The effects of renal variation upon measurements of perfusion and leakage volume in breast tumours

Correlations between intravoxel incoherent motion diffusion-weighted MR imaging parameters and 18F-FDG PET/CT metabolic parameters in patients with vertebral bone metastases: initial experience

OBJECTIVE

To investigate the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted MRI (DW MRI) parameters and ¹⁸F-fluodeoxyglucose (FDG) (PET/CT) metabolic parameters in patients with vertebral bone metastases.

METHODS

19 patients with vertebral bone metastases were retrospectively included in this institutional review board-approved study. All patients underwent IVIM DW-MRI and ¹⁸F-FDG PET/CT before treatment. The IVIM parameters [molecular diffusion coefficient (D), perfusion fraction (f), and perfusion-related D (D*)] and apparent diffusion coefficient were acquired using 11 b-values (0, 10, 15, 20, 25, 50, 80, 120, 200, 300, and 800 s mm^-2). Maximum and mean standardized uptake values (SUVmax and SUVmean, respectively), metabolic tumor volume, and total lesion glycolysis derived from ¹⁸F-FDG PET/CT were calculated using thresholds of 3.0 SUV. The associations among parameters were evaluated by Spearman's correlation analysis.

RESULTS

A total of 19 patients and 41 regions of interest were included in this study. The IVIM parameter f was positively correlated with the metabolic parameters SUVmean and SUVmax [ρ = 0.499 (p < 0.01) and ρ = 0.413 (p < 0.01), respectively]. There was a weak positive correlation between D* and SUVmean (ρ = 0.321, p = 0.041).

CONCLUSION

IVIM perfusion-related parameters, especially f, were correlated with ¹⁸F-FDG PET/CT metabolic parameters in patients with vertebral bone metastases. IVIM DW-MRI, used to evaluate metabolic activity, appears to have diagnostic potential for bone metastasis and may also have utility in monitoring the post-treatment response. Advances in knowledge: The use of IVIM for vertebral bone metastasis is demonstrated. f may be more suitable to reflect the metabolic activity and may facilitate another diagnostic potential for monitoring the posttreatment response.

Pharmacokinetic Parameters Analyzed from MR Contrast Enhancement Kinetics of Multiple Malignant and Benign Breast Lesions Detected in the Same Patients

Ninety-nine patients with confirmed breast cancer were reviewed to identify patients who had two confirmed malignant lesions of identical pathology (Group-1, N=17), and patients who had one malignant lesion and the second benign lesion (Group-2, N=8). Contrast enhancement kinetics from every lesion was measured and analyzed using three different models to obtain fitting parameters related to up-slope, enhancement amplitude, and wash-out, including Model-1: modified Tofts model ( vp, Ktrans, kep), Model-2: standard Tofts model ( Ktrans, kep), and Model-3: a 3-parameter heuristic model ( Tc, A, C). By analyzing lesions from same patients, the differences in whole body hemodynamics thus the blood kinetics could be controlled. Two questions were addressed in this study: i) What is the association between pharmacokinetic parameters analyzed from multiple cancers of identical pathology in same patients?; and ii) What is the difference between secondary malignant lesions and secondary benign lesions with reference to the primary cancer? All three models could fit the enhancement kinetics satisfactorily. Regardless of the analysis model the parameter obtained from the primary cancer and the secondary cancer showed significant correlations. In comparison between Group-1 and Group-2 subjects, the wash-out parameter kep in Models-1 and 2 could significantly differentiate benign from malignant lesions, but not the magnitude parameters, Ktrans in Model-2 or the parameter A in Model-3. If analyzed using appropriate models the early up-slope parameters, vp in Model-1 and Tc in Model-3, might be able to distinguish between benign and malignant lesions. When more data are available a reference database can be established with the method described in this study, and from which to determine the likelihood of malignancy for each incidental lesion found in preoperative MRI, with reference to the primary cancer.

The Added Prognostic Value of Preoperative Dynamic Contrast-Enhanced MRI Histogram Analysis in Patients with Glioblastoma: Analysis of Overall and Progression-Free Survival

BACKGROUND AND PURPOSE

The prognostic value of dynamic contrast-enhanced MR imaging in patients with glioblastoma is controversial. We investigated the added prognostic value of dynamic contrast-enhanced MR imaging to clinical parameters and molecular biomarkers in patients with glioblastoma by using histogram analysis.

