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The object of research in this study is quality of CBV perfusion map,considering detection of perfusion ROI as a key component in processing ofdynamic susceptibility contrast magnetic resonance images of a human head. CBVmap is generally accepted to be the best among others to evaluate location andsize of stroke lesions and angiogenesis of brain tumors. Its poor accuracy cancause failed results for both quantitative measurements and visual assessmentof cerebral blood volume. The impact of perfusion ROI detection on the qualityof maps was analyzed through comparison of maps produced from threshold andreference images of the same datasets from 12 patients with cerebrovasculardisease. Brain perfusion ROI was placed to exclude low intensity (air andnon-brain tissues regions) and high intensity (cerebrospinal fluid regions)pixels. Maps were produced using area under the curve and deconvolutionmethods. For both methods compared maps were primarily correlational accordingto Pearson correlation analysis: r=0.8752 and r=0.8706 for area under the curveand deconvolution, respectively, p<2.2*10^-16. In spite of this, for bothmethods scatter plots had data points associated with missed blood regions andregression lines indicated presence of scale and offset errors for mapsproduced from threshold images. Obtained results indicate that thresholding isan ineffective way to detect brain perfusion ROI, which usage can causedegradation of CBV map quality. Perfusion ROI detection should be standardizedand accepted into validation protocols of new systems for perfusion dataanalysis.

Impact of perfusion ROI detection to the quality of CBV perfusion map