A distributed parameter model of cerebral blood-tissue exchange with account of capillary transit time distribution

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling

PURPOSE

Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied.

METHODS

The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution.

RESULTS

The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s.

CONCLUSION

We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

Application of Distributed Parameter Model to Assessment of Glioma IDH Mutation Status by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Previous studies using contrast-enhanced imaging for glioma isocitrate dehydrogenase (IDH) mutation assessment showed promising yet inconsistent results, and this study attempts to explore this problem by using an advanced tracer kinetic model, the distributed parameter model (DP). Fifty-five patients with glioma examined using dynamic contrast-enhanced imaging sequence at a 3.0 T scanner were retrospectively reviewed. The imaging data were processed using DP, yielding the following parameters: blood flow F, permeability-surface area product PS, fractional volume of interstitial space Ve, fractional volume of intravascular space Vp, and extraction ratio E. The results were compared with the Tofts model. The Wilcoxon test and boxplot were utilized for assessment of differences of model parameters between IDH-mutant and IDH-wildtype gliomas. Spearman correlation r was employed to investigate the relationship between DP and Tofts parameters. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and quantified using the area under the ROC curve (AUC). Results showed that IDH-mutant gliomas were significantly lower in F (P = 0.018), PS (P < 0.001), Vp (P < 0.001), E (P < 0.001), and Ve (P = 0.002) than IDH-wildtype gliomas. In differentiating IDH-mutant and IDH-wildtype gliomas, Vp had the best performance (AUC = 0.92), and the AUCs of PS and E were 0.82 and 0.80, respectively. In comparison, Tofts parameters were lower in K^trans (P = 0.013) and Ve (P < 0.001) for IDH-mutant gliomas. No significant difference was observed in Kep (P = 0.525). The AUCs of K^trans, Ve, and Kep were 0.69, 0.79, and 0.55, respectively. Tofts-derived Ve showed a strong correlation with DP-derived Ve (r > 0.9, P < 0.001). K^trans showed a weak correlation with F (r < 0.3, P > 0.16) and a very weak correlation with PS (r < 0.06, P > 0.8), both of which were not statistically significant. The findings by DP revealed a tissue environment with lower vascularity, lower vessel permeability, and lower blood flow in IDH-mutant than in IDH-wildtype gliomas, being hostile to cellular differentiation of oncogenic effects in IDH-mutated gliomas, which might help to explain the better outcomes in IDH-mutated glioma patients than in glioma patients of IDH-wildtype. The advantage of DP over Tofts in glioma DCE data analysis was demonstrated in terms of clearer elucidation of tissue microenvironment and better performance in IDH mutation assessment.

Severe Blood-Brain Barrier Disruption in Cardioembolic Stroke

Background

Previous studies demonstrated that cardioembolism (CE) was prone to develop hemorrhagic transformation (HT), whereas hyper-permeability of blood–brain barrier (BBB) might be one reason for the development of HT. We, thus, aimed to investigate whether the BBB permeability (BBBP) was higher in CE stroke than other stroke subtypes in acute ischemic stroke (AIS) patients.

Methods

This study was a retrospective review of prospectively collected clinical and imaging database of AIS patients who underwent CT perfusion. Hypoperfusion was defined as Tmax >6 s. The average relative permeability-surface area product (rPS), reflecting the BBBP, was calculated within the hypoperfusion region (rPS_hypo). CE was diagnosed according to the international Trial of Org 10172 in Acute Stroke Treatment criteria. Receiver operating characteristics (ROC) curve analysis was used to determine predictive value of rPS_hypo for CE. Logistic regression was used to identify independent predictors for CE.

Results

A total of 187 patients were included in the final analysis [median age, 73 (61–80) years; 75 (40.1%) females; median baseline National Institutes of Health Stroke Scale score, 12 (7–16)]. Median rPS_hypo was 65.5 (35.8–110.1)%. Ninety-seven (51.9%) patients were diagnosed as CE. ROC analysis revealed that the optimal rPS_hypo threshold for CE was 86.71%. The value of rPS_hypo and the rate of rPS_hypo>86.71% were significantly higher in patients with CE than other stroke subtypes (p < 0.05), after adjusting for the potential confounds.

