Global Arterial Input Function Research Articles

Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing.

There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety's tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF ≤ 10%) and a high-risk group (LSF > 10%). Results: In this patient cohort, LSF was positively correlated with QTM velocity |u| (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety's parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM |u| (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety's Ktrans (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and Ve (0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for |u|, 0.80 for Ve and 0.74 for Ktrans. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.

Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network.

PurposeProof‐of‐concept study of mapping renal blood flow vector field according to the inverse solution to a mass transport model of time resolved tracer‐labeled MRI data.Theory and MethodsTo determine tissue perfusion according to the underlying physics of spatiotemporal tracer concentration variation, the mass transport equation is integrated over a voxel with an approximate microvascular network for fitting time‐resolved tracer imaging data. The inverse solution to the voxelized transport equation provides the blood flow vector field, which is referred to as quantitative transport mapping (QTM). A numerical microvascular network modeling the kidney with computational fluid dynamics reference was used to verify the accuracy of QTM and the current Kety’s method that uses a global arterial input function. Multiple post‐label delay arterial spin labeling (ASL) of the kidney on seven subjects was used to assess QTM in vivo feasibility.ResultsAgainst the ground truth in the numerical model, the error in flow estimated by QTM (18.6%) was smaller than that in Kety’s method (45.7%, 2.5‐fold reduction). The in vivo kidney perfusion quantification by QTM (cortex: 443 ± 58 mL/100 g/min and medulla: 190 ± 90 mL/100 g/min) was in the range of that by Kety’s method (482 ± 51 mL/100 g/min in the cortex and 242 ± 73 mL/100 g/min in the medulla), and QTM provided better flow homogeneity in the cortex region.ConclusionsQTM flow velocity mapping is feasible from multi‐delay ASL MRI data based on inverting the transport equation. In a numerical simulation, QTM with deconvolution in space and time provided more accurate perfusion quantification than Kety’s method with deconvolution in time only.

An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images.

One of the key elements in dynamic contrast enhanced (DCE) image analysis is the arterial input function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used to estimate the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the AIF in the vessel branches, as well as extravasation of the contrast agent (CA) to the extravascular-extracellular space. Profiles of the CA concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (dynamic contrast enhanced T1 ) images of patients with glioblastoma multiforme tumors. The simulation study showed that, compared with the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (Ktrans , vp and Kb ). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the CA concentration profile in both the normal areas and the tumor area, where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.

A PET-Guided Framework Supports a Multiple Arterial Input Functions Approach in DSC-MRI in Acute Stroke.

In acute stroke, arterial-input-function (AIF) determination is essential for obtaining perfusion estimates with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging (DSC-MRI). Standard DSC-MRI postprocessing applies single AIF selection, ie, global AIF. Physiological considerations, however, suggest that a multiple AIFs selection method would improve perfusion estimates to detect penumbral flow. In this study, we developed a framework based on comparable DSC-MRI and positron emission tomography (PET) images to compare the two AIF selection approaches and assess their performance in penumbral flow detection in acute stroke. In a retrospective analysis of 17 sub(acute) stroke patients with consecutive MRI and PET scans, voxel-wise optimized AIFs were calculated based on the kinetic model as derived from both imaging modalities. Perfusion maps were calculated based on the optimized-AIF using two methodologies: (1) Global AIF and (2) multiple AIFs as identified by cluster analysis. Performance of penumbral-flow detection was tested by receiver-operating characteristics (ROC) curve analysis, ie, the area under the curve (AUC). Large variation of optimized AIFs across brain voxels demonstrated that there is no optimal single AIF. Subsequently, the multiple-AIF method (AUC range over all maps: .82-.90) outperformed the global AIF methodology (AUC .72-.85) significantly. We provide PET imaging-based evidence that a multiple AIF methodology is beneficial for penumbral flow detection in comparison with the standard global AIF methodology in acute stroke.

Tmax Determined Using a Bayesian Estimation Deconvolution Algorithm Applied to Bolus Tracking Perfusion Imaging: A Digital Phantom Validation Study.

Purpose:The Bayesian estimation algorithm improves the precision of bolus tracking perfusion imaging. However, this algorithm cannot directly calculate Tmax, the time scale widely used to identify ischemic penumbra, because Tmax is a non-physiological, artificial index that reflects the tracer arrival delay (TD) and other parameters. We calculated Tmax from the TD and mean transit time (MTT) obtained by the Bayesian algorithm and determined its accuracy in comparison with Tmax obtained by singular value decomposition (SVD) algorithms.Methods:The TD and MTT maps were generated by the Bayesian algorithm applied to digital phantoms with time-concentration curves that reflected a range of values for various perfusion metrics using a global arterial input function. Tmax was calculated from the TD and MTT using constants obtained by a linear least-squares fit to Tmax obtained from the two SVD algorithms that showed the best benchmarks in a previous study. Correlations between the Tmax values obtained by the Bayesian and SVD methods were examined.Results:The Bayesian algorithm yielded accurate TD and MTT values relative to the true values of the digital phantom. Tmax calculated from the TD and MTT values with the least-squares fit constants showed excellent correlation (Pearson’s correlation coefficient = 0.99) and agreement (intraclass correlation coefficient = 0.99) with Tmax obtained from SVD algorithms.Conclusions:Quantitative analyses of Tmax values calculated from Bayesian-estimation algorithm-derived TD and MTT from a digital phantom correlated and agreed well with Tmax values determined using SVD algorithms.

