Skip to main content

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

Publication ,  Journal Article
Song, S; Bokkers, RPH; Luby, M; Edwardson, MA; Brown, T; Shah, S; Cox, RW; Saad, ZS; Reynolds, RC; Glen, DR; Cohen, LG; Latour, LL
Published in: PLoS One
2017

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. 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). 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). 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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2017

Volume

12

Issue

10

Start / End Page

e0185552

Location

United States

Related Subject Headings

  • Stroke
  • Retrospective Studies
  • Models, Theoretical
  • Magnetic Resonance Imaging
  • Humans
  • General Science & Technology
  • Automation
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Song, S., Bokkers, R. P. H., Luby, M., Edwardson, M. A., Brown, T., Shah, S., … Latour, L. L. (2017). Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke. PLoS One, 12(10), e0185552. https://doi.org/10.1371/journal.pone.0185552
Song, Sunbin, Reinoud P. H. Bokkers, Marie Luby, Matthew A. Edwardson, Tyler Brown, Shreyansh Shah, Robert W. Cox, et al. “Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke.PLoS One 12, no. 10 (2017): e0185552. https://doi.org/10.1371/journal.pone.0185552.
Song S, Bokkers RPH, Luby M, Edwardson MA, Brown T, Shah S, et al. Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke. PLoS One. 2017;12(10):e0185552.
Song, Sunbin, et al. “Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke.PLoS One, vol. 12, no. 10, 2017, p. e0185552. Pubmed, doi:10.1371/journal.pone.0185552.
Song S, Bokkers RPH, Luby M, Edwardson MA, Brown T, Shah S, Cox RW, Saad ZS, Reynolds RC, Glen DR, Cohen LG, Latour LL. Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke. PLoS One. 2017;12(10):e0185552.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2017

Volume

12

Issue

10

Start / End Page

e0185552

Location

United States

Related Subject Headings

  • Stroke
  • Retrospective Studies
  • Models, Theoretical
  • Magnetic Resonance Imaging
  • Humans
  • General Science & Technology
  • Automation