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Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke.

Authors :
Song S
Bokkers RPH
Luby M
Edwardson MA
Brown T
Shah S
Cox RW
Saad ZS
Reynolds RC
Glen DR
Cohen LG
Latour LL
Source :
PloS one [PLoS One] 2017 Oct 03; Vol. 12 (10), pp. e0185552. Date of Electronic Publication: 2017 Oct 03 (Print Publication: 2017).
Publication Year :
2017

Abstract

Introduction: Interpretation of the extent of perfusion deficits in stroke MRI is highly dependent on the method used for analyzing the perfusion-weighted signal intensity time-series after gadolinium injection. In this study, we introduce a new model-free standardized method of temporal similarity perfusion (TSP) mapping for perfusion deficit detection and test its ability and reliability in acute ischemia.<br />Materials and Methods: Forty patients with an ischemic stroke or transient ischemic attack were included. Two blinded readers compared real-time generated interactive maps and automatically generated TSP maps to traditional TTP/MTT maps for presence of perfusion deficits. Lesion volumes were compared for volumetric inter-rater reliability, spatial concordance between perfusion deficits and healthy tissue and contrast-to-noise ratio (CNR).<br />Results: Perfusion deficits were correctly detected in all patients with acute ischemia. Inter-rater reliability was higher for TSP when compared to TTP/MTT maps and there was a high similarity between the lesion volumes depicted on TSP and TTP/MTT (r(18) = 0.73). The Pearson's correlation between lesions calculated on TSP and traditional maps was high (r(18) = 0.73, p<0.0003), however the effective CNR was greater for TSP compared to TTP (352.3 vs 283.5, t(19) = 2.6, p<0.03.) and MTT (228.3, t(19) = 2.8, p<0.03).<br />Discussion: TSP maps provide a reliable and robust model-free method for accurate perfusion deficit detection and improve lesion delineation compared to traditional methods. This simple method is also computationally faster and more easily automated than model-based methods. This method can potentially improve the speed and accuracy in perfusion deficit detection for acute stroke treatment and clinical trial inclusion decision-making.

Details

Language :
English
ISSN :
1932-6203
Volume :
12
Issue :
10
Database :
MEDLINE
Journal :
PloS one
Publication Type :
Academic Journal
Accession number :
28973000
Full Text :
https://doi.org/10.1371/journal.pone.0185552