NIMG-52DYNAMIC CONTRAST ENHANCED MR ESTIMATION OF PHARMACOKINETIC PARAMETERS OF GLIOBLASTOMA MULTIFORME TUMORS: A COMPARISON STUDY FOR PERITUMORAL AND GLOBAL ARTERIAL INPUT FUNCTIONS USING MODEL SELECTION TECHNIQUE

Identification of Arterial Input Function (AIF) is one of the crucial steps for quantitative analysis of tissue vascular permeability in Dynamic Contrast Enhanced Magnetic Resonance Imaging studies. In practice, the global AIF is usually identified by experts, which never accounts for dispersion, delay, and partial volume effects. In this pilot study, it is hypothesized that embedded tumors are supplied by their surrounding tissue. This study compares the MR estimates of Pharmacokinetic parameters (PK) in Glioblastoma-Multiforme (GBM) tumors for Peritumoral-derived AIF (PTAIF) as opposed to the use of global AIF. Using the trace of the contrast agent concentration (∝ to relaxivity change, ΔR1) in seventeen treatment-naive GBM patients, a neuroradiologist manually picked the global AIF for each patient. A series of two-dimensional Region of Interests were drawn on the periphery of each tumor in each slice to generate a three-dimensional ROI for encompassing the whole tumor. Then the ΔR1 profile was averaged over the 3D-ROI to generate the PTAIF. For each patient, the global AIF and PTAIF profiles were normalized to the brain white matter. Using the Model-Selection technique and Maximum-Likelihood algorithm, all PK parameters of three Toft-derived models (models 1, 2 and 3), were estimated for two AIFs. The results revealed that PK analysis using PTAIF generates higher plasma volumes in model-1 and model-2 (20% and 60%) with 52% lower plasma volume in model-3 compared to global AIF. PK analysis using PTAIF generates lower transfer constants in models-2 and 3 (54% and 90% for outward-transfer-constant of model-2 and 3 respectively and 13% for backward-transfer-constant of model-3). Also, the extra-vascular-extra-cellular space estimated by the PTAIF compared to the AIF shows significant reduction. The results of this study imply that using Tissue Input Function such as PTAIF compared to the globally selected AIF may highly change the estimates of the PK parameters.

Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high‐ and low‐grade glioma using dynamic contrast‐enhanced (DCE) MRI

Purpose:To modify the generalized tracer kinetic model (GTKM) by introducing an additional tissue uptake leakage compartment in extracellular extravascular space (LTKM). In addition, an implicit determination of voxel‐wise local arterial input function (AIF) Cp(t) was performed to see whether these changes help in better discrimination between low‐ and high‐grade glioma using dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI).Materials and Methods:The modified model (LTKM) was explored and fitted to the concentration–time curve C(t) of each voxel, in which the local AIF Cp(t) could be estimated by a time invariant convolution approximation based on a separately measured global AIF Ca(t). A comparative study of tracer kinetic analysis was performed on 184 glioma patients using DCE‐MRI data on 1.5T and 3T MRI systems.Results:The LTKM analysis provided more accurate pharmacokinetic parameters as evidenced by their relative constancy with respect to the length of concentration–time curve used. In addition, LTKM with local AIF resulted in improved discrimination between low‐grade and high‐grade gliomas.Conclusion:LTKM with local AIF provides more accurate estimation of physiological parameters and improves discrimination between low‐grade and high‐grade gliomas as compared with GTKM. J. Magn. Reson. Imaging 2013;38:677–688. © 2013 Wiley Periodicals, Inc.

Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high‐ and low‐grade glioma using dynamic contrast‐enhanced (DCE) MRI

To modify the generalized tracer kinetic model (GTKM) by introducing an additional tissue uptake leakage compartment in extracellular extravascular space (LTKM). In addition, an implicit determination of voxel-wise local arterial input function (AIF) Cp (t) was performed to see whether these changes help in better discrimination between low- and high-grade glioma using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). The modified model (LTKM) was explored and fitted to the concentration-time curve C(t) of each voxel, in which the local AIF Cp (t) could be estimated by a time invariant convolution approximation based on a separately measured global AIF Ca (t). A comparative study of tracer kinetic analysis was performed on 184 glioma patients using DCE-MRI data on 1.5T and 3T MRI systems. The LTKM analysis provided more accurate pharmacokinetic parameters as evidenced by their relative constancy with respect to the length of concentration-time curve used. In addition, LTKM with local AIF resulted in improved discrimination between low-grade and high-grade gliomas. LTKM with local AIF provides more accurate estimation of physiological parameters and improves discrimination between low-grade and high-grade gliomas as compared with GTKM.

