Precision in measurements of perfusion and microvascular permeability withT1-weighted dynamic contrast-enhanced MRI

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis

This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the 'responder' and 'non-responder' tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.

Dynamic contrast-enhanced MRI perfusion parameters are imaging biomarkers for angiogenesis in lung cancer

BACKGROUND

Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may have the potential to reflect angiogenesis and proliferation of pulmonary neoplasms.

PURPOSE

To verify whether DCE-MRI can identify pulmonary neoplasm property and evaluate the correlation of DCE-MRI perfusion parameters with microvessel density (MVD) and Ki-67 in lung cancer.

MATERIAL AND METHODS

This study enrolled 65 patients with one pulmonary neoplasm who underwent computed tomography-guided percutaneous lung biopsy with pathological diagnosis (43 malignant, 22 benign; mean age = 59.71 ± 11.72 years). All patients did DCE-MRI before biopsy. Quantitative MRI parameters including endothelial transfer constant (K^trans), flux rate constant (K_ep), and fractional extravascular extracellular space (EES) volume (V_e) were calculated by extended Tofts linear model. MVD was evaluated by CD34-expressing tumor vessels. Proliferation was assessed by Ki-67 staining. The correlations of parameters with MVD and Ki-67 expression were analyzed.

RESULTS

K^trans and K_ep values were significantly increased in malignant lesions compared to benign lesions (P = 0.001 and 0.022, respectively), whereas no statistical difference in V_e was found. The CD34 expression was positively correlated to K^trans (r = 0.608; P = 0.004) and K_ep (r = 0.556; P = 0.001). Subsequent subtype analyses also showed positive correlations of K^trans and K_ep with MVD in adenocarcinoma group (r = 0.550 and 0.563; P = 0.012 and 0.015, respectively). No significant correlation was found between these parameters and Ki-67.

CONCLUSION

K^trans and K_ep may distinguish benign and malignant pulmonary neoplasm. K^trans and K_ep, with their positive correlation to MVD, can be used as non-invasive parameters reflecting lung cancer angiogenesis.

The LEGATOS technique: A new tissue-validated dynamic contrast-enhanced MRI method for whole-brain, high-spatial resolution parametric mapping

PURPOSE

A DCE-MRI technique that can provide both high spatiotemporal resolution and whole-brain coverage for quantitative microvascular analysis is highly desirable but currently challenging to achieve. In this study, we sought to develop and validate a novel dual-temporal resolution (DTR) DCE-MRI-based methodology for deriving accurate, whole-brain high-spatial resolution microvascular parameters.

METHODS

Dual injection DTR DCE-MRI was performed and composite high-temporal and high-spatial resolution tissue gadolinium-based-contrast agent (GBCA) concentration curves were constructed. The high-temporal but low-spatial resolution first-pass GBCA concentration curves were then reconstructed pixel-by-pixel to higher spatial resolution using a process we call LEGATOS. The accuracy of kinetic parameters (K^trans , v_p , and v_e ) derived using LEGATOS was evaluated through simulations and in vivo studies in 17 patients with vestibular schwannoma (VS) and 13 patients with glioblastoma (GBM). Tissue from 15 tumors (VS) was examined with markers for microvessels (CD31) and cell density (hematoxylin and eosin [H&E]).

RESULTS

LEGATOS derived parameter maps offered superior spatial resolution and improved parameter accuracy compared to the use of high-temporal resolution data alone, provided superior discrimination of plasma volume and vascular leakage effects compared to other high-spatial resolution approaches, and correlated with tissue markers of vascularity (P ≤ 0.003) and cell density (P ≤ 0.006).

CONCLUSION

The LEGATOS method can be used to generate accurate, high-spatial resolution microvascular parameter estimates from DCE-MRI.

Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application

Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen's d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates.

Blind deconvolution estimation of an arterial input function for small animal DCE-MRI

PURPOSE

One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution.

