Delay correction for the assessment of blood-brain barrier permeability using first-pass dynamic perfusion CT

Comparison of blind deconvolution- and Patlak analysis-based methods for determining vascular permeability

This study describes a computational algorithm to determine vascular permeability constants from time-lapse imaging data without concurrent knowledge of the arterial input function. The algorithm is based on "blind" deconvolution of imaging data, which were generated with analytical and finite-element models of bidirectional solute transport between a capillary and its surrounding tissue. Compared to the commonly used Patlak analysis, the blind algorithm is substantially more accurate in the presence of solute delay and dispersion. We also compared the performance of the blind algorithm with that of a simpler one that assumed unidirectional transport from capillary to tissue [as described in Truslow et al., Microvasc. Res. 90, 117-120 (2013)]. The algorithm based on bidirectional transport was more accurate than the one based on unidirectional transport for more permeable vessels and smaller extravascular distribution volumes, and less accurate for less permeable vessels and larger extravascular distribution volumes. Our results indicate that blind deconvolution is superior to Patlak analysis for permeability mapping under clinically relevant conditions, and can thus potentially improve the detection of tissue regions with a compromised vascular barrier.

Focal Low and Global High Permeability Predict the Possibility, Risk, and Location of Hemorrhagic Transformation following Intra-Arterial Thrombolysis Therapy in Acute Stroke

BACKGROUND AND PURPOSE

The contrast volume transfer coefficient (K^trans), which reflects blood-brain barrier permeability, is influenced by circulation and measurement conditions. We hypothesized that focal low BBB permeability values can predict the spatial distribution of hemorrhagic transformation and global high BBB permeability values can predict the likelihood of hemorrhagic transformation.

MATERIALS AND METHODS

We retrospectively enrolled 106 patients with hemispheric stroke who received intra-arterial thrombolytic treatment. K^trans maps were obtained with first-pass perfusion CT data. The K^trans values at the region level, obtained with the Alberta Stroke Program Early CT Score system, were compared to determine the differences between the hemorrhagic transformation and nonhemorrhagic transformation regions. The K^trans values of the whole ischemic region based on baseline perfusion CT were obtained as a variable to hemorrhagic transformation possibility at the global level.

RESULTS

Forty-eight (45.3%) patients had hemorrhagic transformation, and 21 (19.8%) had symptomatic intracranial hemorrhage. At the region level, there were 82 ROIs with hemorrhagic transformation and parenchymal hemorrhage with a mean K^trans, 0.5 ± 0.5/min, which was significantly lower than that in the nonhemorrhagic transformation regions (P < .01). The mean K^trans value of 615 nonhemorrhagic transformation ROIs was 0.7 ± 0.6/min. At the global level, there was a significant difference (P = .01) between the mean K^trans values of patients with symptomatic intracranial hemorrhage (1.3 ± 0.9) and those without symptomatic intracranial hemorrhage (0.8 ± 0.4). Only a high K^trans value at the global level could predict the occurrence of symptomatic intracranial hemorrhage (P < .01; OR = 5.04; 95% CI, 2.01-12.65).

CONCLUSIONS

Global high K^trans values can predict the likelihood of hemorrhagic transformation or symptomatic intracranial hemorrhage at the patient level, whereas focal low K^trans values can predict the spatial distributions of hemorrhagic transformation at the region level.

CT perfusion analysis by nonlinear regression for predicting hemorrhagic transformation in ischemic stroke

PURPOSE

Intravenous thrombolysis can improve clinical outcome in acute ischemic stroke patients but increases the risk of hemorrhagic transformation (HT). Blood-brain barrier damage, which can be quantified by the vascular permeability for contrast agents, is a potential predictor for HT. This study aimed to assess whether this prediction can be improved by measuring vascular permeability using a novel fast nonlinear regression (NLR) method instead of Patlak analysis.

METHODS

From a prospective ischemic stroke multicenter cohort study, 20 patients with HT on follow-up imaging and 40 patients without HT were selected. The permeability transfer constant K(trans) was measured in three ways; using standard Patlak analysis, Patlak analysis with a fixed offset, and the NLR method. In addition, the permeability-surface (PS) area product and the conventional perfusion parameters (blood volume, flow, and mean transit time) were measured using the NLR method. Relative values were calculated in two ways, i.e., by dividing the average in the infarct core by the average in the contralateral hemisphere, and by dividing the average in the ipsilateral hemisphere by the average in the contralateral hemisphere. Mann-Whitney U tests and receiver operating characteristic (ROC) analyses were performed to assess the discriminative power of each of the relative parameters.