Conclusion

The extent of BBB disruption is more severe in CE stroke than other stroke subtypes during the hyperacute stage.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

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.

Patient characteristics influencing the variability of distributed parameter-based models in DCE-CT kinetic analysis

Kinetic parameter variability may be sensitive to kinetic model choice, kinetic model implementation or patient-specific effects. The purpose of this study was to assess their impact on the variability of dynamic contrast-enhanced computed tomography (DCE-CT) kinetic parameters. A total of 11 canine patients with sinonasal tumours received high signal-to-noise ratio, test-double retest DCE-CT scans. The variability for three distributed parameter (DP)-based models was assessed by analysis of variance. Mixed-effects modelling evaluated patient-specific effects. Inter-model variability (CV_inter ) was comparable to or lower than intra-model variability (CV_intra ) for blood flow (CV_inter :[4-28%], CV_intra :[28-31%]), fractional vascular volume (CV_inter :[3-17%], CV_intra :[16-19%]) and permeability-surface area product (CV_inter :[5-12%], CV_intra :[14-15%]). The kinetic models were significantly (P<0.05) impacted by patient characteristics for patient size, area underneath the curve of the artery and of the tumour. In conclusion, DP-based models demonstrated good agreement with similar differences between models and scans. However, high variability in the kinetic parameters and their sensitivity to patient size may limit certain quantitative applications.

Measurement of renal tissue oxygenation with blood oxygen level-dependent MRI and oxygen transit modeling

Blood oxygen level-dependent (BOLD) MRI data of kidney, while indicative of tissue oxygenation level (Po2), is in fact influenced by multiple confounding factors, such as R2, perfusion, oxygen permeability, and hematocrit. We aim to explore the feasibility of extracting tissue Po2 from renal BOLD data. A method of two steps was proposed: first, a Monte Carlo simulation to estimate blood oxygen saturation (SHb) from BOLD signals, and second, an oxygen transit model to convert SHb to tissue Po2. The proposed method was calibrated and validated with 20 pigs (12 before and after furosemide injection) in which BOLD-derived tissue Po2 was compared with microprobe-measured values. The method was then applied to nine healthy human subjects (age: 25.7 ± 3.0 yr) in whom BOLD was performed before and after furosemide. For the 12 pigs before furosemide injection, the proposed model estimated renal tissue Po2 with errors of 2.3 ± 5.2 mmHg (5.8 ± 13.4%) in cortex and -0.1 ± 4.5 mmHg (1.7 ± 18.1%) in medulla, compared with microprobe measurements. After injection of furosemide, the estimation errors were 6.9 ± 3.9 mmHg (14.2 ± 8.4%) for cortex and 2.6 ± 4.0 mmHg (7.7 ± 11.5%) for medulla. In the human subjects, BOLD-derived medullary Po2 increased from 16.0 ± 4.9 mmHg (SHb: 31 ± 11%) at baseline to 26.2 ± 3.1 mmHg (SHb: 53 ± 6%) at 5 min after furosemide injection, while cortical Po2 did not change significantly at ∼58 mmHg (SHb: 92 ± 1%). Our proposed method, validated with a porcine model, appears promising for estimating tissue Po2 from renal BOLD MRI data in human subjects.

Assessment of tumor blood flow distribution by dynamic contrast-enhanced CT

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability.

Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI

Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T(1) -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrast-enhanced MRI data.

Dynamic contrast-enhanced MRI of neuroendocrine hepatic metastases: A feasibility study using a dual-input two-compartment model

Neuroendocrine hepatic metastases exhibit various contrast uptake enhancement patterns in dynamic contrast-enhanced MRI. Using a dual-input two-compartment distributed parameter model, we analyzed the dynamic contrast-enhanced MRI datasets of seven patient study cases with the aim to relate the tumor contrast uptake patterns to parameters of tumor microvasculature. Simulation studies were also performed to provide further insights into the effects of individual microcirculatory parameter on the tumor concentration-time curves. Although the tumor contrast uptake patterns can be influenced by many parameters, initial results indicate that hepatic blood flow and the ratio of fractional vascular volume to fractional interstitial volume may potentially distinguish between the patterns of neuroendocrine hepatic metastases.

