Investigation and optimization of parameter accuracy in dynamic contrast-enhanced MRI

Conversion of arterial input functions for dual pharmacokinetic modeling using Gd-DTPA/MRI and 18F-FDG/PET

Reaching the full potential of magnetic resonance imaging (MRI)-positron emission tomography (PET) dual modality systems requires new methodologies in quantitative image analyses. In this study, methods are proposed to convert an arterial input function (AIF) derived from gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) in MRI, into a (18)F-fluorodeoxyglucose ((18)F-FDG) AIF in PET, and vice versa. The AIFs from both modalities were obtained from manual blood sampling in a F98-Fisher glioblastoma rat model. They were well fitted by a convolution of a rectangular function with a biexponential clearance function. The parameters of the biexponential AIF model were found statistically different between MRI and PET. Pharmacokinetic MRI parameters such as the volume transfer constant (K(trans)), the extravascular-extracellular volume fraction (ν(e)), and the blood volume fraction (ν(p)) calculated with the Gd-DTPA AIF and the Gd-DTPA AIF converted from (18)F-FDG AIF normalized with or without blood sample were not statistically different. Similarly, the tumor metabolic rates of glucose (TMRGlc) calculated with (18) F-FDG AIF and with (18) F-FDG AIF obtained from Gd-DTPA AIF were also found not statistically different. In conclusion, only one accurate AIF would be needed for dual MRI-PET pharmacokinetic modeling in small animal models.

Correction of arterial input function in dynamic contrast-enhanced MRI of the liver

PURPOSE

To develop a postprocessing method to correct saturation of arterial input function (AIF) in T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantification of hepatic perfusion.

MATERIALS AND METHODS

The saturated AIF is corrected by parameterizing the first pass of the AIF as a smooth function with a single peak and minimizing a least-squares error in fitting the liver DCE-MRI data to a dual-input single-compartment model. Sensitivities of the method to the degree of saturation in the AIF first-pass peak and the image contrast-to-noise ratio were assessed. The method was also evaluated by correlating portal venous perfusion with an independent overall liver function measurement.

RESULTS

The proposed method corrects the distorted AIF with a saturation ratio up to 0.45. The corrected AIF improved hepatic arterial perfusion by -23.4% and portal venous perfusion by 26.9% in a study of 12 patients with liver cancers. The correlation between the mean voxelwise portal venous perfusion and overall liver function measurement was improved by using the corrected AIFs (R(2) = 0.67) compared with the saturated AIFs (R(2) = 0.39).

CONCLUSION

The method is robust for correcting AIF distortion and has the potential to improve quantification of hepatic perfusion for assessment of liver tissue response to treatment in patients with hepatic cancers.

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.

A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population-based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIF(ind), and compute a population-averaged AIF, AIF(pop). The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for K(trans), v(p) and v(e) as estimated by AIF(ind) and AIF(pop) are 0.65, 0.74 and 0.31, respectively, based on the region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84 and 0.68 for K(trans), v(p) and v(e), respectively. This work indicates that K(trans) and v(p) show good agreement between AIF(pop) and AIF(ind) while there is a weak agreement on v(e).

Assessment of the effect of haematocrit-dependent arterial input functions on the accuracy of pharmacokinetic parameters in dynamic contrast-enhanced MRI

