For clinical dynamic contrast-enhanced (DCE) MRI studies, it is often not possible to obtain reliable arterial input function (AIF) in each measurement. Thus, it is important to find a representative AIF for pharmacokinetic modeling of DCE-MRI data when individual AIF (Ind-AIF) measurements are not available. A total of 16 patients with osteosarcomas in the lower extremity (knee region) underwent multislice DCE-MRI. Reliable Ind-AIFs were obtained in five patients with a contrast injection rate of 2 cc/s and another five patients with a 1 cc/s injection rate. Average AIF (Avg-AIF) for each injection rate was constructed from the corresponding five Ind-AIFs. For each injection rate there are no statistically significant differences between pharmacokinetic parameters of the five patients derived with Ind-AIFs and Avg-AIF. There are no statistically significant changes in pharmacokinetic parameters of the 16 patients when the two Avg-AIFs were applied in kinetic modeling. The results suggest that it is feasible, as well as practical, to use a limited-population-based Avg-AIF for pharmacokinetic modeling of osteosarcoma DCE-MRI data. Further validation with a larger population and multiple regions is desirable.
dynamic contrast-enhanced MRI; arterial input function; osteosarcoma; Ktrans; pharmakinetic modeling
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 Ktrans, 0% in kep, −11% in vp, and +10% in ve. Corresponding uncertainties (median absolute deviation from best fit line) were 0.0043 min−1 in Ktrans, 0.0491 min−1 in kep, 0.29% in vp, and 0.45% in ve. Use of a published population-averaged AIF resulted in larger mean biases in three of the four parameters (−23% in Ktrans, −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 Ktrans, 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 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.
Magnetic resonance imaging; dynamic contrast-enhanced; DCE-MRI; arterial input function
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 suboptimal, 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 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 boostrapping 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 seconds 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.
Magnetic resonance imaging; dynamic contrast-enhanced; DCE-MRI; arterial input function
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, AIFind, and compute a population averaged AIF, AIFpop. 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 Ktrans, vp, and ve as estimated by AIFind and AIFpop are 0.65, 0.74, and 0.31, respectively, based on region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84, and 0.68 for Ktrans, vp, and ve, respectively. This work indicates that Ktrans and vp show a good agreement between AIFpop and AIFind while there is a weak agreement on ve.
dynamic contrast enhanced MRI; arterial input function; pharmacokinetic modeling; breast cancer
Rationale and objective
To test whether individually measured Arterial Input Function (AIF) provides more accurate prostate cancer diagnosis then population average AIF when DCE MRI data are acquired with limited temporal resolution.
Material and methods
26 patients with a high clinical suspicion for prostate caner and no prior treatment underwent Dynamic Contrast Enhanced (DCE) MRI examination at 3.0 T prior to biopsy. DCE MRI data were fitted to a pharmacokinetic model using three forms of AIF: an individually measured, a local population average, and a literature double exponential population average. Receiver Operating Characteristic (ROC) analysis was used to correlate MRI with the biopsy results. Goodness of fit (χ2) for the three AIFs was compared using non-parametric Mann-Whitney test.
Average Ktrans values were significantly higher in tumour than in normal peripheral zone for all three AIFs. The individually measured and the local population average AIFs had the highest sensitivity (76%), while the double exponential AIF had the highest specificity (82%). The areas under the ROC curves were not significantly different between any of the AIFs (0.81, 0.76, and 0.81 for the individually measured, local population average and double exponential AIFs respectively). χ2 was not significantly different for the 3 AIFs, however, it was significantly higher in enhancing than in non-enhancing regions for all 3 AIFs.
These results suggest that, when DCE MRI data are acquired with limited temporal resolution, experimentally measured individual AIF is not significantly better than population average AIF in predicting the biopsy results in prostate cancer.
