Local Arterial Input Function Research Articles

Multi-stage automated local arterial input function selection in perfusion MRI.

Cerebral blood flow (CBF) quantification using dynamic-susceptibility contrast MRI can be achieved via model-independent deconvolution, with local arterial input function (AIF) deconvolution methods identifying multiple arterial regions with unique corresponding arterial input functions. The clinical application of local AIF methods necessitates an efficient and fully automated solution. To date, such local AIF methods have relied on the computation of a singular surrogate measure of bolus arrival time or custom arterial scoring functions to infer vascular supply origins. This paper aims to introduce a new local AIF method that alternatively utilises a multi-stage approach to perform AIF selection. A fully automated, multi-stage local AIF method is proposed, leveraging both signal-based cluster analysis and priority flooding to define arterial regions and their corresponding vascular supply origins. The introduced method was applied to data from four patients with cerebrovascular disease who showed significant artefacts when using a prevailing automated local AIF method. The immediately apparent image artefacts found using the pre-existing method due to poor AIF selection were found to be absent when using the proposed method. The results suggest the proposed solution provides a more robust approach to perfusion quantification than currently available fully automated local AIF methods.

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI.

Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (COCMRI) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CODCE) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland–Altman plot were used to compare COCMRI and CODCE. The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) COCMRI and CODCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation (r = 0.82, P < .01) and good agreement between COCMRI and CODCE. The CODCE is consistent with the reference standard COCMRI. This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80% of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

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.

Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues

Measurements of arterial input function (AIF) can have large systematic errors at standard contrast agent doses in dynamic contrast enhanced MRI (DCE-MRI). We compared measured AIFs from low dose (AIFLD) and standard dose (AIFSD) contrast agent injections, as well as the AIF derived from a muscle reference tissue and artery (AIFref). Twenty-two prostate cancer patients underwent DCE-MRI. Data were acquired on a 3T scanner using an mDixon sequence. Gadobenate dimeglumine was injected twice, at doses of 0.015 and 0.085mmol/kg. Directly measured AIFs were fitted with empirical mathematical models (EMMs) and compared to the AIF derived from a muscle reference tissue (AIFref). EMMs accurately fitted the AIFs. The 1st and 2nd pass peaks were visualized in AIFLD, but not in AIFSD, thus the peak and shape of AIFSD could not be accurately measured directly. The average scaling factor between AIFSD and AIFLD in the washout phase was only 56% of the contrast dose ratio (~6:1). The shape and magnitude of AIFref closely approximated that of AIFLD after empirically determined dose-dependent normalization. This suggests that AIFref may be a good approximation of the local AIF.

Automatic detection of local arterial input functions through Independent Component Analysis on Dynamic Contrast enhanced Magnetic Resonance Imaging.

Arterial Input Function (AIF) is obtained from perfusion studies as a basic parameter for the calculus of hemodynamic variables used as surrogate markers of the vascular status of tissues. However, at present, its identification is made manually leading to high subjectivity, low repeatability and considerable time consumption. We propose an alternative method to automatically identify local AIF in perfusion images using Independent Component Analysis.

A novel method for detecting striatal hypoperfusion in Parkinson disease ‐Saturated Iron oxide Nanoparticle Contrast (SINC) MRI (LB79)

Striatal dopaminergic denervation is a major characteristic of Parkinson’s disease (PD). Such neuronal damage may lead to disrupted perfusion that reflects changes in regional metabolism and neural activity. Current imaging findings regarding striatal perfusion alterations in PD remain inconclusive. The present study proposed a new perfusion weighted MRI technique that takes advantage of saturated iron oxide nanoparticle contrast (SINC) to estimate cerebral blood flow (CBF), cerebral blood volume (CBV) , and the mean transit time (MTT) in a PD rat model induced by 6‐hydroxydopamine. This SINC‐MRI method uses a high dose of iron oxide nanoparticles (IRON) for inducing signal changes on gradient echo T2* images acquired repeatedly over 60 time frames. As soon as IRON is injected, the signal decreases rapidly from a baseline to an undershoot, and then reaches a saturated steady state. CBV is represented directly by ΔR2* between the baseline and the steady state. Moreover, the derivative of the measured signal changes was modeled as the delta function of impulse response by fitting pixelwisely the signal‐to‐time curve using the piecewise Hermite polynomial function. The modeled signal was then deconvoluted through a local arterial input function to obtain the tissue concentration of the contrast agent so as to estimate CBF and MTT. Our results showed that, in the lesioned striatum, there were consistent decreases in the CBV and CBF by approximately 4‐7% as compared to the intact striatum. Importantly, this difference was not found by conventional dynamic susceptibility contrast MRI method. SINC‐MRI appears as a reliable and sensitive approach in detecting subtle perfusion changes.

