Dynamic perfusion CT assessment of the blood-brain barrier permeability: first pass versus delayed acquisition.

BACKGROUND AND PURPOSE The Patlak model has been applied to first-pass perfusion CT (PCT) data to extract information on blood-brain barrier permeability (BBBP) to predict hemorrhagic transformation in patients with acute stroke. However, the Patlak model was originally described for the delayed steady-state phase of contrast circulation. The goal of this study was to assess whether the first pass or the delayed phase of a contrast bolus injection better respects the assumptions of the Patlak model for the assessment of BBBP in patients with acute stroke by using PCT. MATERIALS AND METHODS We retrospectively identified 125 consecutive patients (29 with acute hemispheric stroke and 96 without) who underwent a PCT study by using a prolonged acquisition time up to 3 minutes. The Patlak model was applied to calculate BBBP in ischemic and nonischemic brain tissue. Linear regression of the Patlak plot was performed separately for the first pass and for the delayed phase of the contrast bolus injection. Patlak linear regression models for the first pass and the delayed phase were compared in terms of their respective square root mean squared errors (square root MSE) and correlation coefficients (R) by using generalized estimating equations with robust variance estimation. RESULTS BBBP values calculated from the first pass were significantly higher than those from the delayed phase, both in nonischemic brain tissue (2.81 mL x 100 g(-1) x min(-1) for the first pass versus 1.05 mL x 100 g(-1) x min(-1) for the delayed phase, P < .001) and in ischemic tissue (7.63 mL x 100 g(-1) x min(-1) for the first pass versus 1.31 mL x 100 g(-1) x min(-1) for the delayed phase, P < .001). Compared with regression models from the first pass, Patlak regression models obtained from the delayed data were of better quality, showing significantly lower square root MSE and higher R. CONCLUSION Only the delayed phase of PCT acquisition respects the assumptions of linearity of the Patlak model in patients with and without stroke.