Early time points perfusion imaging: theoretical analysis of correction factors for relative cerebral blood flow estimation given local arterial input function

If local arterial input function (AIF) could be identified, we present a theoretical approach to generate a correction factor based on local AIF for the estimation of relative cerebral blood flow (rCBF) under the framework of early time points perfusion imaging (ET). If C(t), the contrast agent bolus concentration signal time course, is used for rCBF estimation in ET, the correction factor for C(t) is the integral of its local AIF. The recipe to apply the correction factor is to divide C(t) by the integral of its local AIF to obtain the correct rCBF. By similar analysis, the correction factor for the maximum derivative (MD1) of C(t) is the maximum signal of AIF and the correction factor for the maximum second derivative (MD2) of C(t) is the maximum derivative of AIF. In the specific case of using normalized gamma-variate function as a model for AIF, the correction factor for C(t) (but not for MD1) at the time to reach the maximum derivative is relatively insensitive to the shape of the local AIF.

A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo results

Accurate quantification of pharmacokinetic model parameters in tracer kinetic imaging experiments requires correspondingly accurate determination of the arterial input function (AIF). Despite significant effort expended on methods of directly measuring patient-specific AIFs in modalities as diverse as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), dynamic positron emission tomography (PET), and perfusion computed tomography (CT), fundamental and technical difficulties have made consistent and reliable achievement of that goal elusive. Here, we validate a new algorithm for AIF determination, the Monte Carlo blind estimation (MCBE) method (which is described in detail and characterized by extensive simulations in a companion paper), by comparing AIFs measured in DCE-MRI studies of eight brain tumor patients with results of blind estimation. Blind AIFs calculated with the MCBE method using a pool of concentration-time curves from a region of normal brain tissue were found to be quite similar to the measured AIFs, with statistically significant decreases in fit residuals observed in six of eight patients. Biases between the blind and measured pharmacokinetic parameters were the dominant source of error. Averaged over all eight patients, the mean biases were +7% in K(trans), 0% in k(ep), -11% in v(p) and +10% in v(e). Corresponding uncertainties (median absolute deviation from the best fit line) were 0.0043 min(-1) in K(trans), 0.0491 min(-1) in k(ep), 0.29% in v(p) and 0.45% in v(e). The use of a published population-averaged AIF resulted in larger mean biases in three of the four parameters (-23% in K(trans), -22% in k(ep), -63% in v(p)), with the bias in v(e) unchanged, and led to larger uncertainties in all four parameters (0.0083 min(-1) in K(trans), 0.1038 min(-1) in k(ep), 0.31% in v(p) and 0.95% in v(e)). When blind AIFs were calculated from a region of tumor tissue, statistically significant decreases in fit residuals were observed in all eight patients despite larger deviations of these blind AIFs from the measured AIFs. The observed decrease in root-mean-square fit residuals between the normal brain and tumor tissue blind AIFs suggests that the local blood supply in tumors is measurably different from that in normal brain tissue and that the proposed method is able to discriminate between the two. We have shown the feasibility of applying the MCBE algorithm to DCE-MRI data acquired in brain, finding generally good agreement with measured AIFs and decreased biases and uncertainties relative to the use of a population-averaged AIF. These results demonstrate that the MCBE algorithm is a useful alternative to direct AIF measurement in cases where acquisition of high-quality arterial input function data is difficult or impossible.

Technical aspects of MR perfusion

The most common methods for measuring perfusion with MRI are arterial spin labelling (ASL), dynamic susceptibility contrast (DSC-MRI), and T(1)-weighted dynamic contrast enhancement (DCE-MRI). This review focuses on the latter approach, which is by far the most common in the body and produces measures of capillary permeability as well. The aim is to present a concise but complete overview of the technical issues involved in DCE-MRI data acquisition and analysis. For details the reader is referred to the references. The presentation of the topic is essentially generic and focuses on technical aspects that are common to all DCE-MRI measurements. For organ-specific problems and illustrations, we refer to the other papers in this issue. In Section 1 "Theory" the basic quantities are defined, and the physical mechanisms are presented that provide a relation between the hemodynamic parameters and the DCE-MRI signal. Section 2 "Data acquisition" discusses the issues involved in the design of an optimal measurement protocol. Section 3 "Data analysis" summarizes the steps that need to be taken to determine the hemodynamic parameters from the measured data.

