Patient-specific Arterial Input Function Research Articles

Intraoperative fluorescence perfusion assessment should be corrected by a measured subject-specific arterial input function.

.Significance: The effects of varying the indocyanine green injection dose, injection rate, physiologic dispersion of dye, and intravenous tubing volume propagate into the shape and magnitude of the arterial input function (AIF) during intraoperative fluorescence perfusion assessment, thereby altering the observed kinetics of the fluorescence images in vivo.Aim: Numerical simulations are used to demonstrate the effect of AIF on metrics derived from tissue concentration curves such as peak fluorescence, time-to-peak (TTP), and egress slope.Approach: Forward models of tissue concentration were produced by convolving simulated AIFs with the adiabatic approximation to the tissue homogeneity model using input parameters representing six different tissue examples (normal brain, glioma, normal skin, ischemic skin, normal bone, and osteonecrosis).Results: The results show that AIF perturbations result in variations in estimates of total intensity of up to 80% and TTP error of up to 200%, with the errors more dominant in brain, less in skin, and less in bone. Interestingly, error in ingress slope was as high as 60% across all tissue types. These are key observable parameters used in fluorescence imaging either implicitly by viewing the image or explicitly through intensity fitting algorithms. Correcting by deconvolving the image with a measured subject-specific AIF provides an intuitive means of visualizing the data while also removing the source of variance and allowing intra- and intersubject comparisons.Conclusions: These results suggest that intraoperative fluorescence perfusion assessment should be corrected by patient-specific AIFs measured by pulse dye densitometry.

Pharmacokinetic modeling of dynamic contrast-enhanced MRI using a reference region and input function tail.

Quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5-30 s) and noise levels (σ = 0.01-0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient-specific AIF tails, and another assuming a literature-based AIF tail. RRIFT estimated the transfer constant and interstitial volume with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of had a median and interquartile range of -9 ± 45% with the ETM and -2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for , 0.96 for , and 0.73 for the plasma volume using a measured AIF tail. With the literature-based AIF tail, the CCC was 0.89 for , 0.93 for and 0.78 for . Quantitative DCE-MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.

Comparison of transport of chemotherapeutic drugs in voxelized heterogeneous model of human brain tumor

Systemic administration of chemotherapeutic drugs is widely used in the treatment of cancer. However, a good understanding of drug transport barriers that influence the treatment efficacy is still lacking. In this study, a voxelized numerical model based on dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) and computational fluid dynamics (CFD) is employed to study the transport and efficacy of three different chemotherapeutic drugs, namely methotrexate, doxorubicin and cisplatin in human brain tumors. DCE-MRI data provides realistic heterogeneous vasculature of the tumor, the permeability of tissue to contrast agent, interstitial volume fraction (porosity) of the tissue and patient-specific arterial input function (AIF). The permeability of tissue to aforementioned drugs is determined by correlating it with the permeability of tissue to the contrast agent. The model is employed to simulate drug concentration in the tissue and compare the effect of heterogeneous vasculature on the distribution of the drugs in the tumor. The drug accumulation is observed to be higher in high permeability areas initially, and in higher porosity areas at later times. Furthermore, it is observed that methotrexate remains in the interstitial space of the tumor in higher concentration for a longer duration as compared to other two drugs, facilitating more tumor cell killing.

DCE-MRI based voxelized computational model for chemotherapeutic drug transport in human brain tumor

Understanding the distribution of chemotherapeutic drugs in tumor when administered systemically is important for assessing the efficiency of anticancer treatment. Despite numerous researches in this field, prediction of drug distribution within the tumors still remains a challenge due to wide heterogeneity of tumor vasculature. In this work, a voxelized computational fluid dynamics (CFD) model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been employed to simulate the transport of chemotherapeutic drug (doxorubicin) in human brain tumors. The DCE-MRI data provide realistic heterogeneous vasculature of a tumor and help in determining the permeability of the tissue vascular wall to contrast agent and interstitial volume fraction (porosity) of the tissue. The permeability of the tissue to doxorubicin is calculated by correlating it with the permeability of the tissue to contrast agent. Patient-specific arterial input function (AIF) estimation has also been done to make the model patient-specific. The computational model provides steady-state interstitial fluid pressure and velocity values, which are further used to predict transient drug concentration in tumor tissue. Simulation results show that doxorubicin accumulates more in high permeability areas initially. However, drug accumulation increases in areas of higher interstitial volume fraction as time progresses. This observation implies that higher accumulation of chemotherapeutic drugs occurs in higher porosity areas, facilitating larger tumor cell killing in those areas.