PMID: 20074982 CAMSID: cams1175
dynamic contrast-enhanced MRI; prostate cancer; arterial input function; biopsy
Bone metastases of 16 prostate cancer patients were scanned twice one week apart by DCE-MRI at 2 seconds resolution using a 2D gradient-echo pulse sequence. With a multiple reference tissue method (MRTM), the local tissue Arterial Input Function (AIF) was estimated using the contrast agent enhancement data from tumor sub-regions and muscle. The 32 individual AIFs estimated by the MRTM, which had considerable intra-patient and inter-patient variability, were similar to directly measured AIFs in the literature and using the MRTM AIFs in a pharmacokinetic model to derive estimated individual cardiac outputs provided physiologically reasonable results. The MRTM individual AIFs gave better fits with smaller sum of squared errors and equally reproducible estimate of kinetic parameters compared to a previous reported population AIF measured from remote arteries. The individual MRTM AIFs were also used to obtain a mean local tissue AIF for the unique population of this study which further improved the reproducibility of the estimated kinetic parameters. The MRTM can be applied to DCE-MRI studies of bone metastases from prostate cancers to provide an AIF from which reproducible quantitative DCE-MRI parameters can be derived, thus help standardize DCE-MRI studies in cancer patients.
dynamic contrast enhanced (DCE)-MRI; arterial input function; reference tissue; tracer kinetic modeling; reproducibility
The hypothesis that the arterial input function (AIF) of gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) injected by intravenous (iv) bolus and measured by the change in the T1-relaxation rate (ΔR1; R1=1/T1) of superior sagittal sinus blood (AIF-I) approximates the AIF of 14C-labeled Gd-DTPA measured in arterial blood (AIF*) was tested in a rat stroke model (n=13). Contrary to the hypothesis, the initial part of the ΔR1-time curve was underestimated, and the area under the normalized curve for AIF-I was about 15% lower than that for AIF*, the reference AIF. Hypothetical AIF’s for Gd-DTPA (AIF-II) were derived from the AIF* values and averaged to obtain AIF-III. Influx rate constants (Ki) and proton distribution volumes at zero time (Vp+Vo) were estimated with Patlak plots of AIF-I, -II and -III and tissue ΔR1 data. For the regions of interest, the Ki’s estimated with AIF-I were slightly but not significantly higher than those obtained with AIF-II and AIF-III. In contrast, Vp+Vo was significantly higher when calculated with AIF-I. Similar estimates of Ki and Vp+Vo were obtained with AIF-II and AIF-III. In summary, AIF-I underestimated the reference AIF (AIF*); this shortcoming had little effect on the Ki calculated by Patlak plot but produced a significant overestimation of Vp+Vo.
blood-brain barrier; cerebral ischemia; magnetic resonance contrast agents; reference tissue model
Dynamic Contrast Enhancement (DCE) MRI has been used to measure the kinetic transport constant, Ktrans, which is used to assess tumor angiogenesis and the effects of anti-angiogenic therapies. Standard DCE MRI methods must measure the pharmacokinetics of a contrast agent in the blood stream, known as the Arterial Input Function (AIF), which is then used as a reference for the pharmacokinetics of the agent in tumor tissue. However, the AIF is difficult to measure in pre-clinical tumor models and in patients. Moreover the AIF is dependent on the Fahraeus effect that causes a highly variable hematocrit (Hct) in tumor microvasculature, leading to erroneous estimates of Ktrans. To overcome these problems, we have developed the Reference Agent Model (RAM) for DCE MRI analyses, which determines the ratio of Ktrans values of two contrast agents that are simultaneously co-injected and detected during a single DCE-MRI scan session. The RAM obviates the need to monitor the AIF because one contrast agent effectively serves as an internal reference in the tumor tissue for the other agent, and it also eliminates the systematic errors in the estimated Ktrans caused by assuming an erroneous Hct. Simulations demonstrated that the RAM can accurately and precisely estimate the relative Ktrans (RKtrans) of two agents. To experimentally evaluate the utility of RAM for analyzing DCE MRI results, we optimized a previously reported multiecho 19F MRI method to detect two perfluorocarbon contrast agents that were co-injected during a single in vivo study and selectively detected in the same tumor location. The results demonstrated that RAM determined RKtrans with excellent accuracy and precision.
Dynamic Contrast Enhanced MRI; Permeability; Reference Agent Model; Pharmacokinetics; 19F MRI; Perfluorocarbons; Linear Models
A method to simultaneously estimate the arterial input function (AIF) and pharmacokinetic model parameters from dynamic contrast-enhanced 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” Ktrans values in tumor regions were not significantly different than the estimated values, .99±.41 and .86±.40 min−1 respectively, p=0.27. “True” kep values also matched closely, .70±.24 and .65±.25 min−1, p=0.08. When only tissue curves free of significant vascular contribution are used (vp<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.