MATERIALS AND METHODS

This retrospective study consisted of 61 patients who underwent preoperative dynamic contrast-enhanced MR imaging for glioblastoma. The histogram parameters of dynamic contrast-enhanced MR imaging, including volume transfer constant, extravascular extracellular volume fraction, and plasma volume fraction, were calculated from entire enhancing tumors. Univariate analyses for overall survival and progression-free survival were performed with preoperative clinical and dynamic contrast-enhanced MR imaging parameters and postoperative molecular biomarkers. Multivariate Cox regression was performed to build pre- and postoperative models for overall survival and progression-free survival. The performance of models was assessed by calculating the Harrell concordance index.

RESULTS

In univariate analysis, patients with higher volume transfer constant and extravascular extracellular volume fraction values showed worse overall survival and progression-free survival, whereas plasma volume fraction showed no significant correlation. In multivariate analyses for overall survival, the fifth percentile value of volume transfer constant and kurtosis of extravascular extracellular volume fraction were independently prognostic in the preoperative model, and kurtosis of volume transfer constant and extravascular extracellular volume fraction were independently prognostic in the postoperative model. For progression-free survival, independent prognostic factors were minimum and fifth percentile values of volume transfer constant and kurtosis of extravascular extracellular volume fraction in the preoperative model and kurtosis of extravascular extracellular volume fraction in the postoperative model. The performance of preoperative models for progression-free survival was significantly improved when minimum or fifth percentile values of volume transfer constant and kurtosis of extravascular extracellular volume fraction were added.

CONCLUSIONS

Higher volume transfer constant and extravascular extracellular volume fraction values are associated with worse prognosis, and dynamic contrast-enhanced MR imaging may have added prognostic value in combination with preoperative clinical parameters, especially in predicting progression-free survival.

Quantitative pharmacokinetic analysis of prostate cancer DCE-MRI at 3T: comparison of two arterial input functions on cancer detection with digitized whole mount histopathological validation

Accurate pharmacokinetic (PK) modeling of dynamic contrast enhanced MRI (DCE-MRI) in prostate cancer (PCa) requires knowledge of the concentration time course of the contrast agent in the feeding vasculature, the so-called arterial input function (AIF). The purpose of this study was to compare AIF choice in differentiating peripheral zone PCa from non-neoplastic prostatic tissue (NNPT), using PK analysis of high temporal resolution prostate DCE-MRI data and whole-mount pathology (WMP) validation. This prospective study was performed in 30 patients who underwent multiparametric endorectal prostate MRI at 3.0T and WMP validation. PCa foci were annotated on WMP slides and MR images using 3D Slicer. Foci ≥0.5cm(3) were contoured as tumor regions of interest (TROIs) on subtraction DCE (early-arterial - pre-contrast) images. PK analyses of TROI and NNPT data were performed using automatic AIF (aAIF) and model AIF (mAIF) methods. A paired t-test compared mean and 90th percentile (p90) PK parameters obtained with the two AIF approaches. Receiver operating characteristic (ROC) analysis determined diagnostic accuracy (DA) of PK parameters. Logistic regression determined correlation between PK parameters and histopathology. Mean TROI and NNPT PK parameters were higher using aAIF vs. mAIF (p<0.05). There was no significant difference in DA between AIF methods: highest for p90 volume transfer constant (K(trans)) (aAIF differences in the area under the ROC curve (Az) = 0.827; mAIF Az=0.93). Tumor cell density correlated with aAIF K(trans) (p=0.03). Our results indicate that DCE-MRI using both AIF methods is excellent in discriminating PCa from NNPT. If quantitative DCE-MRI is to be used as a biomarker in PCa, the same AIF method should be used consistently throughout the study.