The detection and prognosis of prostate cancer in its early stages are critically important. It is therefore essential to improve the existing dynamic contrast-enhanced MRI (DCE MRI) techniques commonly used for the assessment of the tumour vascular environment. The goal of this study was to describe a method for the estimation of the arterial input function (AIF) in DCE MRI by measuring R(2) * values in the femoral artery of patients with early-stage prostate cancer. The calculation of contrast agent concentrations was based on calibration curves determined in whole blood samples for a range of normal haematocrit (HCT) values (HCT = 0.35-0.525). Individual AIFs corrected for HCT were compared with individual AIFs calibrated with a mean whole blood [R(2)*-Gd-DTPA-BMA] [Gd-DTPA-BMA, gadolinium diethylenetriaminepentaacetate-bis(methylamide) (gadodiamide)] curve at an assumed HCT = 0.44, as well as a population AIF at an assumed HCT = 0.45. The area under the curve of the first-pass bolus ranged between 0.6 min mM at HCT = 0.53 and 1.3 min mM at HCT = 0.39. Significant differences in magnitude at peak contrast agent concentrations (HCT = 0.36, [Gd-DTPA-BMA](max) = 9 ± 0.4 mM; HCT = 0.46, [Gd-DTPA-BMA](max) = 4.0 ± 0.2 mM) were found. Using model-based simulations, the accuracy of the kinetic parameters estimated using individual AIFs corrected for HCT demonstrated that, for the use of individual calibration curves with HCT values differing by more than 10%, K(trans) and k(ep) values were largely underestimated (up to 60% difference for K(trans)). Moreover, blood volume estimates were severely underestimated. Estimates of kinetic parameters in early-stage prostate cancer patients demonstrated that the efflux rate constant (k(ep)) was influenced significantly by the definition of AIF. Regardless of whether an individually calibrated AIF or a population AIF (average of all individually calibrated AIFs) was used, pixel-by-pixel mapping of k(ep) and v(b) in the prostate gland appeared to be more sensitive than with the usual biexponential approach.

The promise of dynamic contrast-enhanced imaging in radiation therapy

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and computed tomography (CT) scanning are emerging as valuable tools to quantitatively map the spatial distribution of vascular parameters, such as perfusion, vascular permeability, blood volume, and mean transit time in tumors and normal organs. DCE MRI/CT have shown prognostic and predictive value for response of certain cancers to chemotherapy and radiation therapy. DCE MRI/CT offer the promise of early assessment of tumor response to radiation therapy, opening a window for adaptively optimizing radiation therapy based upon functional alterations that occur earlier than morphologic changes. DCE MRI/CT has also shown the potential of mapping dose responses in normal organs and tissue for evaluation of individual sensitivity to radiation, providing additional opportunities to minimize risks of radiation injury. The evidence for potentially applying DCE MRI and CT for selection and delineation of radiation boost targets is growing. The clinical use of DCE MRI and CT scanning as a biomarker or even a surrogate endpoint for radiation therapy assessment of tumor and normal organs must consider technical validation issues, including standardization, reproducibility, accuracy and robustness, and clinical validation of the sensitivity and specificity for each specific problem of interest. Although holding great promise, to date, DCE MRI and CT scanning have not been qualified as a surrogate endpoint for radiation therapy assessment or for treatment modification in any prospective phase III clinical trial for any tumor site.

First order correction for T2*-relaxation in determining contrast agent concentration from spoiled gradient echo pulse sequence signal intensity

PURPOSE

To investigate the accuracy of a method neglecting T(2)*-relaxation, for the conversion of spoiled gradient echo pulse sequence signal intensity to contrast agent (CA) concentration, in dynamic contrast enhanced MRI studies. In addition a new closed form conversion expression is proposed that accounts for a first order approximation of T(2)*-relaxation.

MATERIALS AND METHODS

The accuracy of both conversion methods is compared theoretically by means of simulations for four pulse sequences from literature. Both methods are tested in vivo against the numerical conversion method for measuring the arterial input function in mice.

RESULTS

Simulations show that the T(2)*-neglecting method underestimates typical tissue CA concentrations (0 mM to 2 mM) up to 6%, while the errors for arterial concentrations (0 mM to 10 mM) range up to 43%. The results from our first order method are numerically indistinguishable from the simulation input values in tumor tissue, while for arterial concentrations the error is reduced up to a factor 10. In vivo, peak Gd-DOTA concentration is underestimated up to 14% with the T(2)*-neglecting method and up to 0.9% with our first order method.

CONCLUSION

Our conversion method reduces the underestimation of CA concentration severely in a broad physiological concentration range and is easy to perform in any clinical setting.