Variability induced by the MR imager in dynamic contrast-enhanced imaging of the prostate

To evaluate the variability induced by the imager in discriminating high-grade (Gleason≥7) prostate cancers (HGC) using dynamic contrast-enhanced MRI. We retrospectively selected 3T MRIs with temporal resolution<10seconds and comprising T1 mapping from a prospective radiologic-pathologic database of patients treated by prostatectomy. Ktrans, Kep, Ve and Vp were calculated for each lesion seen on MRI using the Weinmann arterial input function (AIF) and three patient-specific AIFs measured in the right and left iliac arteries in pixels in the center of the lumen (psAIF-ST) or manually selected by two independent readers (psAIF-R1 and psAIF-R2). A total of 43 patients (mean age, 63.6±4.9 [SD]; range: 48-72 years) with 100 lesions on MRI (55 HGC) were selected. MRIs were performed on imager A (22 patients, 49 lesions) or B (21 patients, 51 lesions) from two different manufacturers. Using the Weinmann AIF, Kep (P=0.005), Ve (P=0.04) and Vp (P=0.01) significantly discriminated HCG. After adjusting on tissue classes, the imager significantly influenced the values of Kep (P=0.049) and Ve (P=0.007). Using patient-specific AIFs, Vp with psAIF-ST (P=0.008) and psAIF-R2 (P=0.04), and Kep with psAIF-R1 (P=0.03) significantly discriminated HGC. After adjusting on tissue classes, types of patient-specific AIF and side of measurement, the imager significantly influenced the values of Ktrans (P=0.0002), Ve (P=0.0072) and Vp (P=0.0003). For all AIFs, the diagnostic value of pharmacokinetic parameters remained unchanged after adjustment on the imager, with stable odds ratios. The imager induced variability in the absolute values of pharmacokinetic parameters but did not change their diagnostic performance.

Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast-enhanced MRI using a model consistency constraint.

To develop and evaluate a model-based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast-enhanced MRI (DCE-MRI) data. The proposed method poses the tracer-kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down-sampled brain tumor DCE-MRI datasets. We also demonstrate application to 30-fold prospectively undersampled brain tumor DCE-MRI. In DRO studies with up to 60-fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third-party TK solver. In retrospective undersampling studies, this method provided patient-specific AIF with normalized root mean-squared-error (normalized by the 90th percentile value) less than 8% at up to 100-fold undersampling. In the 30-fold undersampled prospective study, the proposed method provided high-resolution whole-brain TK maps and patient-specific AIF. The proposed model-based DCE-MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient-specific AIF. TK maps and patient-specific AIF with high fidelity can be reconstructed at up to 100-fold undersampling in k,t-space. Magn Reson Med 79:2804-2815, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Improving the arterial input function in dynamic contrast enhanced MRI by fitting the signal in the complex plane

Dynamic contrast enhanced (DCE) imaging is a widely used technique in oncologic imaging. An essential prerequisite for obtaining quantitative values from DCE-MRI is the determination of the arterial input function (AIF). However, it is very challenging to accurately estimate the AIF using MR. A comprehensive model, which uses complex data instead of either magnitude or phase, was developed to improve AIF estimation. The model was first applied to simulated data. Subsequently, the accuracy of the estimated contrast agent concentration was validated in a phantom. Finally the method was applied to existing DCE scans of 13 prostate cancer patients. The complex signal method combines the complementary strengths of the magnitude and phase method, increasing the precision and accuracy of concentration estimation in simulated and phantom data. The in vivo AIFs show a good agreement between arterial voxels (standard deviation in the peak and tail equal 0.4 mM and 0.12 mM, respectively). Furthermore, the dynamic behavior closely followed the AIF obtained with DCE-CT in the same patients (mean correlation coefficient: 0.92). By using the complex signal, the AIF estimation becomes more accurate and precise. This might enable patient specific AIFs, thereby improving the quantitative values obtained from DCE-MRI. Magn Reson Med 76:1236-1245, 2016. © 2015 Wiley Periodicals, Inc.

SU‐D‐18C‐04: The Feasibility of Quantifying MRI Contrast Agent in Pulsatile Flowing Blood Using DCE‐MRI