METHODS

A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution.

RESULTS

Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight.

CONCLUSION

Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.

Dynamic contrast-enhanced magnetic resonance imaging in denervated skeletal muscle: Experimental study in rabbits

Purpose

To investigate the value of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for evaluating denervated skeletal muscle in rabbits.

Materials and methods

24 male rabbits were randomly divided into an irreversible neurotmesis group and a control group. In the experimental group, the sciatic nerves of rabbits were transected for irreversible neurotmesis model. A sham operation was performed in the control group. MRI of rabbit lower legs was performed before nerve surgery and 1 day, 3 days, 5 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 6 weeks, 8 weeks, 10 weeks, and 12 weeks after surgery.

Results

Signal intensity changes were seen in the left gastrocnemius muscle on the T2-weighted images. DCE-MRI derived parameters (K^trans, K_ep, and V_p) were measured in vivo. In the irreversible neurotmesis group, T2-weighted images showed increased signal intensity in the left gastrocnemius muscle. K^trans, V_p values changes occur as early as 1 day after denervation, and increased gradually until 4 weeks after surgery. There are significant increases in both K^trans and V_p values compared with those in the control group after surgery (P < 0.05). K_ep values show no significant difference between the irreversible neurotmesis group and the control group.

Conclusion

DCE-MRI hold the promise of an early and sensitive diagnosis of denervated skeletal muscle.

Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves, which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals.

Robust and efficient pharmacokinetic parameter non-linear least squares estimation for dynamic contrast enhanced MRI of the prostate

PURPOSE

To describe an efficient numerical optimization technique using non-linear least squares to estimate perfusion parameters for the Tofts and extended Tofts models from dynamic contrast enhanced (DCE) MRI data and apply the technique to prostate cancer.

METHODS

Parameters were estimated by fitting the two Tofts-based perfusion models to the acquired data via non-linear least squares. We apply Variable Projection (VP) to convert the fitting problem from a multi-dimensional to a one-dimensional line search to improve computational efficiency and robustness. Using simulation and DCE-MRI studies in twenty patients with suspected prostate cancer, the VP-based solver was compared against the traditional Levenberg-Marquardt (LM) strategy for accuracy, noise amplification, robustness to converge, and computation time.

RESULTS

The simulation demonstrated that VP and LM were both accurate in that the medians closely matched assumed values across typical signal to noise ratio (SNR) levels for both Tofts models. VP and LM showed similar noise sensitivity. Studies using the patient data showed that the VP method reliably converged and matched results from LM with approximate 3× and 2× reductions in computation time for the standard (two-parameter) and extended (three-parameter) Tofts models. While LM failed to converge in 14% of the patient data, VP converged in the ideal 100%.

CONCLUSION

The VP-based method for non-linear least squares estimation of perfusion parameters for prostate MRI is equivalent in accuracy and robustness to noise, while being more reliably (100%) convergent and computationally about 3× (TM) and 2× (ETM) faster than the LM-based method.

TumourMetrics: a comprehensive clinical solution for the standardization of DCE-MRI analysis in research and routine use

Background

A reliable analysis methodology is needed to provide valuable imaging biomarkers for clinical trials, with particular regards to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) application using pharmacokinetic (PK) model analysis. In order to address this scientific challenge, we provided a comprehensive analysis solution that could overcome the impediments to clinical research and routine use.

Methods

TumourMetrics has been designed to meet the Quantitative Imaging Biomarkers Alliance (QIBA) v.1.0 profile. The quality performance was assessed using the QIBA test data and our customizable numeric phantom. The analysis workflow is made customizable to facilitate standardization of optimized protocol across centers.

Results

Our quantification workflow estimated the PK model parameters accurately. The method is robust, almost fully automatic and allows a direct integration of the results into the diagnostic workflow.

Conclusions

The analysis is easy-to-use and accessible for routine implementation of DCE-MRI into clinical practice.