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.

Hemodynamic Segmentation of Brain Perfusion Images with Delay and Dispersion Effects Using an Expectation-Maximization Algorithm

Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified.

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.

Ultrasound perfusion analysis combining bolus-tracking and burst-replenishment

A new signal model and processing method for quantitative ultrasound perfusion analysis is presented, called bolus-and-burst. The method has the potential to provide absolute values of blood flow, blood volume, and mean transit time. Furthermore, it provides an estimate of the local arterial input function which characterizes the arterial tree, allowing accurate estimation of the bolus arrival time. The method combines two approaches to ultrasound perfusion analysis: bolus-tracking and burst-replenishment. A pharmacokinetic model based on the concept of arterial input functions and tissue residue functions is used to model both the bolus and replenishment parts of the recording. The pharmacokinetic model is fitted to the data using blind deconvolution. A preliminary assessment of the new perfusion-analysis method is presented on clinical recordings.

Parallel perfusion imaging processing using GPGPU

Background and purposeThe objective of brain perfusion quantification is to generate parametric maps of relevant hemodynamic quantities such as cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) that can be used in diagnosis of acute stroke. These calculations involve deconvolution operations that can be very computationally expensive when using local Arterial Input Functions (AIF). As time is vitally important in the case of acute stroke, reducing the analysis time will reduce the number of brain cells damaged and increase the potential for recovery. MethodsGPUs originated as graphics generation dedicated co-processors, but modern GPUs have evolved to become a more general processor capable of executing scientific computations. It provides a highly parallel computing environment due to its large number of computing cores and constitutes an affordable high performance computing method. In this paper, we will present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose Graphics Processor Units) using the CUDA programming model. We present the serial and parallel implementations of such algorithms and the evaluation of the performance gains using GPUs. ResultsOur method has gained a 5.56 and 3.75 speedup for CT and MR images respectively. ConclusionsIt seems that using GPGPU is a desirable approach in perfusion imaging analysis, which does not harm the quality of cerebral hemodynamic maps but delivers results faster than the traditional computation.

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.

Validating a Local Arterial Input Function Method for Improved Perfusion Quantification in Stroke

In bolus-tracking perfusion magnetic resonance imaging (MRI), temporal dispersion of the contrast bolus due to stenosis or collateral supply presents a significant problem for accurate perfusion quantification in stroke. One means to reduce the associated perfusion errors is to deconvolve the bolus concentration time-course data with local Arterial Input Functions (AIFs) measured close to the capillary bed and downstream of the arterial abnormalities causing dispersion. Because the MRI voxel resolution precludes direct local AIF measurements, they must be extrapolated from the surrounding data. To date, there have been no published studies directly validating these local AIFs. We assess the effectiveness of local AIFs in reducing dispersion-induced perfusion error by measuring the residual dispersion remaining in the local AIF deconvolved perfusion maps. Two approaches to locating the local AIF voxels are assessed and compared with a global AIF deconvolution across 19 bolus-tracking data sets from patients with stroke. The local AIF methods reduced dispersion in the majority of data sets, suggesting more accurate perfusion quantification. Importantly, the validation inherently identifies potential areas for perfusion underestimation. This is valuable information for the identification of at-risk tissue and management of stroke patients.

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.