Purpose:To assess the feasibility of accurately quantifying the concentration of MRI contrast agent (CA) in pulsatile flowing blood by measuring its T1, as is common for the purposes of obtaining a patientspecific arterial input function (AIF). Dynamic contrast enhanced (DCE) – MRI and pharmacokinetic (PK) modelling is widely used to produce measures of vascular function but accurate measurement of the AIF undermines their accuracy. A proposed solution is to measure the T1 of blood in a large vessel using the Fram double flip angle method during the passage of a bolus of CA. This work expands on previous work by assessing pulsatile flow and the changes in T1 seen with a CA bolus.Methods:A phantom was developed which used a physiological pump to pass fluid of a known T1 (812ms) through the centre of a head coil of a clinical 1.5T MRI scanner. Measurements were made using high temporal resolution sequences suitable for DCE‐MRI and were used to validate a virtual phantom that simulated the expected errors due to pulsatile flow and bolus of CA concentration changes typically found in patients.Results:: Measured and virtual results showed similar trends, although there were differences that may be attributed to the virtual phantom not accurately simulating the spin history of the fluid before entering the imaging volume. The relationship between T1 measurement and flow speed was non‐linear. T1 measurement is compromised by new spins flowing into the imaging volume, not being subject to enough excitations to have reached steady‐state. The virtual phantom demonstrated a range of recorded T1 for various simulated T1 / flow rates.Conclusion:T1 measurement of flowing blood using standard DCE‐MRI sequences is very challenging. Measurement error is non‐linear with relation to instantaneous flow speed. Optimising sequence parameters and lowering baseline T1 of blood should be considered.

A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. Simulations

Widespread adoption of quantitative pharmacokinetic modeling methods in conjunction with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has led to increased recognition of the importance of obtaining accurate patient-specific arterial input function (AIF) measurements. Ideally, DCE-MRI studies use an AIF directly measured in an artery local to the tissue of interest, along with measured tissue concentration curves, to quantitatively determine pharmacokinetic parameters. However, the numerous technical and practical difficulties associated with AIF measurement have made the use of population-averaged AIF data a popular, if sub-optimal, alternative to AIF measurement. In this work, we present and characterize a new algorithm for determining the AIF solely from the measured tissue concentration curves. This Monte Carlo blind estimation (MCBE) algorithm estimates the AIF from the subsets of D concentration–time curves drawn from a larger pool of M candidate curves via nonlinear optimization, doing so for multiple (Q) subsets and statistically averaging these repeated estimates. The MCBE algorithm can be viewed as a generalization of previously published methods that employ clustering of concentration–time curves and only estimate the AIF once. Extensive computer simulations were performed over physiologically and experimentally realistic ranges of imaging and tissue parameters, and the impact of choosing different values of D and Q was investigated. We found the algorithm to be robust, computationally efficient and capable of accurately estimating the AIF even for relatively high noise levels, long sampling intervals and low diversity of tissue curves. With the incorporation of bootstrapping initialization, we further demonstrated the ability to blindly estimate AIFs that deviate substantially in shape from the population-averaged initial guess. Pharmacokinetic parameter estimates for Ktrans, kep, vp and ve all showed relative biases and uncertainties of less than 10% for measurements having a temporal sampling rate of 4 s and a concentration measurement noise level of σ = 0.04 mM. A companion paper discusses the application of the MCBE algorithm to DCE-MRI data acquired in eight patients with malignant brain tumors.

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 kep, −11% in vp and +10% in ve. Corresponding uncertainties (median absolute deviation from the best fit line) were 0.0043 min−1 in K trans, 0.0491 min−1 in kep, 0.29% in vp and 0.45% in ve. 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 kep, −63% in vp), with the bias in ve unchanged, and led to larger uncertainties in all four parameters (0.0083 min−1 in K trans, 0.1038 min−1 in kep, 0.31% in vp and 0.95% in ve). 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.

Scan‐rescan variability in perfusion assessment of tumors in MRI using both model and data‐derived arterial input functions

To evaluate the contribution to scan-rescan coefficient of variation (CV) of patient-specific arterial input function (AIF) measurement in dynamic contrast-enhanced MRI (DCE-MRI) data, and to determine whether any advantage or disadvantage to using a data-derived arterial input function is related to the anatomical location of the target lesion. Two methods are presented for the calculation of perfusion parameters from DCE-MRI data using a two-compartment model. The first method makes use of a single-model AIF across all study data sets, while the second uses an automated process to derive an AIF specific to each data set. Both methods are applied to the analysis of a 25-subject scan-rescan study of patients with advanced solid tumors located in either the lungs or the liver. The parameters of interest in this study are the volume transfer constant between arterial plasma and extracellular extravascular space (Ktrans) and the blood-normalized initial area under the tumor enhancement curve over the first 90 seconds postinjection (IAUCBN90). The use of a data-derived AIF reduces the visit-to-visit CV in both parameters for liver lesions by approximately 70% while the improvement is less than 20% for lung lesions. The use of a data-derived AIF in the analysis of DCE-MRI data provides a substantial reduction in scan-rescan CV in the measurement of vascular parameters such as Ktrans and IAUCBN90. These results show a much larger advantage in the liver than in the lungs. However, this difference is largely driven by a small number of outliers, and may be spurious.