The Role of Bolus Delay and Dispersion in Predictor Models for Stroke

In combination with diffusion-weighted imaging, perfusion-weighted imaging parameters are hypothesized to detect tissue at risk of infarction in patients with acute stroke. Recent studies have suggested that in addition to perfusion deficits, vascular flow parameters indicating bolus delay and/or dispersion may also contain important predictive information. This work investigates the infarct risk associated with delay/dispersion using multiparametric predictor models. Predictor models were developed using specific combinations of perfusion parameters calculated using global arterial input function deconvolution (where perfusion is biased by dispersion), local arterial input function deconvolution (where perfusion has minimal dispersion bias), and parameters approximating bolus delay/dispersion. We also compare predictor models formed using summary parameters (which primarily reflect delay/dispersion). The models were trained on 15 patients with acute stroke imaged at 3 to 6 hours. The global arterial input function models performed significantly better than their local arterial input function counterparts. Furthermore, in a paired comparison, the models including the delay/dispersion parameter performed significantly better than those without. There was no significant difference between the best deconvolution model and the best summary parameter model. Delay and dispersion information is important to achieve accurate infarct prediction in the acute time window.

Evaluation of signal formation in local arterial input function measurements of dynamic susceptibility contrast MRI

Correct arterial input function (AIF) measurements in dynamic susceptibility contrast-MRI are crucial for quantification of the hemodynamic parameters. Often a single global AIF is selected near a large brain-feeding artery. Alternatively, local AIF measurements aim for voxel-specific AIFs from smaller arteries. Because local AIFs are measured higher in the arterial-tree, it is assumed that these will reflect the true input of the microvasculature much better. However, do the measured local AIFs reflect the true concentration-time curves of small arteries? To answer this question, in vivo data were used to evaluate local AIF candidates selected based on two different types of angiograms. For interpretation purposes, a 3D numerical model that simulated partial-volume effects in local AIF measurements was created and the simulated local AIFs were compared to the ground truth. The findings are 2-fold. First, the in vivo data showed that the shape-characteristics of local AIFs are similar to the shape-characteristics of gray matter concentration-time curves. Second, these findings are supported by the simulations showing broadening of the measured local AIFs compared to the ground truth. These findings are suggesting that local AIF measurements do not necessarily reflect the true concentration-time curve in small arteries.

Dynamic susceptibility contrast MRI with localized arterial input functions

Compared to gold-standard measurements of cerebral perfusion with positron emission tomography using H(2)[(15)O] tracers, measurements with dynamic susceptibility contrast MR are more accessible, less expensive, and less invasive. However, existing methods for analyzing and interpreting data from dynamic susceptibility contrast MR have characteristic disadvantages that include sensitivity to incorrectly modeled delay and dispersion in a single, global arterial input function. We describe a model of tissue microcirculation derived from tracer kinetics that estimates for each voxel a unique, localized arterial input function. Parameters of the model were estimated using Bayesian probability theory and Markov-chain Monte Carlo, circumventing difficulties arising from numerical deconvolution. Applying the new method to imaging studies from a cohort of 14 patients with chronic, atherosclerotic, occlusive disease showed strong correlations between perfusion measured by dynamic susceptibility contrast MR with localized arterial input function and perfusion measured by quantitative positron emission tomography with H(2)[(15)O]. Regression to positron emission tomography measurements enabled conversion of dynamic susceptibility contrast MR to a physiologic scale. Regression analysis for localized arterial input function gave estimates of a scaling factor for quantitation that described perfusion accurately in patients with substantial variability in hemodynamic impairment.

Absolute quantification of cerebral blood flow: correlation between dynamic susceptibility contrast MRI and model-free arterial spin labeling

To compare absolute cerebral blood flow (CBF) estimates obtained by model-free arterial spin labeling (ASL) and dynamic susceptibility contrast MRI (DSC-MRI), corrected for partial volume effects (PVEs). CBF was measured using DSC-MRI and model-free ASL (quantitative signal targeting with alternating radiofrequency labeling of arterial regions) at 3 T in 15 subjects with brain tumor, and the two modalities were compared with regard to CBF estimates in normal gray matter (GM) and DSC-to-ASL CBF ratios in selected tumor regions. The DSC-MRI CBF maps were calculated using a global arterial input function (AIF) from the sylvian-fissure region, but, in order to minimize PVEs, the AIF time integral was rescaled by a venous output function time integral obtained from the sagittal sinus. In GM, the average DSC-MRI CBF estimate was 150+/-45 ml/(min 100 g) (mean+/-SD) while the corresponding ASL CBF was 44+/-10 ml/(min 100 g). The linear correlation between GM CBF estimates obtained by DSC-MRI and ASL was r=.89, and observed DSC-to-ASL CBF ratios differed by less than 3% between GM and tumor regions. A satisfactory positive linear correlation between the CBF estimates obtained by model-free ASL and DSC-MRI was observed, and DSC-to-ASL CBF ratios showed no obvious tissue dependence.

