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
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
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
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
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
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
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
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)
Cervical tumors of 38 cervix cancer patients were scanned by T1-weighted dynamic contrast enhanced (DCE) MRI and then by DCE-CT on the same day. Gadodiamide and iohexol were respectively used as the low-molecular-weight contrast agent in DCE-MRI and DCE-CT. Under an extended Tofts model, DCE-MRI data were analyzed using either individual arterial input functions estimated by a multiple reference tissue method or a population arterial input function by Parker et al., whereas DCE-CT data were analyzed using the arterial input function directly measured from the external iliac arteries. The derived quantitative parameters of cervical tumors were compared between DCE-MRI and DCE-CT. When using the individual multiple reference tissue method arterial input functions to analyze the DCE-MRI data, the correlation coefficients between DCE-MRI- and DCE-CT-derived parameters were, respectively, back-flux rate constant (r = 0.80), extravascular extracellular fractional volume (r = 0.73), contrast agent transfer rate (r = 0.62), and blood plasma volume (r = 0.32); when using the Parker population arterial input function, the correlation coefficients were back-flux rate constant (r = 0.79), extravascular extracellular fractional volume (r = 0.77), contrast agent transfer rate (r = 0.63), and blood plasma volume (r = 0.58). Tumor parametric maps derived by DCE-MRI and DCE-CT had very similar morphologies. However, the means of most derived quantitative parameters were significantly different between the two imaging methods. Close correlation of quantitative parameters derived from two independent imaging modalities suggests both are measuring similar tumor physiologic variables.
dynamic contrast enhanced MRI; dynamic contrast enhanced CT; quantitative analysis; correlation; tracer kinetic modeling; T1 MRI
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
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
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
Pretreatment multimodality imaging can provide useful anatomical and functional data about tumors, including perfusion and possibly hypoxia status. The purpose of our study was to assess non-invasively the tumor microenvironment of neck nodal metastases in patients with head and neck (HN) cancer by investigating the relationship between tumor perfusion measured using Dynamic Contrast Enhanced MRI (DCE-MRI) and hypoxia measured by 18F-fluoromisonidazole (18F-FMISO) PET.
Methods and Materials
Thirteen newly diagnosed HN cancer patients with metastatic neck nodes underwent DCE-MRI and 18F-FMISO PET imaging prior to chemotherapy and radiation therapy. The matched regions of interests from both modalities were analyzed. To examine the correlations between DCE-MRI parameters and standard uptake value (SUV) measurements from 18F-FMISO PET, the non-parametric Spearman correlation coefficient was calculated. Furthermore, DCE-MRI parameters were compared between nodes with 18F-FMISO uptake and nodes with no 18F-FMISO uptake using Mann-Whitney U tests.
For the 13 patients, a total of 18 nodes were analyzed. The nodal size strongly correlated with the 18F-FMISO SUV (ρ=0.74, p<0.001). There was a strong negative correlation between the median kep (ρ=−0.58, p=0.042) and the 18F-FMISO SUV. Hypoxic nodes (moderate to severe 18F-FMISO uptake) had significantly lower median Ktrans (p=0.049) and median kep (p=0.027) values than did non-hypoxic nodes (no 18F-FMISO uptake).
This initial evaluation of the preliminary results support the hypothesis that in metastatic neck lymph nodes, hypoxic nodes are poorly perfused (i.e., have significantly lower kep and Ktrans values) compared to non-hypoxic nodes.
Dynamic Contrast Enhanced-MRI (DCE-MRI); 18F-fluoromisonidazole (FMISO) PET; 18F-fluorodeoxyglucose (FDG); head and neck (HN) cancer
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
Dynamic contrast enhanced (DCE) cardiovascular magnetic resonance (CMR) is increasingly used to quantify microvessels and permeability in atherosclerosis. Accurate quantification depends on reliable sampling of both vessel wall (VW) uptake and contrast agent dynamic in the blood plasma (the so called arterial input function, AIF). This poses specific challenges in terms of spatial/temporal resolution and matched dynamic MR signal range, which are suboptimal in current vascular DCE-CMR protocols. In this study we describe a novel dual-imaging approach, which allows acquiring simultaneously AIF and VW images using different spatial/temporal resolution and optimizes imaging parameters for the two compartments. We refer to this new acquisition as SHILO, Simultaneous HI-/LOw-temporal (low-/hi-spatial) resolution DCE-imaging.