RESULTS

Both the infarct-core and whole-hemisphere averaged relative K(trans) (rK(trans)) values, measured with the NLR method, were significantly higher in the patients who developed HT as compared with those who did not. The rK(trans) measured with standard Patlak analysis was not significantly different. The relative PS (rPS), measured with NLR, had the highest discriminative power (P = 0.002). ROC analysis of rPS showed an area under the curve (AUC) of 0.75 (95% confidence interval: 0.62-0.89) and a sensitivity of 0.75 at a specificity of 0.75. The AUCs of the Patlak rK(trans), the Patlak rK(trans) with fixed offset, and the NLR rK(trans) were 0.58, 0.66, and 0.67, respectively.

CONCLUSIONS

CT perfusion analysis may aid in predicting HT, but standard Patlak analysis did not provide estimates for rK(trans) that were significantly higher in the HT group. The rPS, measured in the infarct core with NLR, had superior discriminative power compared with K(trans) measured with either Patlak analysis with a fixed offset or NLR, and conventional perfusion parameters.

Blood-brain barrier permeability imaging using perfusion computed tomography

Background.

The blood-brain barrier represents the selective diffusion barrier at the level of the cerebral microvascular endothelium. Other functions of blood-brain barrier include transport, signaling and osmoregulation. Endothelial cells interact with surrounding astrocytes, pericytes and neurons. These interactions are crucial to the development, structural integrity and function of the cerebral microvascular endothelium. Dysfunctional blood-brain barrier has been associated with pathologies such as acute stroke, tumors, inflammatory and neurodegenerative diseases.

Conclusions.

Blood-brain barrier permeability can be evaluated in vivo by perfusion computed tomography - an efficient diagnostic method that involves the sequential acquisition of tomographic images during the intravenous administration of iodinated contrast material. The major clinical applications of perfusion computed tomography are in acute stroke and in brain tumor imaging.

Increased blood-brain barrier permeability on perfusion computed tomography predicts hemorrhagic transformation in acute ischemic stroke

BACKGROUND/PURPOSE

Perfusion computed tomography (CT) is capable of measuring the permeability surface product (PS). PS reflects the permeability of the blood-brain barrier, involved in the pathophysiology of hemorrhagic transformation (HT) of ischemic stroke. The aim of our study was to determine if an increased PS can predict HT.

METHODS

A total of 86 patients with ischemic stroke were included. They underwent multimodality CT, including the measurement of PS. We compared the clinical and radiological characteristics of patients who developed HT to those who did not, using univariate analysis. Multivariate regression analyses were then used to determine HT predictors.

RESULTS

HT was observed in 27 patients (31%). Infarct PS was significantly associated with HT (p = 0.047), as were atrial fibrillation (p = 0.03), admission National Institute of Health Stroke Scale score (p = 0.02), infarct volume (p = 0.0004), presence of large-vessel occlusion (p = 0.0005) and a poorer collateral status (p = 0.003). Using logistic regression modeling, an infarct PS >0.84 ml/100 g/min was an independent predictor of HT (OR 28, 95% CI 1.75-452.98; p = 0.02). Other independent predictors of HT were infarct volume and a history of atrial fibrillation.

CONCLUSIONS

Our findings suggest that infarct PS can be a predictor of HT and may help clinicians to improve patient care around thrombolysis decisions in the acute phase of ischemic stroke.

A fast nonlinear regression method for estimating permeability in CT perfusion imaging

Blood-brain barrier damage, which can be quantified by measuring vascular permeability, is a potential predictor for hemorrhagic transformation in acute ischemic stroke. Permeability is commonly estimated by applying Patlak analysis to computed tomography (CT) perfusion data, but this method lacks precision. Applying more elaborate kinetic models by means of nonlinear regression (NLR) may improve precision, but is more time consuming and therefore less appropriate in an acute stroke setting. We propose a simplified NLR method that may be faster and still precise enough for clinical use. The aim of this study is to evaluate the reliability of in total 12 variations of Patlak analysis and NLR methods, including the simplified NLR method. Confidence intervals for the permeability estimates were evaluated using simulated CT attenuation-time curves with realistic noise, and clinical data from 20 patients. Although fixating the blood volume improved Patlak analysis, the NLR methods yielded significantly more reliable estimates, but took up to 12 × longer to calculate. The simplified NLR method was ∼4 × faster than other NLR methods, while maintaining the same confidence intervals (CIs). In conclusion, the simplified NLR method is a new, reliable way to estimate permeability in stroke, fast enough for clinical application in an acute stroke setting.

Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

Blood-brain barrier permeability (BBBP) measurements extracted from the perfusion computed tomography (PCT) using the Patlak model can be a valuable indicator to predict hemorrhagic transformation in patients with acute stroke. Unfortunately, the standard Patlak model based PCT requires excessive radiation exposure, which raised attention on radiation safety. Minimizing radiation dose is of high value in clinical practice but can degrade the image quality due to the introduced severe noise. The purpose of this work is to construct high quality BBBP maps from low-dose PCT data by using the brain structural similarity between different individuals and the relations between the high- and low-dose maps. The proposed sparse high-dose induced (shd-Patlak) model performs by building a high-dose induced prior for the Patlak model with a set of location adaptive dictionaries, followed by an optimized estimation of BBBP map with the prior regularized Patlak model. Evaluation with the simulated low-dose clinical brain PCT datasets clearly demonstrate that the shd-Patlak model can achieve more significant gains than the standard Patlak model with improved visual quality, higher fidelity to the gold standard and more accurate details for clinical analysis.

Determination of vascular permeability coefficients under slow luminal filling

This Communication describes a method to obtain the permeability product (permeability coefficient normalized by vascular dimensions) from time-lapse intensity data for which the introduction of labeled solute into the vasculature does not occur at a sharply defined time. This method has an error of ~10% across a wide range of filling times and noise levels, and is particularly well-suited for situations in which the permeability coefficient is greater than 10(-6)cm/s. We show that it is applicable whether the increase in vascular solute concentration is sustained or transient.

Measurement of blood-brain barrier permeability in acute ischemic stroke using standard first-pass perfusion CT data

Background and purpose

Increased blood–brain barrier permeability is believed to be associated with complications following acute ischemic stroke and with infarct expansion. Measurement of blood–brain barrier permeability requires a delayed image acquisition methodology, which prolongs examination time, increasing the likelihood of movement artefacts and radiation dose. Existing quantitative methods overestimate blood–brain barrier permeability when early phase CT perfusion data are used. The purpose of this study is to develop a method that yields the correct blood–brain barrier permeability value using first-pass perfusion CT data.

Methods

We acquired 43 CT perfusion datasets, comprising experimental (n = 30) and validation subject groups (n = 13). The Gjedde–Patlak method was used to estimate blood–brain barrier permeability using first-pass (30–60 s after contrast administration) and delayed phase (30–200 s) data. In the experimental group, linear regression was used to obtain a function predicting first-pass blood–brain barrier permeability estimates from delayed phase estimates in each stroke compartment. The reliability of prediction with this function was then tested using data from the validation group.

Results

The predicted delayed phase blood–brain barrier permeability was strongly correlated with the measured delayed phase value (r = 0.67 and 0.6 for experimental and validation group respectively; p < 0.01). Predicted and measured delayed phase blood–brain barrier permeability in each stroke compartment were not significantly different in both experimental and validation groups.

Conclusion

We have developed a method of estimating blood–brain barrier permeability using first-pass perfusion CT data. This predictive method allows reliable blood–brain barrier permeability estimation within standard acquisition time, minimizing the likelihood of motion artefacts thereby improving image quality and reducing radiation dose.

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Delayed phase BBBP can be predicted from first-pass perfusion CT data.

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Predicted BBBP was not significantly different from delayed phase measurements.

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Prediction model allows reliable BBBP estimation within the standard acquisition time.

Quantification of blood-to-brain transfer rate in multiple sclerosis

Blood-brain barrier (BBB) disruption visualized in lesions by MRI is a major biomarker of disease activity in multiple sclerosis (MS). However, in MS, destruction occurs to a variable extent in lesions as well as in gray matter (GM) and in the normal appearing white matter (NAWM). A method to quantify the BBB disruption in lesions as well as in non-lesion areas would be useful for assessment of MS progression and treatments. The objective of this study was to quantify the BBB transfer rate (Ki) in WM lesions, in the NAWM, and in the full-brain of MS patients. Thirteen MS patients with active lesions and 10 healthy controls with age and gender matching were recruited for full-brain and WM Ki studies. Dynamic contrast-enhanced MRI (DCEMRI) scans were conducted using T1 mapping with partial inversion recovery (TAPIR), a fast T1 mapping technique, following administration of a quarter-dose of the contrast agent Gadolinium-DTPA (Gd-DTPA). The Patlak modeling technique was used to derive a voxel-based map of Ki. In all patients contrast-enhanced lesions, quantified by Ki maps, were observed. Compared with controls, patients with MS exhibited an increase in mean Ki of the full-brain (P-value<0.05) but no significant difference in mean Ki of NAWM. The identified increase in full-brain Ki of MS patients suggests a global vascular involvement associated with MS disease. The lack of observed significant decrease in Ki in NAWM suggests lower involvement of WM vasculature than full-brain vasculature in MS. Ki maps constructed from time series data acquired by DCEMRI provide additional information about BBB that could be used for evaluation of vascular involvement in MS and monitoring treatment effectiveness.