A novel estimation method for physiological parameters in dynamic contrast-enhanced MRI: application of a distributed parameter model using Fourier-domain calculations

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.

Computed tomography assessment of cerebral perfusion using a distributed parameter tracer kinetics model: validation with H(2)((15))O positron emission tomography measurements and initial clinical experience in patients with acute stroke

We describe a distributed parameter (DP) model for tracer kinetic analysis in brain and validate the derived perfusion values with positron emission tomography (PET) scans. The proposed model is applied on actual clinical cases of hemispheric stroke. Nine patients with experienced transient ischaemic attack or minor stroke and a stenosis of the internal carotid artery were referred for computed tomography (CT) and PET imaging. The applicability of the DP model in clinical practice was tested in seven patients with acute stroke who received a baseline perfusion CT study and a noncontrast follow-up CT study after 2.4+/-1.8 days. The mean blood flow (F) value for all patients with carotid stenosis in the pooled data (54 regions of interest (ROIs)) was 37.9+/-11.2 mL/min per 100 g in perfusion CT and 35.6+/-9.8 mL/min per 100 g in perfusion PET imaging [r=0.77 (P=0.00)]. Regression analysis of the pooled ROIs for every patient revealed significant correlation between F values in seven patients [r=0.50 to 0.79 (r(2)-values ranged from 0.45 to 0.79), (0.01 < or = P < or = 0.05)]. Parametric maps that corresponded to all physiologic parameters were generated for every perfusion CT in the patients with acute stroke using the DP model. The ischaemic area was better delineated in F, intravascular blood volume and lag time (t(lag)) maps. The correlation coefficient comparing the visually outlined regions of abnormality between the t(lag) parametric map and the follow-up CT scans was 0.81 (P=0.003). In conclusion, DP physiological model using more realistic pharmacokinetics is feasible in dynamic contrast-enhanced CT of the brain in patients with acute and chronic cerebrovascular disease.

Prediction of subsequent hemorrhage in acute ischemic stroke using permeability CT imaging and a distributed parameter tracer kinetic model

PURPOSE

Hemorrhagic transformation (HT) is a common consequence of infarction independent of thrombolytic therapy. Our purpose was to examine if permeability imaging in admission perfusion CT data of patients with acute stroke might indicate a subsequent HT by imaging the disrupted permeability barriers between blood and brain.

MATERIALS AND METHODS

A distributed parameter model analysis of the perfusion data were used to analyze the admission perfusion surveys of eight patients with HT of the initial infarct without thrombolysis. The perfusion findings of these patients were compared with those of eight age- and gender-matched patients from the initial group that did not present with HT.

RESULTS

The applied statistics for comparing the ischemic voxels with the contralateral healthy tissue showed significantly higher permeability-surface product (PS), extraction ratio (E), and extracellular extravascular space volume (V(EES)) in the ischemic voxels (P range, 0.05-0.0001). In the patients without HT, the PS, E and V(EES) values in the ischemic voxels were not significantly different from those in the normal region.

CONCLUSION

Our findings indicate that early perfusion CT physiological imaging in stroke is a promising tool for identifying patients with higher risk of HT and, thus, may serve to guide therapeutic options.

Cerebral perfusion mapping using a robust and efficient method for deconvolution analysis of dynamic contrast-enhanced images

Dynamic contrast-enhanced (DCE) imaging using MRI or CT is emerging as a promising tool for diagnostic imaging of cerebral disorders and the monitoring of tumor response to treatment. In this study, we present a robust and efficient deconvolution method based on a linearized model of the impulse residue function, which allows for the mapping of functional cerebral parameters such as cerebral blood flow, volume, mean transit time, and permeability. Monte Carlo simulation studies were performed to study the accuracy and stability of the proposed method, before applying it to clinical study cases of patients with cerebral tumors imaged using DCE CT. Functional parameter maps generated using the proposed method revealed the locations of the cerebral tumors and were found to be of sufficiently good clarity for marked regional differences in tissue vascularity and permeability to be assessed. In particular, tumor visualization and delineation were found to be better on the parameter maps that were indicative of the breakdown of the blood-brain barrier.