In SHILO, the acquisition of low spatial resolution single-shot AIF images is interleaved with segments of higher spatial resolution images of the VW. This allows sampling the AIF and VW with different spatial/temporal resolution and acquisition parameters, at independent spatial locations. We show the adequacy of this temporal sampling scheme by using numerical simulations. Following, we validate the MR signal of SHILO against a standard 2D spoiled gradient recalled echo (SPGR) acquisition with in vitro and in vivo experiments. Finally, we show feasibility of using SHILO imaging in subjects with carotid atherosclerosis.
Our simulations confirmed the superiority of the SHILO temporal sampling scheme over conventional strategies that sample AIF and tissue curves at the same time resolution. Both the median relative errors and standard deviation of absolute parameter values were lower for the SHILO than for conventional sampling schemes. We showed equivalency of the SHILO signal and conventional 2D SPGR imaging, using both in vitro phantom experiments (R2 =0.99) and in vivo acquisitions (R2 =0.95). Finally, we showed feasibility of using the newly developed SHILO sequence to acquire DCE-CMR data in subjects with carotid atherosclerosis to calculate plaque perfusion indices.
We successfully demonstrate the feasibility of using the newly developed SHILO dual-imaging technique for simultaneous AIF and VW imaging in DCE-CMR of atherosclerosis. Our initial results are promising and warrant further investigation of this technique in wider studies measuring kinetic parameters of plaque neovascularization with validation against gold standard techniques.
Atherosclerosis; Neovascularization; Perfusion/permeability
To correlate proton magnetic resonance spectroscopy (1H-MRS), dynamic contrast-enhanced MRI (DCE-MRI) and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in nodal metastases of patients with head and neck squamous cell carcinoma (HNSCC) for assessment of tumor biology. Additionally, pretreatment multimodality imaging (MMI) was evaluated for its efficacy in predicting short-term response to treatment.
Methods and Materials
Metastatic neck nodes were imaged with 1H-MRS, DCE-MRI and 18F-FDG PET in 16 patients with newly diagnosed HNSCC before treatment. Short-term radiological response was evaluated at 3–4 months. The correlations between 1H-MRS (choline concentration, Cho/W), DCE-MRI (volume transfer constant, Ktrans; volume fraction of the extravascular extracellular space, ve; and redistribution rate constant, kep) and 18F-FDG PET (standard uptake value, SUV; and total lesion glycolysis, TLG) were calculated using non-parametric Spearman rank correlation. To predict the short-term response, logistic regression analysis was performed.
A significant positive correlation was found between Cho/W and TLG (ρ = 0.599, p = 0.031). Cho/W correlated negatively with heterogeneity measures std(ve) (ρ = −0.691, p = 0.004) and std(kep) (ρ = −0.704, p = 0.003). SUVmax values correlated strongly with MRI tumor volume (ρ = 0.643, p = 0.007). Logistic regression indicated that std(Ktrans) and SUVmean were significant predictors of short-term response (p < 0.07).
Pretreatment multi-modality imaging using 1H-MRS, DCE-MRI and 18F-FDG PET is feasible in HNSCC patients with nodal metastases. Additionally, combined DCE-MRI and 18F-FDG PET parameters were predictive of short-term response to treatment.
Head and neck squamous cell carcinoma; 1H-MRS; DCE-MRI; 18F-FDG PET; short-term treatment response
Head and neck Magnetic Resonance (MR) Images are vulnerable to the arterial blood in-flow effect. To compensate for this effect and enhance accuracy and reproducibility, dynamic tracer concentration in veins was proposed and investigated for quantitative dynamic contrast-enhanced (DCE) MRI analysis in head and neck.