The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer

Current standard-of-care radiological methods for assessing the response of solid tumors to treatment are based on measuring changes in lesion size in a single dimension using high-resolution x-ray computed tomography (CT) or magnetic resonance imaging (MRI). Even if size measurements are adapted to record true volume changes more accurately, the effects of therapeutic drugs on tumor size may not occur for several cycles of treatment. Furthermore, current and future generations of anticancer drugs will be designed to affect highly specific cancer characteristics, and their effects may not be immediately cytotoxic. More sensitive and specific measures are required that can report on tumor status and treatment response early in the course of therapy. Several MRI techniques have matured to the point where they can offer quantitative information on tissue status and greater insight into specific biophysical and physiological characteristics of tumors. Here we review and provide illustrative examples of two MRI methods that have already been incorporated into clinical trials of treatment response in solid tumors: diffusion imaging and dynamic contrast-enhanced MRI. We also discuss the limitations and future research directions required for these techniques to gain greater acceptance and to have their maximum impact.

Arterial input functions determined from MR signal magnitude and phase for quantitative dynamic contrast-enhanced MRI in the human pelvis

Dynamic contrast-enhanced (DCE) MRI is often used to measure the transfer constant (Ktrans) and distribution volume (ve) in pelvic tumors. For optimal accuracy and reproducibility, one must quantify the arterial input function (AIF). Unfortunately, this is challenging due to inflow and signal saturation. A potential solution is to use MR signal phase (ϕ), which is relatively unaffected by these factors. We hypothesized that phase-derived AIFs (AIFϕ) would provide more reproducible Ktrans and ve values than magnitude-derived AIFs (AIF|S|). We tested this in 27 prostate dynamic contrast-enhanced MRI studies (echo time=2.56 ms, temporal resolution=13.5 s), using muscle as a standard. AIFϕ peak amplitude varied much less as a function of measurement location (inferior-superior) than AIF|S| (5.6±0.6 mM vs. 2.6±1.5 mM), likely as a result of ϕ inflow insensitivity. However, our main hypothesis was not confirmed. The best AIF|S| provided similar reproducibility versus AIFϕ (interpatient muscle Ktrans=0.039±0.021 min(-1) vs. 0.037±0.025 min(-1), ve=0.090±0.041 vs. 0.062±0.022, respectively).

Measuring the integrity of the human blood-brain barrier using magnetic resonance imaging

The evaluation of blood-brain barrier (BBB) integrity with contrast-enhanced magnetic resonance imaging (MRI) may prove valuable in the setting of certain brain pathologies, such as brain tumors and acute ischemic stroke. Various MRI protocols have been developed to explore the integrity of the BBB by monitoring the leakage of intravenously administered contrast medium into the brain parenchyma. In its simplest form, BBB integrity is assessed qualitatively, by determining the presence or absence of contrast-enhancement on a structural MR image. When a dynamic contrast-enhanced (DCE) MRI protocol is combined with a suitable pharmacokinetic model, DCE-MRI can map the spatial distribution of BBB integrity throughout the brain and assist with evaluating the effects of therapy. Several model-free surrogate measures of BBB permeability have been recently proposed, all of which can be readily computed from standard dynamic susceptibility contrast MRI perfusion scans. Contrast-enhanced MRI offers multiple strategies for evaluating BBB integrity.

Current and future trends in magnetic resonance imaging assessments of the response of breast tumors to neoadjuvant chemotherapy

The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions.

The MRI-measured arterial input function resulting from a bolus injection of Gd-DTPA in a rat model of stroke slightly underestimates that of Gd-[14C]DTPA and marginally overestimates the blood-to-brain influx rate constant determined by Patlak plots