Pharmacokinetic modeling; Tofts-Kety model; Blind Estimation; DCE-MRI; Perfusion Imaging; arterial input function
Kinetic quantitation of dynamic positron emission tomography (PET) studies via compartmental modeling usually requires the time-course of the radio-tracer concentration in the arterial blood as an arterial input function (AIF). For human and animal imaging applications, significant practical difficulties are associated with direct arterial sampling and as a result there is substantial interest in alternative methods that require no blood sampling at the time of the study. A fixed population template input function derived from prior experience with directly sampled arterial curves is one possibility. Image-based extraction, including requisite adjustment for spillover and recovery, is another approach. The present work considers a hybrid statistical approach based on a penalty formulation in which the information derived from a priori studies is combined in a Bayesian manner with information contained in the sampled image data in order to obtain an input function estimate. The absolute scaling of the input is achieved by an empirical calibration equation involving the injected dose together with the subject’s weight, height and gender. The technique is illustrated in the context of 18F-Flu-orodeoxyglucose (FDG) PET studies in humans. A collection of 79 arterially sampled FDG blood curves are used as a basis for a priori characterization of input function variability, including scaling characteristics. Data from a series of 12 dynamic cerebral FDG PET studies in normal subjects are used to evaluate the performance of the penalty-based AIF estimation technique. The focus of evaluations is on quantitation of FDG kinetics over a set of 10 regional brain structures. As well as the new method, a fixed population template AIF and a direct AIF estimate based on segmentation are also considered. Kinetics analyses resulting from these three AIFs are compared with those resulting from radially sampled AIFs. The proposed penalty-based AIF extraction method is found to achieve significant improvements over the fixed template and the segmentation methods. As well as achieving acceptable kinetic parameter accuracy, the quality of fit of the region of interest (ROI) time-course data based on the extracted AIF, matches results based on arterially sampled AIFs. In comparison, significant deviation in the estimation of FDG flux and degradation in ROI data fit are found with the template and segmentation methods. The proposed AIF extraction method is recommended for practical use.
Blood curve representation; image segmentation; kinetics; mixture modeling; no blood sampling; penalty method
To assess the quality of the arterial input function (AIF) reconstructed using a dedicated pre-bolus low-dose contrast material injection imaged with a high temporal resolution and the resulting estimated liver perfusion parameters.
Materials and Methods
In this IRB–approved prospective study, 24 DCE-MRI examinations were performed in 21 patients with liver disease (M/F 17/4, mean age 56 y). The examination consisted of 1.3 mL and 0.05 mmol/kg of gadobenate dimeglumine for pre-bolus and main bolus acquisitions, respectively. The concentration-curve of the abdominal aorta in the pre-bolus acquisition was used to reconstruct the AIF. AIF quality and shape parameters obtained with pre-bolus and main bolus acquisitions and the resulting estimated hepatic perfusion parameters obtained with a dual-input single compartment model were compared between the 2 methods. Test–retest reproducibility of perfusion parameters were assessed in three patients.
The quality of the pre-bolus AIF curve was significantly better than that of main bolus AIF. Shape parameters peak concentration, area under the time activity curve of gadolinium contrast at 60 s and upslope of pre-bolus AIF were all significantly higher, while full width at half maximum was significantly lower than shape parameters of main bolus AIF. Improved liver perfusion parameter reproducibility was observed using pre-bolus acquisition [coefficient of variation (CV) of 4.2%–38.7% for pre-bolus vs. 12.1–71.4% for main bolus] with the exception of distribution volume (CV of 23.6% for pre-bolus vs. 15.8% for main bolus). The CVs between pre-bolus and main bolus for the perfusion parameters were lower than 14%.
The AIF reconstructed with pre-bolus low dose contrast injection displays better quality and shape parameters and enables improved liver perfusion parameter reproducibility, although the resulting liver perfusion parameters demonstrated no clinically significant differences between pre-bolus and main bolus acquisitions.
To evaluate the roles of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and optimum tracer kinetic parameters in the noninvasive grading of the glial brain tumors with histopathological grades (I–IV).