21 patients with head and neck tumors underwent DCE-MRI at 3T. An automated method was developed for blood vessel selection and separation. Dynamic concentration-time-curves (CTCs) in arteries and veins were used for the Tofts model parameter estimations. The estimation differences by using CTCs in arteries and veins were compared. Artery and vein voxels were accurately separated by the automated method. Remarkable inter-slice tracer concentration differences were found in arteries while the inter-slice concentration differences in veins were moderate. Tofts model fitting by using the CTCs in arteries and veins produced significantly different parameter estimations. The individual artery CTCs resulted in large (>50% generally) inter-slice parameter estimation variations. Better inter-slice consistency was achieved by using the vein CTCs.
The use of vein CTCs helps to compensate for arterial in-flow effect and reduce kinetic parameter estimation error and inconsistency for head and neck DCE-MRI.
The objective of this study was to assess changes in the water apparent diffusion coefficient (ADC) and in pharmacokinetic parameters obtained from the fast-exchange regime (FXR) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during neoadjuvant chemotherapy in breast cancer.
Materials and Methods
Eleven patients with locally advanced breast cancer underwent MRI examination prior to and after chemotherapy but prior to surgery. A 1.5-T scanner was used to obtain T1, ADC and DCE-MRI data. DCE-MRI data were analyzed by the FXR model returning estimates of Ktrans (volume transfer constant), νe (extravascular extracellular volume fraction) and τsi (average intracellular water lifetime). Histogram and correlation analyses assessed parameter changes post-treatment.
Significant ( P <.05) changes or trends towards significance ( P <.10) were seen in all parameters except τi, although there was qualitative reduction in τi values post-treatment. In particular, there was reduction ( P <.035) in voxels with Ktrans values in the range 0.2–0.5 min-1 and a decrease ( P <.05) in voxels with ADC values in the range 0.99×10-3 to 1.35×10-3 mm2/s. ADC and νe were negatively correlated (r = -.60, P <.02). Parameters sensitive to water distribution and geometry (T1, νe,τsi and ADC) correlated with a multivariable linear regression model.
The analysis presented here is sensitive to longitudinal changes in breast tumor status; Ktrans and ADC are most sensitive to these changes. Relationships between parameters provide information on water distribution and geometry in the tumor environment.
DCE-MRI; ADC; Neoadjuvant chemotherapy; Fast-exchange regime
In this paper, we propose a new pharmacokinetic model for parameter estimation of dynamic contrast-enhanced (DCE) MRI by using Gaussian process inference. Our model is based on the Tofts dual-compartment model for the description of tracer kinetics and the observed time series from DCE-MRI is treated as a Gaussian stochastic process. The parameter estimation is done through a maximum likelihood approach and we propose a variant of the coordinate descent method to solve this likelihood maximization problem. The new model was shown to outperform a baseline method on simulated data. Parametric maps generated on prostate DCE data with the new model also provided better enhancement of tumors, lower intensity on false positives, and better boundary delineation when compared with the baseline method. New statistical parameter maps from the process model were also found to be informative, particularly when paired with the PK parameter maps.
DCE-MRI; Gaussian Stochastic Process; Pharmacokinetic Model; Bayesian Inference; Coordinate Descent Optimization
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
Many researchers have established the utility of the dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in the differential diagnosis in the head and neck region, especially in the salivary gland tumors. The subjective assessment of the pattern of the time-intensity curve (TIC) or the simple quantification of the TIC, such as the time to peak enhancement (Tpeak) and the wash-out ratio (WR), is commonly used. Although the semiquantitative evaluations described above have been widely applied, they do not provide information on the underlying pharmacokinetic analysis in tissue.
The quantification of DCE-MRI is preferable; therefore, many compartment model analyses have been proposed. The Toft and Kermode (TK) model is one of the most popular compartment models, which provide information about the influx forward volume transfer constant from plasma into the extravascular-extracellular space (EES) and the fractional volume of EES per unit volume of tissue is used in many clinical studies. This paper will introduce the method of pharmacokinetic analysis and also describe the clinical application of this technique in the head and neck region.
With advances in MRI technology, Dynamic-Contrast-Enhanced (DCE) MRI is approaching the capability to simultaneously deliver both high spatial- and temporal-resolutions for clinical applications. However, Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) considerations, and their impacts regarding pharmacokinetic modeling of the time-course data continue to represent challenges in the design of DCE-MRI acquisitions. Given that many acquisition parameters can affect the nature of DCE-MRI data, minimizing tissue-specific data acquisition discrepancy (among sites and scanner models) is as important as synchronizing pharmacokinetic modeling approaches.
