Comparison of 10 perfusion MRI parameters in 97 sub-6-hour stroke patients using voxel-based receiver operating characteristics analysis.
OBJECTIVE
Perfusion-weighted imaging can predict infarct growth in acute stroke and potentially be used to select patients with tissue at risk for reperfusion therapies. However, the lack of consensus and evidence on how to best create PWI maps that reflect tissue at risk challenges comparisons of results and acute decision-making in trials. Deconvolution using an arterial input function has been hypothesized to generate maps of a more quantitative nature and with better prognostic value than simpler summary measures such as time-to-peak or the first moment of the concentration time curve. We sought to compare 10 different perfusion parameters by their ability to predict tissue infarction in acute ischemic stroke.


METHODS
In a retrospective analysis of 97 patients with acute stroke studied within 6 hours from symptom onset, we used receiver operating characteristics in a voxel-based analysis to compare 10 perfusion parameters: time-to-peak, first moment, cerebral blood volume and flow, and 6 variants of time to peak of the residue function and mean transit time maps. Subanalysis assessed the effect of reperfusion on outcome prediction.


RESULTS
The most predictive maps were the summary measures first moment and time-to-peak. First moment was significantly more predictive than time to peak of the residue function and local arterial input function-based methods (P<0.05), but not significantly better than conventional mean transit time maps.


CONCLUSIONS
Results indicated that if a single map type was to be used to predict infarction, first moment maps performed at least as well as deconvolved measures. Deconvolution decouples delay from tissue perfusion; we speculate this negatively impacts infarct prediction.
Center of Functionally Integrative Neuroscience, Department of Neuroradiology, Aarhus University Hospital, Building 30, Nørrebrogade 44, 8000 Aarhus C, Denmark. sorenc@unimelb.edu.au
Søren
S
Christensen
Kim
K
Mouridsen
Ona
O
Wu
Niels
N
Hjort
Henrik
H
Karstoft
Götz
G
Thomalla
Joachim
J
Röther
Jens
J
Fiehler
Thomas
T
Kucinski
Leif
L
Østergaard
D000368:Aged; D001931:Brain Mapping; D002544:Cerebral Infarction / Q000473:pathology; D002560:Cerebrovascular Circulation / Q000502:physiology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D015424:Reperfusion; D020521:Stroke / Q000473:pathology* / Q000503:physiopathology*