Gadoxetate-enhanced MR imaging and compartmental modelling to assess hepatocyte bidirectional transport function in rats with advanced liver fibrosis

OBJECTIVES

Changes in the expression of hepatocyte membrane transporters in advanced fibrosis decrease the hepatic transport function of organic anions. The aim of our study was to assess if these changes can be evaluated with pharmacokinetic analysis of the hepatobiliary transport of the MR contrast agent gadoxetate.

METHODS

Dynamic gadoxetate-enhanced MRI was performed in 17 rats with advanced fibrosis and 8 normal rats. After deconvolution, hepatocyte three-compartmental analysis was performed to calculate the hepatocyte influx, biliary efflux and sinusoidal backflux rates. The expression of Oatp1a1, Mrp2 and Mrp3 organic anion membrane transporters was assessed with reverse transcription polymerase chain reaction.

RESULTS

In the rats with advanced fibrosis, the influx and efflux rates of gadoxetate decreased and the backflux rate increased significantly (p = 0.003, 0.041 and 0.010, respectively). Significant correlations were found between influx and Oatp1a1 expression (r = 0.78, p < 0.001), biliary efflux and Mrp2 (r = 0.50, p = 0.016) and sinusoidal backflux and Mrp3 (r = 0.61, p = 0.002).

CONCLUSION

These results show that changes in the bidirectional organic anion hepatocyte transport function in rats with advanced liver fibrosis can be assessed with compartmental analysis of gadoxetate-enhanced MRI.

KEY POINTS

• Expression of hepatocyte transporters is modified in rats with advanced liver fibrosis. • Kinetic parameters at gadoxetate-enhanced MRI are correlated with hepatocyte transporter expression. • Hepatocyte transport function can be assessed with compartmental analysis of gadoxetate-enhanced MRI. • Compartmental analysis of gadoxetate-enhanced MRI might provide biomarkers in advanced liver fibrosis.

Optimization of two-compartment-exchange-model analysis for dynamic contrast-enhanced mri incorporating bolus arrival time

PURPOSE

To optimize the analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) under the two-compartment-exchange-model (2CXM) and to incorporate voxelwise bolus-arrival-time (BAT).

MATERIALS AND METHODS

The accuracy of the pharmacokinetic (PK) parameters, extracted from 3T DCE-MRI using 2CXM, was tested under several conditions: eight algorithms for data estimation; correction for BAT; using model selection; different temporal resolution and scan duration. Comparisons were performed on simulated data. The best algorithm was applied to seven patients with brain tumors or following stroke. The extracted perfusion parameters were compared to those of dynamic susceptibility contrast MRI (DSC-MRI).

RESULTS

ACoPeD (AIF-corrected-perfusion-DCE-MRI), an analysis using a 2^nd derivative regularized-spline and incorporating BAT, achieved the most accurate estimation in simulated data, mean-relative-error: F_p , F, v_p , v_e : 24.8%, 41.7%, 26.4%, 27.2% vs. 76.5%, 190.8%, 78.8%, 82.39% of the direct four parameters estimation (one-sided two-sample t-test, P < 0.001). Correction for BAT increased the estimation accuracy of the PK parameters by more than 30% and provided a supertemporal resolution estimation of the BAT (higher than the acquired resolution, mean-absolute-error 0.2 sec). High temporal resolution (∼2 sec) is required to avoid biased estimation of PK parameters, and long scan duration (∼20 min) is important for reliable permeability but not for perfusion estimations, mean-error-reduction: E: ∼12%, v_e : ∼6%. Using ACoPeD, PK values from normal-appearing white matter, gray matter, and lesion were extracted from patients. Preliminary results showed significant voxelwise correlations to DSC-MRI, between flow values in a patient following stroke (r = 0.49, P < 0.001), and blood volume in a patient with a brain tumor (r = 0.62, P < 0.001).