Minimising the effects of bolus dispersion in bolus‐tracking MRI

Bolus-tracking perfusion measurements in patients with vascular abnormalities are often unreliable, because delay and/or dispersion of the bolus within the vessels distorts the measured arterial input function (AIF). Erroneous measurements of perfusion can be identified by examining the measured response function, the shape of which is determined by both the tissue and arterial retention. In this work, an accurate response function is extracted by combining maximum-likelihood expectation-maximisation deconvolution, regularised using an oscillation index, with subsequent wavelet thresholding. Simulations show that this method recovers both the smooth-dispersed and the sharp-delayed response functions. This enables regions where the bolus is delayed and/or dispersed to be identified when the methodology is applied to data from patients with vascular abnormalities. Simulations also demonstrate robust and accurate perfusion estimates when there is no bolus delay and/or dispersion. The presence of delay and/or dispersion in the response function suggests that the perfusion measurements are erroneous, and that the global AIF is an inaccurate approximation to the true AIF in these regions. Perfusion measurements are corrected within the affected regions by defining a regional AIF from the independent component analysis of the dynamic susceptibility contrast MRI data. The regional AIF is shown to remove the delay and dispersion, improving the accuracy of the perfusion maps.

Bolus dispersion issues related to the quantification of perfusion MRI data

Quantification of cerebral blood flow (CBF) using dynamic-susceptibility contrast (DSC) MRI relies on the deconvolution of the arterial input function (AIF). The AIF is commonly measured in a major artery (e.g., the middle cerebral artery), and the estimated function is used as a global AIF for the whole slice. However, the presence of bolus delay and dispersion between the artery and the tissue of interest can introduce significant errors in CBF quantification. While several methods have been introduced to minimize or eliminate the effects of bolus delay, the correction of bolus dispersion is more difficult to address because it requires a model for the vascular bed. This article summarizes how this dispersion effect can be incorporated into the model for CBF quantification, and discusses the magnitude of the errors introduced. Furthermore, alternative methods for correcting or minimizing the effects of bolus dispersion in the quantification of CBF are reviewed.

Defining a local arterial input function for perfusion MRI using independent component analysis.

Quantification of cerebral blood flow (CBF) using dynamic-susceptibility contrast MRI relies on the deconvolution of the arterial input function (AIF), which is commonly estimated from the signal changes in a major artery. However, it has been shown that the presence of bolus delay/dispersion between the artery and the tissue of interest can be a significant source of error. These effects could be minimized if a local AIF were used, although the measurement of a local AIF can be problematic. This work describes a new methodology to define a local AIF using independent component analysis (ICA). The methodology was tested on data from patients with various cerebrovascular abnormalities and compared to the conventional approach of using a global AIF. The new methodology produced higher CBF and shorter mean transit time values (compared to the global AIF case) in areas with distorted AIFs, suggesting that the effects of delay/dispersion are minimized. The minimization of these effects using the calculated local AIF should lead to a more accurate quantification of CBF, which can have important implications for diagnosis and management of patients with cerebral ischemia.

Effects of tracer arrival time on flow estimates in MR perfusion-weighted imaging.

A common technique for calculating cerebral blood flow (CBF) and mean transit time (MTT) is to track a bolus of contrast agent using perfusion-weighted MRI (PWI) and to deconvolve the change in concentration with an arterial input function (AIF) using singular value decomposition (SVD). This method has been shown to often overestimate the volume of tissue that infarcts and in cases of severe vasculopathy to produce CBF maps that are inconsistent with clinical presentation. This study examines the effects of tracer arrival time differences between tissue and a user-selected global AIF on flow estimates. CBF and MTT were calculated in both numerically simulated and clinically acquired PWI data where the AIF and tissue signals were shifted backward and forward in time with respect to one another. Results show that when the AIF leads the tissue, CBF is underestimated independent of extent of delay, but dependent on MTT. When the AIF lags the tissue, flow may be over- or underestimated depending on MTT and extent of timing differences. These conditions may occur in practice due to the application of a user-selected AIF that is not the "true AIF" and therefore caution must be taken in interpreting CBF and MTT estimates.