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Updated: Aug 16, 2026
A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
Published on: September 16, 2017
A supervised method for calculating perfusion/diffusion mismatch volume in acute ischemic stroke
Judy R James1, Karmen K Yoder, Olaniyi Osuntokun
1Department of Biomedical Engineering, Indiana University-Purdue University, (IUPUI) Indianapolis, IN, USA.
Abstract:
Diffusion and perfusion (MR) imaging modalities identify overlapping but not identical areas of tissue as lesion following a stroke. It is thought that the 'mismatch' between modalities may represent tissue that could be recovered with proper (thrombolytic) treatment. We have designed a tool for semi-automated segmentation of the images and calculation of the mismatch volume. We present results from software phantoms and clinical data. Phantom results show our mismatch volume calculations are unbiased at realistic noise levels. Clinical data show that raters using our tool are consistent, fast (15min per subject) and indistinguishable from an expert using manual segmentation.
Insights
Stroke imaging reveals tissue mismatch between diffusion and perfusion MRI scans. A new semi-automated tool accurately calculates this mismatch volume, aiding potential stroke recovery treatments.
Area of Science:
- Medical imaging
- Neurology
- Biomedical engineering
Background:
- Diffusion and perfusion MRI detect distinct but overlapping areas of tissue damage post-stroke.
- The 'mismatch' volume between these modalities is hypothesized to represent salvageable brain tissue.
Purpose of the Study:
- To develop and validate a semi-automated tool for segmenting stroke lesions and calculating the diffusion-perfusion mismatch volume.
- To assess the accuracy, consistency, and speed of the developed tool using phantom and clinical data.
Main Methods:
- Semi-automated image segmentation software was developed to delineate diffusion and perfusion lesion volumes.
- Mismatch volume was calculated by comparing the segmented areas from both modalities.
- Software phantoms and clinical stroke patient data were used for validation.
Main Results:
- Phantom data demonstrated unbiased mismatch volume calculations even at realistic noise levels.
- Clinical data showed high inter-rater consistency among users of the tool.
- The tool enabled rapid analysis, with an average time of 15 minutes per subject, comparable to expert manual segmentation.
Conclusions:
- The developed semi-automated tool provides accurate and consistent measurement of diffusion-perfusion mismatch in stroke imaging.
- This tool offers a time-efficient method for quantifying potentially salvageable brain tissue, supporting treatment decisions in acute stroke.