CONCLUSION

This study proposes an optimized analysis method, ACoPeD, for tissue perfusion and permeability estimation using DCE-MRI, to be used in clinical settings.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:237-249.

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study

This study compares the shutter-speed (SS) and the Tofts models as used in assessing therapeutic response in a longitudinal DCE-MRI experiment. Sixteen nu/nu mice with implanted colorectal adenocarcinoma cell line (LS-174T) were randomly assigned into treatment/control groups (n  =  8/group) and received bevacizumab/saline twice weekly (Day1/Day4/Day8). All mice were scanned at one pre- (Day0) and two post-treatment (Day2/Day9) time points using a high spatiotemporal resolution DCE-MRI pulse sequence. The CA extravasation rate constant [Formula: see text] from the Tofts/SS model and the mean intracellular water residence time [Formula: see text] from the SS model were analyzed. A biological subvolume (BV) within the tumor was identified based on the [Formula: see text] intensity distribution, and the SS model parameters within the BV ([Formula: see text] and [Formula: see text]) were analyzed. It is found that [Formula: see text] and [Formula: see text] have a similar spatial distribution in the tumor volume. The Bayesian information criterion results show that the SS model was a better fit for all scans. At Day9, the treatment group had significantly higher tumor mean [Formula: see text] (p  =  0.021), [Formula: see text] (p  =  0.021) and [Formula: see text] (p  = 0.045). When BV from transcytolemmal water exchange analysis was adopted, the treatment group had higher mean [Formula: see text] at both Day2 (p  =  0.038) and Day9 (p  =  0.007). Additionally, at Day9, the treatment group had higher mean [Formula: see text] (p  =  0.045) and higher [Formula: see text] spatial heterogeneity indices (Rényi dimensions) d 1 (p  = 0.010) and d 2 (p  = 0.021). When mean [Formula: see text] and its coefficient of variation (CV) were used to separate treatment/control group samples using supporting vector machine, the accuracy of treatment/control classification was 68.8% at Day2 and 87.5% at Day9; in contrast, the Day2/Day9 accuracy were 62.5%/87.5% using tumor mean [Formula: see text] and its CV and were 50.0%/87.5% using tumor mean [Formula: see text] and its CV, respectively. These results suggest that the SS model parameters outperformed the Tofts model parameters in terms of capturing bevacizumab therapeutic effect in this longitudinal experiment.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

PURPOSE

To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging.

METHODS

Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K^trans in the extended Tofts model and blood flow F_B and blood volume V_B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated.

RESULTS

The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data.

CONCLUSION

With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation

The use of multiparametric MRI scans for the evaluation of men with prostate cancer has increased dramatically and is likely to continue expanding as new developments come to practice. However, it has not yet gained the same level of acceptance of other imaging tests. Partly, this is because of the use of suboptimal protocols, lack of standardization, and inadequate patient preparation. In this manuscript, we describe several practical aspects of prostate MRI that may facilitate the implementation of new prostate imaging programs or the expansion of existing ones.

Application of intravoxel incoherent motion perfusion imaging to shoulder muscles after a lift-off test of varying duration