Dynamic contrast-enhanced magnetic resonance imaging in prostate cancer clinical trials: potential roles and possible pitfalls

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluates the tissue microvasculature and may have a role in assessing and predicting therapeutic response in prostate cancer (PCa). In this review, we review principles of DCE-MRI and present the potential quantitative information that can be obtained. We discuss how it may be used as a biomarker for treatment with antiangiogenic and antivascular agents and potentially identify patients with PCa who may benefit from this form of therapy. Likewise, DCE-MRI may play a role in assessing response to combined androgen deprivation therapy and radiation therapy and theoretically could be a prognostic biomarker in evaluating second-generation hormone therapies. We also address the challenges of using DCE-MRI in PCa clinical trials and discuss the difficulties with standardization of this methodology to allow for biomarker validation, with particular reference to PCa.

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: a step towards practical implementation

Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (K(trans)) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of K(trans). Comparing cAIF, different estimates for both ve, and K(trans) were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while K(trans) values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. K(trans) estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.

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.

The effect of motion correction on pharmacokinetic parameter estimation in dynamic-contrast-enhanced MRI

A dynamic-contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset consists of many imaging frames, often acquired both before and after contrast injection. Due to the length of time spent acquiring images, patient motion is likely and image re-alignment or registration is required before further analysis such as pharmacokinetic model fitting. Non-rigid image registration procedures may be used to correct motion artefacts; however, a careful choice of registration strategy is required to reduce misregistration artefacts associated with enhancing features. This work investigates the effect of registration on the results of model-fitting algorithms for 52 DCE-MR mammography cases for 14 patients. Results are divided into two sections: a comparison of registration strategies in which a DCE-MRI-specific algorithm is preferred in 50% of cases, followed by an investigation of parameter changes with known applied deformations, inspecting the effect of magnitude and timing of motion artefacts. Increased motion magnitude correlates with increased model-fit residual and is seen to have a strong influence on the visibility of strongly enhancing features. Motion artefacts in images close to the contrast agent arrival have a disproportionate effect on discrepancies in parameter estimation. The choice of algorithm, magnitude of motion and timing of the motion are each shown to influence estimated pharmacokinetic parameters even when motion magnitude is small.

Automated registration of sequential breath-hold dynamic contrast-enhanced MR images: a comparison of three techniques

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions and also places constraints on temporal resolution vis-à-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all three methods, a 3D rigid-body registration scheme with a mutual information similarity measure was used as a preprocessing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration, which was corrected using the three schemes. All three schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI data sets is also presented.

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.

The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T

The purpose of this work is to quantify the accuracy of pharmacokinetic parameter measurement in DCE-MRI of breast cancer at 3 T in relation to three sources of error. Individually, T1 measurement error, temporal resolution and transmitted RF field inhomogeneity are considered. Dynamic contrast enhancement curves were simulated using standard acquisition parameters of a DCE-MRI protocol. Errors on pre-contrast T1 due to incorrect RF spoiling were considered. Flip angle errors were measured and introduced into the fitting routine, and temporal resolution was also varied. The error in fitted pharmacokinetic parameters, K(trans) and v(e), was calculated. Flip angles were found to be reduced by up to 55% of the expected value. The resultant errors in our range of K(trans) and v(e) were found to be up to 66% and 74%, respectively. Incorrect T1 estimation results in K(trans) and v(e) errors up to 531% and 233%, respectively. When the temporal resolution is reduced from 10 to 70 s K(trans) drops by up to 48%, while v(e) shows negligible variation. In combination, uncertainties in tissue T1 map and applied flip angle were shown to contribute to errors of up to 88% in K(trans) and 73% in v(e). These results demonstrate the importance of high temporal resolution, accurate T1 measurement and good B1 homogeneity.

Model-based and model-free parametric analysis of breast dynamic-contrast-enhanced MRI

A wide range of dynamic-contrast-enhanced (DCE) sequences and protocols, image processing methods, and interpretation criteria have been developed and evaluated over the last 20 years. In particular, attempts have been made to better understand the origin of the contrast observed in breast lesions using physiological models that take into account the vascular and tissue-specific features that influence tracer perfusion. In addition, model-free algorithms to decompose enhancement patterns in order to segment and classify different breast tissue types have been developed. This review includes a description of the mechanism of contrast enhancement by gadolinium-based contrast agents, followed by the current status of the physiological models used to analyze breast DCE-MRI and related critical issues. We further describe more recent unsupervised and supervised methods that use a range of different common algorithms. The model-based and model-free methods strive to achieve scientific accuracy and high clinical performance--both important goals yet to be reached.

