions at individual timepoints have been widely used but may underestimate the true lesion spatio-temporal dynamics. Currently there is no spatio-temporal 4D dynamic model that simulates the continuous evolution of ischemic stroke from MR images. We determined whether a 4D current-based diffeomorphic model, developed in the field of statistical modeling for measuring the variability of anatomical surfaces, could estimate patient-specific spatio-temporal continuous evolution for MR PWI (measured as mean transit time, (MTT)) and DWI lesions. In our representative pilot sample, the model fitted the data well. Our dynamic analysis of lesion evolution showed different patterns; for example, some DWI/PWI dynamic changes corresponded with DWI lesion expansion into PWI lesions, but other patterns were much more complex and diverse. There was wide variation in the time when the final tissue damage was reached after stroke for DWI and MTT.
ISSN:1748-670X
1748-6718
DOI:10.1155/2013/283593