Intravoxel incoherent motion (IVIM) MRI is a method to extract microvascular blood flow information out of diffusion-weighted images acquired at multiple b-values. We hypothesized that IVIM can identify the muscles selectively involved in a specific task, by measuring changes in activity-induced local muscular perfusion after exercise. We tested this hypothesis using a widely used clinical maneuver, the lift-off test, which is known to assess specifically the subscapularis muscle functional integrity. Twelve shoulders from six healthy male volunteers were imaged at 3 T, at rest, as well as after a lift-off test hold against resistance for 30 s, 1 and 2 min respectively, in three independent sessions. IVIM parameters, consisting of perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient D* and blood flow-related fD*, were estimated within outlined muscles of the rotator cuff and the deltoid bundles. The mean values at rest and after the lift-off tests were compared in each muscle using a one-way ANOVA. A statistically significant increase in fD* was measured in the subscapularis, after a lift-off test of any duration, as well as in D. A fD* increase was the most marked (30 s, +103%; 1 min, +130%; 2 min, +156%) and was gradual with the duration of the test (in 10(-3) mm(2) /s: rest, 1.41 ± 0.50; 30 s, 2.86 ± 1.17; 1 min, 3.23 ± 1.22; 2 min, 3.60 ± 1.21). A significant increase in fD* and D was also visible in the posterior bundle of the deltoid. No significant change was consistently visible in the other investigated muscles of the rotator cuff and the other bundles of the deltoid. In conclusion, IVIM fD* allows the demonstration of a task-related microvascular perfusion increase after a specific task and suggests a direct relationship between microvascular perfusion and the duration of the effort. It is a promising method to investigate non-invasively skeletal muscle physiology and clinical perfusion-related muscular disorders.

DUSTER: dynamic contrast enhance up-sampled temporal resolution analysis method

Dynamic contrast enhanced (DCE) MRI using Tofts' model for estimating vascular permeability is widely accepted, yet inter-tissue differences in bolus arrival time (BAT) are generally ignored. In this work we propose a method, incorporating the BAT in the analysis, demonstrating its applicability and advantages in healthy subjects and patients. A method for DCE Up Sampled TEmporal Resolution (DUSTER) analysis is proposed which includes: baseline T1 map using DESPOT1 analyzed with flip angle (FA) correction; preprocessing; raw-signal-to-T1-to-concentration time curves (CTC) conversion; automatic arterial input function (AIF) extraction at temporal super-resolution; model fitting with model selection while incorporating BAT in the pharmacokinetic (PK) model, and fits contrast agent CTC while using exhaustive search in the BAT dimension in super-resolution. The method was applied to simulated data and to human data from 17 healthy subjects, six patients with glioblastoma, and two patients following stroke. BAT values were compared to time-to-peak (TTP) values extracted from dynamic susceptibility contrast imaging. Results show that the method improved the AIF estimation and allowed extraction of the BAT with a resolution of 0.8 s. In simulations, lower mean relative errors were detected for all PK parameters extracted using DUSTER compared to analysis without BAT correction (vp:5% vs. 20%, Ktrans: 9% vs. 24% and Kep: 8% vs. 17%, respectively), and BAT estimates demonstrated high correlations (r = 0.94, p < 1e− 10) with true values. In real data, high correlations between BAT values were detected when extracted from data acquired with high temporal resolution (2 s) and sub-sampled standard resolution data (6 s) (mean r = 0.85,p < 1e− 10). BAT and TTP values were significantly correlated in the different brain regions in healthy subjects (mean r = 0.72,p = < 1e− 3), as were voxel-wise comparisons in patients (mean r = 0.89, p < 1e− 10). In conclusion, incorporating BAT in DCE analysis improves estimation accuracy for the AIF and the PK parameters while providing an additional clinically important parameter.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast enhanced MRI data

PURPOSE

To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors.

THEORY

Quantitative DCE-MRI requires an estimate of precontrast T1 , which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters.

METHODS

Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions.

RESULTS

In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%).

CONCLUSION

Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc.

Fitting the two-compartment model in DCE-MRI by linear inversion

PURPOSE

Model fitting of dynamic contrast-enhanced-magnetic resonance imaging-MRI data with nonlinear least squares (NLLS) methods is slow and may be biased by the choice of initial values. The aim of this study was to develop and evaluate a linear least squares (LLS) method to fit the two-compartment exchange and -filtration models.

METHODS

A second-order linear differential equation for the measured concentrations was derived where model parameters act as coefficients. Simulations of normal and pathological data were performed to determine calculation time, accuracy and precision under different noise levels and temporal resolutions. Performance of the LLS was evaluated by comparison against the NLLS.

