Acute stroke: a comparison of different CT perfusion algorithms and validation of ischaemic lesions by follow-up imaging.
OBJECTIVE
To compare ischaemic lesions predicted by different CT perfusion (CTP) post-processing techniques and validate CTP lesions compared with final lesion size in stroke patients.


METHODS
Fifty patients underwent CT, CTP and CT angiography. Quantitative values and colour maps were calculated using least mean square deconvolution (LMSD), maximum slope (MS) and conventional singular value decomposition deconvolution (SVDD) algorithms. Quantitative results, core/penumbra lesion sizes and Alberta Stroke Programme Early CT Score (ASPECTS) were compared among the algorithms; lesion sizes and ASPECTS were compared with final lesions on follow-up MRI + MRA or CT + CTA as a reference standard, accounting for recanalisation status.


RESULTS
Differences in quantitative values and lesion sizes were statistically significant, but therapeutic decisions based on ASPECTS and core/penumbra ratios would have been the same in all cases. CTP lesion sizes were highly predictive of final infarct size: Coefficients of determination (R (2)) for CTP versus follow-up lesion sizes in the recanalisation group were 0.87, 0.82 and 0.61 (P < 0.001) for LMSD, MS and SVDD, respectively, and 0.88, 0.87 and 0.76 (P < 0.001), respectively, in the non-recanalisation group.


CONCLUSIONS
Lesions on CT perfusion are highly predictive of final infarct. Different CTP post-processing algorithms usually lead to the same clinical decision, but for assessing lesion size, LMSD and MS appear superior to SVDD.


CONCLUSIONS
Following an acute stroke, CT perfusion imaging can help predict lesion evolution. Delay-insensitive deconvolution and maximum slope approach are superior to delay-sensitive deconvolution regarding accuracy. Different CT perfusion post-processing algorithms usually lead to the same clinical decision. CT perfusion offers new insights into the evolution of stroke.
Institute of Radiology, University Hospital Erlangen, Erlangen, Germany. benjamin.abels@med.uni-heidelberg.de
Benjamin
B
Abels
J Pablo
JP
Villablanca
Bernd F
BF
Tomandl
Michael
M
Uder
Michael M
MM
Lell
D000368:Aged; D000465:Algorithms; D002545:Brain Ischemia / Q000000981:diagnostic imaging* / Q000188:drug therapy; D017023:Coronary Angiography; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging; D008297:Male; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D012984:Software; D018709:Statistics, Nonparametric; D020521:Stroke / Q000000981:diagnostic imaging* / Q000188:drug therapy; D015912:Thrombolytic Therapy; D014057:Tomography, X-Ray Computed / Q000379:methods*