The hypothesis that the arterial input function (AIF) of gadolinium-diethylenetriaminepentaacetic acid injected by intravenous bolus and measured by the change in the T(1)-relaxation rate (Delta R(1); R(1) = 1/T(1)) of superior sagittal sinus blood (AIF-I) approximates the AIF of (14)C-labeled gadolinium-diethylenetriaminepentaacetic acid measured in arterial blood (reference AIF) was tested in a rat stroke model (n = 13). Contrary to the hypothesis, the initial part of the Delta R(1)-time curve was underestimated, and the area under the normalized curve for AIF-I was about 15% lower than that for the reference AIF. Hypothetical AIFs for gadolinium-diethylenetriaminepentaacetic acid were derived from the reference AIF values and averaged to obtain a cohort-averaged AIF. Influx rate constants (K(i)) and proton distribution volumes at zero time (V(p) + V(o)) were estimated with Patlak plots of AIF-I, hypothetical AIFs, and cohort-averaged AIFs and tissue Delta R(1) data. For the regions of interest, the K(i)s estimated with AIF-I were slightly but not significantly higher than those obtained with hypothetical AIFs and cohort-averaged AIF. In contrast, V(p) + V(o) was significantly higher when calculated with AIF-I. Similar estimates of K(i) and V(p) + V(o) were obtained with hypothetical AIFs and cohort-averaged AIF. In summary, AIF-I underestimated the reference AIF; this shortcoming had little effect on the K(i) calculated by Patlak plot but produced a significant overestimation of V(p) + V(o).

Temporal resolution and SNR requirements for accurate DCE-MRI data analysis using the AATH model

Dynamic contrast-enhanced MRI has been used in conjunction with tracer kinetics modeling in a wide range of tissues for treatment monitoring, oncology drug development, and investigation of disease processes. Accurate measurement of model parameters relies on acquiring data with high temporal resolution and low noise, particularly for models with large numbers of free parameters, such as the adiabatic approximation to the tissue homogeneity model for separate measurements of blood flow and vessel permeability. In this simulation study, accuracy of the adiabatic approximation to the tissue homogeneity model was investigated, examining the effects of temporal resolution, noise levels, and error in the measured arterial input function. A temporal resolution of 1.5 s and high SNR (noise sd = 0.05) were found to ensure minimal bias (<5%) in all four model parameters (extraction fraction, blood flow, mean transit time, and extravascular extracellular volume), and the sampling interval can be relaxed to 6 s, if the transit time need not be measured accurately (bias becomes >10%). A 10% error in the measured height of the arterial input function first pass peak resulted in an error of at most 10% in each model parameter.

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 K(trans), k(ep), v(p) and v(e) 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 sigma = 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.

Dynamic contrast-enhanced CT for prostate cancer: relationship between image noise, voxel size, and repeatability

PURPOSE

To evaluate the relationship between image noise, voxel size, and voxel-wise repeatability of a dynamic contrast agent-enhanced (DCE) computed tomographic (CT) examination for prostate cancer.

MATERIALS AND METHODS

This prospective study was approved by the local research ethics committee, and all patients gave written informed consent. Twenty-nine patients (mean age, 69.1 years; range, 56-80 years) with biopsy-proved prostate cancer underwent two DCE CT examinations within 1 week prior to radiation therapy. Parameter maps of transfer constant (K(trans)), the fraction of blood plasma (v(p)), the fraction of extravascular extracellular space (v(e)), and the flux rate constant between the extravascular extracellular space and plasma (k(ep)) were calculated at 15 different image resolutions, with kernel sizes ranging from 0.002 to 2.57 cm(3). Statistical analysis to quantify the voxel-wise repeatability was performed by using a Bland-Altman analysis on all tracer kinetic model parameter maps of each patient. From this analysis, the within-voxel standard deviation (wSD) was calculated as a function of spatial resolution.

RESULTS

A kernel size in the range of 0.1-0.3 cm(3) yields reliable information. At 0.15 cm(3), the median wSDs of K(trans), k(ep), v(p), and v(e) are 0.047 min(-1), 0.144 min(-1), 0.011, and 0.104, respectively. With increasing kernel size, these values reach stable levels of approximately 0.02 min(-1), 0.05 min(-1), 0.005, and 0.05, respectively.

CONCLUSION

There is a high voxel-wise repeatability of the DCE CT imaging technique for prostate cancer for kernel sizes as small as 0.1 cm(3). With the relationship between kernel size, image noise and voxel-wise repeatability, it becomes possible to estimate for alternative DCE CT protocols (eg, those with a reduced radiation dose) at what kernel size a sufficient repeatability can be obtained.