New algorithm for quantifying vascular changes in dynamic contrast-enhanced MRI independent of absolute T1 values

In this work, we present a new method for predicting changes in tumor vascularity using only one flip angle in dynamic contrast-enhanced (DCE) imaging. The usual DCE approach finds the tissue initial T1 value T1(0) prior to injection of a contrast agent. We propose finding changes in the tissue contrast agent uptake characteristics pre- and postdrug treatment by fixing T1(0). Using both simulations and imaging pre- and postadministration of caffeine, we find that the relative change (NR50) in the median of the cumulative distribution (R50) is almost independent of T1(0). Fixing T1(0) leads to a concentration curve c(t) more robust to the presence of noise than calculating T1(0). Consequently, the NR50 for the tumor remains roughly the same as the ideal NR50 when T1(0) is exactly known. Further, variations in eating habits are shown to create significant changes in the R50 response for both liver and muscle. In conclusion, analyzing data with fixed T1(0) leads to a more stable measure of changes in NR50 and does not require knowledge of T1(0). Both caffeine and eating introduce major changes in blood flow that can significantly modify the NR50 and lead to incorrect conclusions regarding drug treatment.

Bayesian estimation of pharmacokinetic parameters for DCE-MRI with a robust treatment of enhancement onset time

When applying pharmacokinetic (PK) models to dynamic contrast enhanced MRI (DCE-MRI) data it is important to appropriately deal with the enhancement onset time, because errors in the onset time will affect the PK parameter estimates. This paper presents a Bayesian approach to the estimation of the PK parameters k(ep) and K(trans) that robustly treats the onset time. This approach involves the computation of an analytically intractable integral, so two approximate methods are developed. The first uses adaptive numerical quadrature, which produces results accurate to a given tolerance, and the other a simple approximation with a summation. These approaches are compared with each other, and with the standard least-squares (LS) approach. The results of a Monte Carlo experiment show that the LS approach produces biased estimates when k(ep) is large and K(trans) is small, whereas both the Bayesian methods are unbiased. The two Bayesian methods produce very similar estimates, but the simple summation method requires less than half the computation time of either the LS, or the quadrature approximation. The standard deviation of the LS estimates is shown to be larger than either of the Bayesian estimates, while uncertainty estimates based around a Hessian approximation are shown to be too small for all three methods. A more detailed method of assessing the uncertainty of the Bayesian approach is described, and the results show that this is a more accurate description of the estimation uncertainty.

A method for pharmacokinetic modelling of dynamic contrast enhanced MRI studies of rapidly enhancing lesions acquired in a clinical setting

Abnormal microcirculation is a feature of many neoplastic and non-neoplastic diseases. Physiological variables that characterize tissue microcirculation (capillary permeability and the volume of the extravascular extracellular fluid) are altered in pathological states. Pharmacokinetic analysis of dynamic contrast enhanced MRI (DCE-MRI) has found a widespread use in the assessment of abnormal microcirculation due to the direct link between the contrast agent kinetics and underlying microcirculatory properties. A representation of temporal variation of contrast agent concentration in blood plasma (C(p)(t)) is central to this analysis. In clinical applications of DCE-MRI, signal intensity curves derived from rapidly enhancing lesions often display a sigmoid shape during the initial phase of contrast uptake and rapid arrival at the equilibrium phase. In this work, the features of two principal methods for pharmacokinetic analysis of DCE-MRI which allow for theoretical representation of C(p)(t) are examined and combined to improve analysis of this particular class of DCE-MRI curves. The proposed method allows the representation of the initial sigmoid part of the enhancement profiles whilst retaining a realistic representation of C(p)(t) based on previously published measurements obtained in healthy volunteers. The results of the computer simulations indicate that in rapidly enhancing lesions, with the transfer constant K(trans) greater than 0.1 min(-1), the DCE-MRI acquisition can be restricted to 5 min post-injection and a mono-exponential representation of C(p)(t) decay is sufficient. Furthermore, non-ideal bolus delivery can be represented as a short constant rate infusion when the tissue under investigation exhibits a sigmoid pattern of contrast uptake.