Toward local arterial input functions in dynamic contrast‐enhanced MRI

To present a method for estimating the local arterial input function (AIF) within a dynamic contrast-enhanced MRI scan, based on the alternating minimization with model (AMM) method. This method clusters a subset of data into representative curves, which are then input to the AMM algorithm to return a parameterized AIF and pharmacokinetic parameters. Computer simulations are used to investigate the accuracy with which the AMM is able to estimate the true AIF as a function of the input tissue curves. Simulations show that a power law relates uncertainty in kinetic parameters and SNR and heterogeneity of the input. Kinetic parameters calculated with the measured AIF are significantly different from those calculated with either a global (P < 0.005) or a local input function (P = 0.0). The use of local AIFs instead of measured AIFs yield mean lesion-averaged parameter changes: K(trans): +24% [+15%, +70%], k(ep): +13% [-36%, +300%]. Globally estimated input functions yield mean lesion-averaged changes: K(trans): +9% [-38%, +65%], k(ep): +13% [-100%, +400%]. The observed improvement in fit quality with local AIFs was found to be significant when additional free parameters were accounted for using the Akaike information criterion. Local AIFs result in significantly different kinetic parameter values. The statistically significant improvement in fit quality suggests that changes in parameter estimates using local AIFs reflect differences in underlying tissue physiology.

Use of a reference tissue and blood vessel to measure the arterial input function in DCEMRI

Accurate measurement of the arterial input function is critical for quantitative evaluation of dynamic contrast enhanced magnetic resonance imaging data. Use of the reference tissue method to derive a local arterial input function avoided large errors associated with direct arterial measurements, but relied on literature values for K(trans) and v(e). We demonstrate that accurate values of K(trans) and v(e) in a reference tissue can be measured by comparing contrast media concentration in a reference tissue to plasma concentrations measured directly in a local artery after the 1-2 passes of the contrast media bolus-when plasma concentration is low and can be measured accurately. The values of K(trans) and v(e) calculated for the reference tissue can then be used to derive a more complete arterial input function including the first pass of the contrast bolus. This new approach was demonstrated using dynamic contrast enhanced magnetic resonance imaging data from rodent hind limb. Values obtained for K(trans) and v(e) in muscle, and the shape and amplitude of the derived arterial input function are consistent with published results.

A Spatio-Temporal Deconvolution Method to Improve Perfusion CT Quantification

Perfusion imaging is a useful adjunct to anatomic imaging in numerous diagnostic and therapy-monitoring settings. One approach to perfusion imaging is to assume a convolution relationship between a local arterial input function and the tissue enhancement profile of the region of interest via a "residue function" and subsequently solve for this residue function. This ill-posed problem is generally solved using singular-value decomposition based approaches, and the hemodynamic parameters are solved for each voxel independently. In this paper, we present a formulation which incorporates both spatial and temporal correlations, and show through simulations that this new formulation yields higher accuracy and greater robustness with respect to image noise. We also show using rectal cancer tumor images that this new formulation results in better segregation of normal and cancerous voxels.

Model‐based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)‐MRI

A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced (DCE)-MRI data was developed. This algorithm uses a parameterized functional form to model the AIF and k-means clustering to classify tissue time-concentration measurements into a set of characteristic curves. An iterative blind estimation algorithm alternately estimated parameters for the input function and the pharmacokinetic model. Computer simulations were used to investigate the algorithm's sensitivity to noise and initial estimates. In 12 patients with sarcomas, pharmacokinetic parameter estimates were compared with "truth" obtained from model regression using a measured AIF. When arterial voxels were included in the blind estimation algorithm, the resulting AIF was similar to the measured input function. The "true" K(trans) values in tumor regions were not significantly different than the estimated values, 0.99 +/- 0.41 and 0.86 +/- 0.40 min(-1), respectively, P = 0.27. "True" k(ep) values also matched closely, 0.70 +/- 0.24 and 0.65 +/- 0.25 min(-1), P = 0.08. When only tissue curves free of significant vascular contribution are used (v(p) < 0.05), the resulting AIF showed substantial delay and dispersion consistent with a more local AIF such as has been observed in dynamic susceptibility contrast imaging in the brain.