Sunny Virmani

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 (√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 × 100 g−1 × min−1 for the first pass versus 1.05 mL × 100 g−1 × min−1 for the delayed phase, P < .001) and in ischemic tissue (7.63 mL × 100 g−1 × min−1 for the first pass versus 1.31 mL × 100 g−1 × 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 √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.

Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA

We propose a method for segmenting and labeling the main head and neck vessels (common, internal, external carotid, vertebral) from a contrast enhanced computed tomography angiography (CTA) volume. First, an initial centerline of each vessel is extracted. Next, the vessels are segmented using 3D active objects initialized using the first step. Finally, the true centerline is identified by smoothly deforming it away from the segmented mask edges using a spline-snake. We focus particularly on the novel initial centerline extraction technique. It uses a locally adaptive front propagation algorithm that attempts to find the optimal path connecting the ends of the vessel, typically from the lowest image of the scan to the Circle of Willis in the brain. It uses a patient adapted anatomical model of the different vessels both to initialize and constrain this fast marching, thus eliminating the need for manual selection of seed points. The method is evaluated using data from multiple regions (USA, India, China, Israel) including a variety of scanners (10, 16, 40, 64-slice; Brilliance CT, Philips Healthcare, Cleveland, OH, USA), contrast agent dose, and image resolution. It is fully successful in over 90% of patients and only misses a single vessel in most remaining cases. We also demonstrate its robustness to metal and dental artifacts and anatomical variability. Total processing time is approximately two minutes with no user interaction, which dramatically improves the workflow over existing clinical software. It also reduces patient dose exposure by obviating the need to acquire an unenhanced scan for bone suppression as this can be done by applying the segmentation masks.

Accuracy and anatomical coverage of perfusion CT assessment of the blood-brain barrier permeability: one bolus versus two boluses.

Purpose: To assess whether blood-brain barrier permeability (BBBP) values, extracted with the Patlak model from the second perfusion CT (PCT) contrast bolus, are significantly lower than the values extracted from the first bolus in the same patient. Materials and Methods: 125 consecutive patients (29 with acute hemispheric stroke and 96 without stroke) who underwent a PCT study using a prolonged acquisition time up to 3 min were retrospectively identified. The Patlak model was applied to calculate the rate of contrast leakage out of the vascular compartment. Patlak plots were created from the arterial and parenchymal time enhancement curves obtained in multiple regions of interest drawn in ischemic brain tissue and in nonischemic brain tissue. The slope of a regression line fit to the Patlak plot was used as an indicator of BBBP. Square roots of the mean squared errors and correlation coefficients were used to describe the quality of the linear regression model. This was performed separately for the first and the second PCT bolus. Results from the first and the second bolus were compared in terms of BBBP values and the quality of the linear model fitted to the Patlak plot, using generalized estimating equations with robust variance estimation. Results: BBBP values from the second bolus were not lower than BBBP values from the first bolus in either nonischemic brain tissue [estimated mean with 95% confidence interval: 1.42 (1.10–1.82) ml·100 g–1·min–1 for the first bolus versus 1.64 (1.31–2.05) ml·100 g–1·min–1 for the second bolus, p = 1.00] or in ischemic tissue [1.04 (0.97–1.12) ml·100 g–1·min–1 for the first bolus versus 1.19 (1.11–1.28) ml·100 g–1·min–1 for the second bolus, p = 0.79]. Compared to regression models from the first bolus, the Patlak regression models obtained from the second bolus were of similar or slightly better quality. This was true both in nonischemic and ischemic brain tissue. Conclusion: The contrast material from the first bolus of contrast for PCT does not negatively influence measurements of BBBP values from the second bolus. The second bolus can thus be used to increase anatomical coverage of BBBP assessment using PCT.