Materials and Methods
Twenty eight patients with histopathologically graded gliomas were imaged. Images with five flip angles were acquired before injection of gadolinium-DTPA and were processed to calculate the T1 value of each regions of interest (ROI). All the DCE-MRI data acquired during the injection were processed based on the MRI signal and pharmacokinetic models to establish concentration-time curves in the ROIs drawn within the tumors, counterlateral normal areas, and area of the individual artery input functions (iAIF) of each patient. A nonlinear least square fitting method was used to obtain tracer kinetic parameters. Kruskal-Wallis H-test and Mann-Whitney U-test were applied to these parameters in different histopathological grade groups for statistical differences (P<0.05).
Volume transfer coefficient (Ktrans) and extravascular extracellular space volume fraction (Ve) calculated by using iAIFs can be used not only to distinguish the low (i.e., I and II) from the high (i.e., III and IV) grade gliomas (P(Ktrans) <0.001 and PVe<0.001), but also grade II from III (P(Ktrans) =0.016 and PVe=0.033).
Ktrans is the most sensitive and specific parameter in the noninvasive grading, distinguishing the high (III and IV) from the low (I and II) grade and high grade III from low grade II gliomas.
MR perfusion imaging; gliomas; microvascular permeability; pharmacokinetics; grades of glioma
Dynamic Contrast Enhanced MRI (DCE-MRI) is today one of the most popular methods for tumor assessment. Several pharmacokinetic models have been proposed to analyze DCE-MRI. Most of them depend on an accurate arterial input function (AIF). We propose an automatic and versatile method to determine the AIF. The method has two stages, detection and segmentation, incorporating knowledge about artery structure, fluid kinetics, and the dynamic temporal property of DCE-MRI. We have applied our method in DCE-MRIs of four different body parts: breast, brain, liver and prostate. The results show that we achieve average 89.5% success rate for 40 cases. The pharmacokinetic parameters computed from the automatic AIF are highly agreeable with those from a manually derived AIF (R2=0.89, P(T<=t)=0.19) and a semiautomatic AIF (R2=0.98, P(T<=t)=0.01).
DCE-MRI; AIF; tumor imaging
To develop a post-processing method to correct saturation of arterial input function (AIF) in T1-weighted 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.
The proposed method corrects the distorted AIF with a saturation ratio up to 0.45. The corrected AIF improves 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 (R2=0.67) compared with the saturated AIFs (R2=0.39).
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.
DCE MRI; AIF saturation; correction; liver; dual-input single compartment model
Dynamic contrast-enhanced-MRI (DCE-MRI) can provide information regarding tumor perfusion and permeability and has shown prognostic value in certain tumors types. The goal of the present study was to assess the prognostic value of pretreatment DCE-MRI in head and neck squamous cell carcinoma (HNSCC) patients with nodal disease undergoing chemoradiation therapy or surgery.
Methods and Materials
Seventy-four patients with histologically proven squamous cell carcinoma and neck nodal metastases were eligible for the study. Pretreatment DCE-MRI was performed on a 1.5T MRI. Clinical follow-up was a minimum of 12 months. DCE-MRI data were analyzed using Tofts model. DCE-MRI parameters were related to treatment outcome (progression free survival [PFS] and overall survival [OS]). Patients were grouped as no evidence of disease (NED), alive with disease (AWD), dead with disease (DOD) or dead of other causes (DOC). Prognostic significance was assessed using the log rank test for single variables and Cox proportional hazards regression for combinations of variables.
At last clinical follow-up, for stage III, all 12 pts were NED, for stage IV, 43 patients were NED, 4 were AWD, 11 were DOD, and 4 were DOC. Ktrans is volume transfer constant. In a stepwise Cox regression skewness of Ktrans was the strongest predictor for stage IV patients (PFS and OS: p<0.001).
Our study shows that skewness of Ktrans was the strongest predictor of PFS and OS in stage IV HNSCC patients with nodal disease. This study suggests an important role for pretreatment DCE-MRI parameter Ktrans as a predictor of outcome in these patients.