For cancer related DCE-MRI studies where rapid contrast reagent (CR) extravasation is expected, current DCE-MRI protocols often adopt a 3D fast low-angle shot (FLASH) sequence to achieve spatial-temporal resolution requirements. Based on breast and prostate DCE-MRI data acquired with different FLASH sequence parameters, this paper elucidates a number of SNR and CNR considerations for acquisition optimization and pharmacokinetic modeling implications therein. Simulations based on ROI data further indicate that the effects of intercompartmental water exchange often play an important role in DCE time-course data modeling, especially for protocols optimized for post-CR SNR.
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
To assess the temporal sampling requirements needed for quantitative analysis of dynamic contrast enhanced MRI (DCE-MRI) data with a reference region (RR) model in human breast cancer.
Materials and Methods
Simulations were used to study errors in pharmacokinetic parameters (Ktrans and ve) estimated by the RR model using six DCE-MRI acquisitions over a range of pharmacokinetic parameter values, arterial input functions, and temporal samplings. DCE-MRI data were acquired on 12 breast cancer patients and parameters were estimated using the native resolution data (16.4 second) and compared to downsampled 32.8 second and 65.6 second data.
Simulations show that, in the majority of parameter combinations, the RR model results in an error less than 20% in the extracted parameters with temporal sampling as poor as 35.6 seconds. The experimental results show a high correlation between Ktrans and ve estimates from data acquired at 16.4 second temporal resolution compared to the downsampled 32.8 second data: the slope of the regression line was 1.025 (95% CI: 1.021, 1.029), Pearson's correlation r = 0.943 (CI: 0.940, 0.945) for Ktrans, and 1.023 (CI: 1.021. 1.025), r = 0.979 (CI: 0.978, 0.980) for ve. For the 64 second temporal resolution data the results were: 0.890 (CI: 0.894, 0.905), r = 0.8645, (CI: 0.858, 0.871) for Ktrans, and 1.041 (CI:1.039, 1.043), r = 0.970 (CI:0.968, 0.971) for ve.
RR analysis allows for a significant reduction in temporal sampling requirements and this lends itself to analyze DCE-MRI data acquired in practical situations.
DCE-MRI; breast cancer; temporal sampling; pharmacokinetics
Increased microvascularization of the abdominal aortic aneurysm (AAA) vessel wall has been related to AAA progression and rupture. The aim of this study was to compare the suitability of three pharmacokinetic models to describe AAA vessel wall enhancement using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
Materials and Methods
Patients with AAA underwent DCE-MRI at 1.5 Tesla. The volume transfer constant (Ktrans), which reflects microvascular flow, permeability and surface area, was calculated by fitting the blood and aneurysm vessel wall gadolinium concentration curves. The relative fit errors, parameter uncertainties and parameter reproducibilities for the Patlak, Tofts and Extended Tofts model were compared to find the most suitable model. Scan-rescan reproducibility was assessed using the interclass correlation coefficient and coefficient of variation (CV). Further, the relationship between Ktrans and AAA size was investigated.
DCE-MRI examinations from thirty-nine patients (mean age±SD: 72±6 years; M/F: 35/4) with an mean AAA maximal diameter of 49±6 mm could be included for pharmacokinetic analysis. Relative fit uncertainties for Ktrans based on the Patlak model (17%) were significantly lower compared to the Tofts (37%) and Extended Tofts model (42%) (p<0.001). Ktrans scan-rescan reproducibility for the Patlak model (ICC = 0.61 and CV = 22%) was comparable with the Tofts (ICC = 0.61, CV = 23%) and Extended Tofts model (ICC = 0.76, CV = 22%). Ktrans was positively correlated with maximal AAA diameter (Spearman’s ρ = 0.38, p = 0.02) using the Patlak model.
Using the presented imaging protocol, the Patlak model is most suited to describe DCE-MRI data of the AAA vessel wall with good Ktrans scan-rescan reproducibility.