Relative recirculation: a fast, model-free surrogate for the measurement of blood-brain barrier permeability and the prediction of hemorrhagic transformation in acute ischemic stroke

OBJECTIVES

To retrospectively evaluate the prognostic performance of a dynamic susceptibility contrast (DSC) MRI metric for permeability (relative recirculation or rR) for the prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS). To compare rR with dynamic contrast-enhanced (DCE) MRI estimates of blood-brain barrier permeability (KPS).

MATERIALS AND METHODS

Data obtained from 26 patients (age: 27-89 years) with a working diagnosis of AIS were examined retrospectively. Patients were examined within a mean of 3.5 hours of symptom onset. Eleven patients received intravenous recombinant tissue-plasminogen activator (rt-PA). HT was determined by follow-up computed tomography and/or magnetic resonance imaging 24 to 72 hours after initial imaging. Permeability (DCE) always preceded DSC imaging and consisted of a 3D gradient-recalled echo scan acquired in 4 minutes 48 s. DSC imaging consisted of a T2*-weighted single shot EPI scan acquired in 43 to 86 seconds. Gadodiamide (0.1 mmol/kg) was injected as a bolus for each scan. Permeability (KPS) and rR were calculated offline for regions of interest (ROI) defined within the core of the infarct, as well as within the homologous location in the contralateral hemisphere. The relationship between KPS and rR was investigated using linear regression and receiver operating characteristic (ROC) curves were computed for predicting HT from either rR or KPS.

RESULTS

Eleven patients proceeded to HT (including 5 treated with rt-PA). Mean KPS values were significantly elevated in infarct relative to contralateral areas (0.84 +/- 0.57 vs. 0.42 +/- 0.34 mL/100 g/min; P = 0.0003). For infarct ROIs, KPS values were significantly greater in patients with HT compared with non-HT patients (1.25 +/- 0.63 vs. 0.53 +/- 0.23 mL/100 g/min; P = 0.0015). KPS values were higher in patients who received rt-PA than in untreated patients (1.09 +/- 0.61 vs. 0.65 +/- 0.47 mL/100 g/min; P = 0.0497). ROC analysis indicated a KPS threshold value of 0.67 mL/100 g/min for providing an optimal sensitivity and specificity for predicting HT of 91% and 80%, respectively. Mean rR values for infarct ROIs were significantly higher than those determined for contralateral regions (0.17 +/- 0.06 vs. 0.09 +/- 0.03; P < 0.0001). The mean rR for the HT group was significantly greater than for the non-HT group (0.22 +/- 0.05 vs. 0.14 +/- 0.05; P = 0.0002). As with KPS, the mean rR for patients who were treated with rt-PA was significantly greater than for untreated patients (0.21 +/- 0.07 vs. 0.15 +/- 0.05; P = 0.0112). ROC analysis indicated a threshold value of 0.17 for providing optimal sensitivity and specificity for predicting HT of 91% and 87%, respectively. There was a significant correlation between rR and KPS for infarct ROIs (r = 0.67; P < 0.001).

CONCLUSIONS

Both KPS and rR are significantly elevated in infarcted, relative to uninfarcted tissue in the same AIS patient. Both parameters were also significantly elevated in HT, relative to non-HT infarcts. The strong correlation between rR and KPS, coupled with the high sensitivity and specificity of rR for the prediction of HT suggest that rR is related to blood-brain barrier integrity in AIS and may prove valuable in the prediction of HT.

Sensitivity of quantitative metrics derived from DCE MRI and a pharmacokinetic model to image quality and acquisition parameters

RATIONALE AND OBJECTIVES

This study aims to investigate the sensitivity of quantitative metrics derived from dynamic contrast-enhanced (DCE) magnetic resonance imaging and a pharmacokinetic (PK) model to image quality and acquisition parameters.

MATERIALS AND METHODS

A computer-synthesized DCE model that consisted of a large range of values of K(trans) (transfer constant of a paramagnetic contrast agent from blood to tissue), v(p) (fractional plasma volume), and k(ep) (back flux rate) was created to test the reliability of quantitative metrics derived from a standard PK model. Effects of the contrast-to-noise ratio (CNR), total acquisition time, and sampling interval on the stability and bias of the derived metrics were investigated.