Dynamic Contrast Enhanced-MRI (DCE-MRI); head and neck squamous cell carcinoma (HNSCC); volume transfer constant (Ktrans)
A widespread application of integrin αvβ3 imaging has been emerging in both pre-clinical and clinical studies. But few studies reported its value as compared with 18F-FDG PET, especially for differentiated thyroid cancer (DTC). In this study, we compared the tracer uptake of 18F-AIF-NOTA-PRGD2 and 18F-FDG in lymph node metastasis of DTC to evaluate 18F-AIF-NOTA-PRGD2 as compared with 18F-FDG.
20 DTC patients with presumptive lymph node metastasis were examined with 18F-AIF-NOTA-PRGD2 and 18F-FDG PET/CT. 16 patients undergoing fine needle aspiration biopsy (FNAB) were evaluated by cytology results. For lesions without FNAB, the findings of clinical staging procedures served as the standard of reference (including neck ultrasound and serum thyroglobulin).
A total of 39 presumptive lymph node metastases were visualized on PET/CT images. 35 lesions were confirmed as malignant by FNAB and other clinical findings. The mean 18F-AIF-NOTA-PRGD2 in radioactive iodine-refractory (RAIR) lesions and benign lesions were 2.5±0.9 and 2.8±0.9 respectively. The mean SUV for 18F-FDG in all malignant lesions was 4.5±1.6 while in benign lesions it was 3.3±1.2. For all malignant lesions, the mean SUV for 18F-FDG was significantly higher than that for 18F-AIF-NOTA-PRGD2 (P<0.05). No significant correlation was found between the SUVs of 18F-AIF-NOTA-PRGD2 and 18F-FDG for 35 lesions (r = 0.114, P = 0.515). Moreover, 15 lesions of which the diameter larger than 1.5cm had higher 18F-AIF-NOTA-PRGD2 uptake as compared with the lesions smaller than 1.5cm.
Although most lymph node metastases of DTC showed abnormal uptake of 18F-AIF-NOTA-PRGD2, its diagnostic value was inferior to 18F-FDG. No correlation was found between the uptake of 18F-AIF-NOTA-PRGD2 and 18F-FDG, which may suggest the two tracers provide complementary information in DTC lesions.
The aim of the present study is to correlate non-invasive, pretreatment biological imaging (dynamic contrast enhanced-MRI [DCE-MRI] and proton magnetic resonance spectroscopy [1H-MRS]) findings with specific molecular marker data in neck nodal metastases of head and neck squamous cell carcinoma (HNSCC) patients.
Materials and Methods
Pretreatment DCE-MRI and 1H-MRS were performed on neck nodal metastases of 12 patients who underwent surgery. Surgical specimens were analyzed with immunohistochemistry (IHC) assays for: Ki-67 (reflecting cellular proliferation), vascular endothelial growth factor (VEGF) (the “endogenous marker” of tumor vessel growth), carbonic anhydrase (CAIX), hypoxia inducible transcription factor (HIF-1α), and human papillomavirus (HPV). Additionally, necrosis was estimated based on H&E staining. The Spearman correlation was used to compare DCE-MRI, 1H-MRS, and molecular marker data.
A significant correlation was observed between DCE-MRI parameter std(kep) and VEGF IHC expression level (rho = 0.81, p = 0.0001). Furthermore, IHC expression levels of Ki-67 inversely correlated with std(Ktrans) and std(ve) (rho = −0.71; p = 0.004, and rho = −0.73; p = 0.003, respectively). Other DCE-MRI, 1H-MRS and IHC values did not show significant correlation.
The results of this preliminary study indicate that the level of heterogeneity of perfusion in metastatic HNSCC seems positively correlated with angiogenesis, and inversely correlated with proliferation. These results are preliminary in nature and are indicative, and not definitive, trends portrayed in HNSCC patients with nodal disease. Future studies with larger patient populations need to be carried out to validate and clarify our preliminary findings.
