Abstract
The object of research in this study is quality of CBV perfusion map, considering detection of perfusion ROI as a key component in processing of dynamic susceptibility contrast magnetic resonance images of a human head. CBV map is generally accepted to be the best among others to evaluate location and size of stroke lesions and angiogenesis of brain tumors. Its poor accuracy can cause failed results for both quantitative measurements and visual assessment of cerebral blood volume. The impact of perfusion ROI detection on the quality of maps was analyzed through comparison of maps produced from threshold and reference images of the same datasets from 12 patients with cerebrovascular disease. Brain perfusion ROI was placed to exclude low intensity (air and non-brain tissues regions) and high intensity (cerebrospinal fluid regions) pixels. Maps were produced using area under the curve and deconvolution methods. For both methods compared maps were primarily correlational according to Pearson correlation analysis: r=0.8752 and r=0.8706 for area under the curve and deconvolution, respectively, p<2.2*10^-16. In spite of this, for both methods scatter plots had data points associated with missed blood regions and regression lines indicated presence of scale and offset errors for maps produced from threshold images. Obtained results indicate that thresholding is an ineffective way to detect brain perfusion ROI, which usage can cause degradation of CBV map quality. Perfusion ROI detection should be standardized and accepted into validation protocols of new systems for perfusion data analysis.
Highlights
Analysis of the dynamic susceptibility contrast (DSC) magnetic resonance (MR) brain perfusion data is extensively used in medical practice and it is still a subject of ongoing research efforts
The calculation of cerebral blood volume (CBV) from DSC perfusion MR studies is based on the following assumptions [3]: 1) the contrast agent is retained in the intravascular space and does not diffuse into tissues; 2) stability of the flow during the measurement; 3) T1-effects to be negligible after injecting the contrast agent
Clinical images demonstrate the presence of abnormal brain anatomy with overlapping pixel intensities in lesion regions and regions which should be excluded from further perfusion analysis. From this point of view, threshold-based delineation of brain perfusion region of interest (ROI) can impact the quality of CBV values evaluation, which is directly affected by pixels involved in the analysis
Summary
Analysis of the dynamic susceptibility contrast (DSC) magnetic resonance (MR) brain perfusion data is extensively used in medical practice and it is still a subject of ongoing research efforts. The second method requires knowledge of the contrast agent concentration in the artery supplying the region of interest to take into account the variety of physio logical conditions In this method the CBV is obtained from the ratio of the areas under the tissue and arterial signal intensity time curves, respectively. These differences in the calculation algorithms potentially impact the quality of CBV values evaluat ion, and, as a result, to the diagnostic efficiency of DSC perfusion MR imaging [6]. The aim of research is analysis of the impact of perfusion ROI detection on the quality of CBV perfusion maps in patients with known cerebrovascular disease using both semiquantitative and physiological-based perfusion analysis levels
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