RESULTS

The instability and bias of the estimated K(trans), v(p), and k(ep) values increased with sampling interval and decreased with increasing CNR. Total acquisition times had limited influence on the estimations of K(trans) and v(p) values, but increasing the total acquisition time improved the stability of the estimation of k(ep) values. However, for small k(ep) values, the stability was still poor even with a total acquisition time of 8 minutes. Also, the stability and bias of the estimated values of K(trans), v(p), and k(ep) are interrelated.

CONCLUSIONS

Our synthesized DCE model represents perfectly reproduced data except for the presence of Gaussian-distributed random noise. Our analysis suggests minimum changes that may be considered potentially significant in longitudinal therapy assessment studies. Our data are complementary to experimental data from human subjects and phantoms, and provide guidance for the design of image acquisition strategies.

The influence of temporal resolution in determining pharmacokinetic parameters from DCE-MRI data

We investigated the influence of the temporal resolution of dynamic contrast-enhanced MRI data on pharmacokinetic parameter estimation. Dynamic Gd-DTPA (Gadolinium-diethylene triamine pentaacetic acid) enhanced MRI data of implanted prostate tumors on rat hind limb were acquired at 4.7 T, with a temporal resolution of approximately 5 sec. The data were subsequently downsampled to temporal resolutions in the range of 15 sec to 85 sec, using a strategy that involves a recombination of k-space data. A basic two-compartment model was fit to the contrast agent uptake curves. The results demonstrated that as temporal resolution decreases, the volume transfer constant (K(trans)) is progressively underestimated (approximately 4% to approximately 25%), and the fractional extravascular extracellular space (v(e)) is progressively overestimated (approximately 1% to approximately 10%). The proposed downsampling strategy simulates the influence of temporal resolution more realistically than simply downsampling by removing samples.

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.

Monitoring angiogenesis in soft-tissue engineered constructs for calvarium bone regeneration: an in vivo longitudinal DCE-MRI study

Tissue engineering is a promising technique for bone repair and can overcome the major drawbacks of conventional autogenous bone grafting. In this in vivo longitudinal study, we proposed a new tissue-engineering paradigm: inserting a biological soft-tissue construct within the bone defect to enhance angiogenesis for improved bone regeneration. The construct acts as a resorbable scaffold to support desired angiogenesis and cellular activity and as a vector of vascular endothelial growth factor, known to promote both vessel and bone growth. Dynamic contrast- enhanced magnetic resonance imaging was performed to investigate and characterize angiogenesis necessary for bone formation following the proposed paradigm of inserting a VEGF-impregnated tissue-engineered construct within the critical-sized calvarial defect in the membranous parietal bone of the rabbit. Results show that a model-free quantitative approach, the normalized initial area under the curve metric, provides sensitive and reproducible measures of vascularity that is consistent with known temporal evolution of angiogenesis during bone regeneration.

Characterization of tumor angiogenesis in rat brain using iron-based vessel size index MRI in combination with gadolinium-based dynamic contrast-enhanced MRI

This study aimed at combining an iron-based, steady-state, vessel size index magnetic resonance imaging (VSI MRI) approach, and a gadolinium (Gd)-based, dynamic contrast-enhanced MRI approach (DCE MRI) to characterize tumoral microvasculature. Rats bearing an orthotopic glioma (C6, n=14 and RG2, n=6) underwent DCE MRI and combined VSI and DCE MRI 4 h later, at 2.35 T. Gd-DOTA (200 mumol of Gd per kg) and ultrasmall superparamagnetic iron oxide (USPIO) (200 micromol of iron per kg) were used for DCE and VSI MRI, respectively. C6 and RG2 gliomas were equally permeable to Gd-DOTA but presented different blood volume fractions and VSI, in good agreement with histologic data. The presence of USPIO yielded reduced K(trans) values. The K(trans) values obtained with Gd-DOTA in the absence and in the presence of USPIO were well correlated for the C6 glioma but not for the RG2 glioma. It was also observed that, within the time frame of DCE MRI, USPIO remained intravascular in the C6 glioma whereas it extravasated in the RG2 glioma. In conclusion, VSI and DCE MRI can be combined provided that USPIO does not extravasate with the time frame of the DCE MRI experiment. The mechanisms at the origin of USPIO extravasation remain to be elucidated.

Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI

PURPOSE

The purpose was to compare two approaches for the acquisition and analysis of dynamic-contrast-enhanced MRI data with respect to differences in the modelling of the arterial input-function (AIF), the dependency of the model parameters on physiological parameters and their numerical stability. Eight hundred tissue concentration curves were simulated for different combinations of perfusion, permeability, interstitial volume and plasma volume based on two measured AIFs and analysed according to the two commonly used approaches. The transfer constants (Approach 1) K (trans) and (Approach 2) k (ep) were correlated with all tissue parameters. K (trans) showed a stronger dependency on perfusion, and k (ep) on permeability. The volume parameters (Approach 1) v (e) and (Approach 2) A were mainly influenced by the interstitial and plasma volume. Both approaches allow only rough characterisation of tissue microcirculation and microvasculature. Approach 2 seems to be somewhat more robust than 1, mainly due to the different methods of CA administration.

Fundamentals of quantitative dynamic contrast-enhanced MR imaging

Quantitative analysis of dynamic contrast-enhanced MR imaging (DCE-MR imaging) has the power to provide information regarding physiologic characteristics of the microvasculature and is, therefore, of great potential value to the practice of oncology. In particular, these techniques could have a significant impact on the development of novel anticancer therapies as a promising biomarker of drug activity. Standardization of DCE-MR imaging acquisition and analysis to provide more reproducible measures of tumor vessel physiology is of crucial importance to realize this potential. The purpose of this article is to review the pathophysiologic basis and technical aspects of DCE-MR imaging techniques.

Average arterial input function for quantitative dynamic contrast enhanced magnetic resonance imaging of neck nodal metastases

Background

The present study determines the feasibility of generating an average arterial input function (Avg-AIF) from a limited population of patients with neck nodal metastases to be used for pharmacokinetic modeling of dynamic contrast-enhanced MRI (DCE-MRI) data in clinical trials of larger populations.

Methods

Twenty patients (mean age 50 years [range 27–77 years]) with neck nodal metastases underwent pretreatment DCE-MRI studies with a temporal resolution of 3.75 to 7.5 sec on a 1.5T clinical MRI scanner. Eleven individual AIFs (Ind-AIFs) met the criteria of expected enhancement pattern and were used to generate Avg-AIF. Tofts model was used to calculate pharmacokinetic DCE-MRI parameters. Bland-Altman plots and paired Student t-tests were used to describe significant differences between the pharmacokinetic parameters obtained from individual and average AIFs.

Results

Ind-AIFs obtained from eleven patients were used to calculate the Avg-AIF. No overall significant difference (bias) was observed for the transfer constant (K^trans) measured with Ind-AIFs compared to Avg-AIF (p = 0.20 for region-of-interest (ROI) analysis and p = 0.18 for histogram median analysis). Similarly, no overall significant difference was observed for interstitial fluid space volume fraction (v_e) measured with Ind-AIFs compared to Avg-AIF (p = 0.48 for ROI analysis and p = 0.93 for histogram median analysis). However, the Bland-Altman plot suggests that as K^trans increases, the Ind-AIF estimates tend to become proportionally higher than the Avg-AIF estimates.

Conclusion

We found no statistically significant overall bias in K^trans or v_e estimates derived from Avg-AIF, generated from a limited population, as compared with Ind-AIFs.

However, further study is needed to determine whether calibration is needed across the range of K^trans. The Avg-AIF obtained from a limited population may be used for pharmacokinetic modeling of DCE-MRI data in larger population studies with neck nodal metastases. Further validation of the Avg-AIF approach with a larger population and in multiple regions is desirable.