Head and neck squamous cell carcinoma; 1H-MRS; DCE-MRI; molecular markers
An MR image-based computational model of a murine KHT sarcoma is presented that allows the calculation of plasma fluid and solute transport within tissue. Such image-based models of solid tumors may be used to optimize patient-specific therapies. This model incorporates heterogeneous vasculature and tissue porosity to account for non-uniform perfusion of an MR-visible tracer, Gd-DTPA. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was conducted following intravenous infusion of Gd-DTPA to provide 1 h of tracer-concentration distribution data within tissue. Early time points (19 min) were used to construct 3D Ktrans and porosity maps using a two-compartment model; tracer transport was predicted at later time points using a 3D porous media model. Model development involved selecting an arterial input function (AIF) and conducting a sensitivity analysis of model parameters (tissue, vascular, and initial estimation of solute concentration in plasma) to investigate the effects on transport for a specific tumor. The developed model was then used to predict transport in two additional tumors. The sensitivity analysis suggests that plasma fluid transport is more sensitive to parameter changes than solute transport due to the dominance of transvascular exchange. Gd-DTPA distribution was similar to experimental patterns, but differences in Gd-DTPA magnitude at later time points may result from inaccurate selection of AIF. Thus, accurate AIF estimation is important for later time point prediction of low molecular weight tracer or drug transport in smaller tumors.
DCE-MRI; drug transport model; extracellular transport; arterial input function; two-compartment model; contrast agent extravasation
Accurate quantification of myocardial perfusion remains challenging due to saturation of the arterial input function at high contrast concentrations. A method for estimating the arterial input function directly from tissue curves in the myocardium that avoids these difficulties is presented. In this constrained alternating minimization with model (CAMM) algorithm, a portion of the left ventricular blood pool signal is also used to constrain the estimation process. Extensive computer simulations assessing the accuracy of kinetic parameter estimation were performed. In 5000 noise realizations, the use of the AIF given by the estimation method returned kinetic parameters with mean Ktrans error of –2% and mean kep error of 0.4%. Twenty in vivo resting perfusion datasets were also processed with this method, and pharmacokinetic parameter values derived from the blind AIF were compared with those derived from a dual-bolus measured AIF. For 17 of the 20 datasets, there were no statistically significant differences in Ktrans estimates, and in aggregate the kinetic parameters were not significantly different from the dual-bolus method. The cardiac constrained alternating minimization with model method presented here provides a promising approach to quantifying perfusion of myocardial tissue with a single injection of contrast agent and without a special pulse sequence though further work is needed to validate the approach in a clinical setting.
pharmacokinetics; myocardial perfusion imaging; contrast-enhanced MRI
AIM: To evaluate the sources of variation influencing the microvascularization parameters measured by dynamic contrast-enhanced ultrasonography (DCE-US).
METHODS: Firstly, we evaluated, in vitro, the impact of the manual repositioning of the ultrasound probe and the variations in flow rates. Experiments were conducted using a custom-made phantom setup simulating a tumor and its associated arterial input. Secondly, we evaluated, in vivo, the impact of multiple contrast agent injections and of examination day, as well as the influence of the size of region of interest (ROI) associated with the arterial input function (AIF). Experiments were conducted on xenografted B16F10 female nude mice. For all of the experiments, an ultrasound scanner along with a linear transducer was used to perform pulse inversion imaging based on linear raw data throughout the experiments. Semi-quantitative and quantitative analyses were performed using two signal-processing methods.
RESULTS: In vitro, no microvascularization parameters, whether semi-quantitative or quantitative, were significantly correlated (P values from 0.059 to 0.860) with the repositioning of the probe. In addition, all semi-quantitative microvascularization parameters were correlated with the flow variation while only one quantitative parameter, the tumor blood flow, exhibited P value lower than 0.05 (P = 0.004). In vivo, multiple contrast agent injections had no significant impact (P values from 0.060 to 0.885) on microvascularization parameters. In addition, it was demonstrated that semi-quantitative microvascularization parameters were correlated with the tumor growth while among the quantitative parameters, only the tissue blood flow exhibited P value lower than 0.05 (P = 0.015). Based on these results, it was demonstrated that the ROI size of the AIF had significant influence on microvascularization parameters: in the context of larger arterial ROI (from 1.17 ± 0.6 mm3 to 3.65 ± 0.3 mm3), tumor blood flow and tumor blood volume were correlated with the tumor growth, exhibiting P values lower than 0.001.
CONCLUSION: AIF selection is an essential aspect of the deconvolution process to validate the quantitative DCE-US method.
Dynamic contrast-enhanced ultrasonography; Angiogenesis; Linear raw data; Arterial input function; Functional imaging
To validate a new method for converting MR arterial signal intensity versus time curves to arterial input functions (AIF).
Materials and Methods:
The method constrains AIF with patient's cardiac output (Q). Monte Carlo simulations of MR renography and tumor perfusion protocols were carried out for comparison with two alternative methods: direct measurement and population-averaged input function. MR renography was performed to assess the method's inter- and intra-day reproducibility for renal parameters.
In simulations of tumor perfusion, the precision of the parameters (Ktrans and ve) computed using the proposed method was improved by at least a factor of three compared to direct measurement. Similar improvements were obtained in simulations of MR renography. Volunteer study for testing inter-day reproducibility confirmed the improvement of precision in renal parameters when using the proposed method, compared to conventional methods. In another patient study (two injections within one session), the proposed method significantly increased the correlation coefficient (R) between GFR of the two exams (0.92 vs. 0.83), compared to direct measurement.
A new method significantly improves the precision of DCE parameters. The method may be especially useful for analyzing repeated DCE examinations, such as monitoring tumor therapy or ACE-inhibitor renography.
cardiac output; dynamic contrast-enhanced MRI; arterial input function
The feasibility of Shutter-Speed Model (SSM) (Dynamic-Contrast-Enhanced) DCE-MRI pharmacokinetic analyses for prostate cancer detection was investigated in a pre-biopsy patient cohort. Differences of results from the fast exchange regime-allowed (FXR-a) SSM version and the fast-exchange-limit-constrained (FXL-c) standard model (SM) are demonstrated. Though the spatial information is more limited, post-DCE-MRI biopsy specimens were also examined. The MRI results were correlated with the biopsy pathology findings. Of all the model parameters, ROI-averaged Ktrans difference [ΔKtrans ≡ Ktrans(FXR-a) − Ktrans(FXL-c)] or 2D Ktrans(FXR-a) vs. kep(FXR-a) values were found to provide the most useful biomarkers for malignant/benign prostate tissue discrimination [at 100% sensitivity for a population of 13, the specificity is 88%] and disease burden determination. [The best specificity for the FXL-c analysis is 67%, with the 2D plot.] Ktrans and kep are each measures of passive trans-capillary contrast reagent transfer rate constants. Parameter value increases with shutter-speed model (relative to standard model) analysis are larger in malignant foci than in normal appearing glandular tissue. Pathology analyses verify the SSM(FXR-a) promise for prostate cancer detection. Parametric mapping may further improve pharmacokinetic biomarker performance.
Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as Ktrans (rate constant for plasma/interstitium contrast agent transfer), ve (extravascular extracellular volume fraction), and vp (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for Ktrans and vp being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the Ktrans intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for Ktrans) to 0.92 (for Ktrans percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor Ktrans and kep (=Ktrans/ve, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.
To quantitatively evaluate the kinetic parameter estimation for head and neck (HN) dynamic contrast-enhanced (DCE) MRI with dual-flip-angle (DFA) T1 mapping.
Materials and methods
Clinical DCE-MRI datasets of 23 patients with HN tumors were included in this study. T1 maps were generated based on multiple-flip-angle (MFA) method and different DFA combinations. Tofts model parameter maps of kep, Ktrans and vp based on MFA and DFAs were calculated and compared. Fitted parameter by MFA and DFAs were quantitatively evaluated in primary tumor, salivary gland and muscle.
T1 mapping deviations by DFAs produced remarkable kinetic parameter estimation deviations in head and neck tissues. In particular, the DFA of [2º, 7º] overestimated, while [7º, 12º] and [7º, 15º] underestimated Ktrans and vp, significantly (P<0.01). [2º, 15º] achieved the smallest but still statistically significant overestimation for Ktrans and vp in primary tumors, 32.1% and 16.2% respectively. kep fitting results by DFAs were relatively close to the MFA reference compared to Ktrans and vp.
T1 deviations induced by DFA could result in significant errors in kinetic parameter estimation, particularly Ktrans and vp, through Tofts model fitting. MFA method should be more reliable and robust for accurate quantitative pharmacokinetic analysis in head and neck.
DCE-MRI; head and neck; Tofts model; T1 mapping; dual-flip-angle method
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.
Arterial Input Function; bolus-tracking MRI; cerebral blood flow; deconvolution; perfusion; stroke