RESULTS

The LLS method is about 200 times faster, which reduces the calculation times for a 256 × 256 MR slice from 9 min to 3 s. For ideal data with low noise and high temporal resolution the LLS and NLLS were equally accurate and precise. The LLS was more accurate and precise than the NLLS at low temporal resolution, but less accurate at high noise levels.

CONCLUSION

The data show that the LLS leads to a significant reduction in calculation times, and more reliable results at low noise levels. At higher noise levels the LLS becomes exceedingly inaccurate compared to the NLLS, but this may be improved using a suitable weighting strategy. Magn Reson Med 76:998-1006, 2016. © 2015 Wiley Periodicals, Inc.

Dynamic contrast-enhanced MRI in mice: an investigation of model parameter uncertainties

PURPOSE

To establish the experimental factors that dominate the uncertainty of hemodynamic parameters in commonly used pharmacokinetic models.

METHODS

By fitting simulation results from a multiregion tissue exchange model (Multiple path, Multiple tracer, Indicator Dilution, 4 region), the precision and accuracy of hemodynamic parameters in dynamic contrast-enhanced MRI with four tracer kinetic models is investigated. The impact of various injection rates as well as imprecise knowledge of the arterial input functions is examined.

RESULTS

Fast injections are beneficial for K(trans) precision within the extended Tofts model and within the two-compartment exchange model but do not affect the other models under investigation. Biases from errors in the arterial input functions are mostly consistent in size and direction for the simple and the extended Tofts model, while they are hardly predictable for the other models. Errors in the hematocrit introduce the greatest loss in parameter accuracy, amounting to an average K(trans) bias of 40% for a 30% overestimation throughout all models.

CONCLUSION

This simulation study allows the detailed inspection of the isolated impact from various experimental conditions on parameter uncertainty. Because parameter uncertainty comparable to human studies was found, this study represents a validation of preclinical dynamic contrast-enhanced MRI for modeling human tumor physiology.

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

PURPOSE

One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used.

METHODS

Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested.

RESULTS

The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates.

CONCLUSION

Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.

An efficient calculation method for pharmacokinetic parameters in brain permeability study using dynamic contrast-enhanced MRI

PURPOSE

To develop an efficient method for calculating pharmacokinetic (PK) parameters in brain DCE-MRI permeability studies.

METHODS

A linear least-squares fitting algorithm based on a derivative expression of the two-compartment PK model was proposed to analytically solve for the PK parameters. Noise in the expression was minimized through low-pass filtering. Simulation studies were conducted in which the proposed method was compared with two existing methods in terms of accuracy and efficiency. Five in vivo brain studies were demonstrated for potential clinical application.

RESULTS

In the simulation studies using chosen parameter values, the calculated percent difference of K(trans) by the proposed method was <5.0% with a temporal resolution (Δt) < 5 s, and the accuracies of all parameter results were better or comparable to existing methods. When analyzed within certain parameter intensity ranges, the proposed method was more accurate than the existing methods and improved the efficiency by a factor of up to 458 for a Δt = 1 s and up to 38 for a Δt = 5 s. In the in vivo study, the calculated parameters using the proposed method were comparable to those using the existing methods with improved efficiencies.

CONCLUSIONS

An efficient method was developed for the accurate and efficient calculation of parameters in brain DCE-MRI permeability studies.

An analysis of the uncertainty and bias in DCE-MRI measurements using the spoiled gradient-recalled echo pulse sequence

PURPOSE

The pharmacokinetic parameters derived from dynamic contrast-enhanced (DCE) MRI have been used in more than 100 phase I trials and investigator led studies. A comparison of the absolute values of these quantities requires an estimation of their respective probability distribution function (PDF). The statistical variation of the DCE-MRI measurement is analyzed by considering the fundamental sources of error in the MR signal intensity acquired with the spoiled gradient-echo (SPGR) pulse sequence.

METHODS

The variance in the SPGR signal intensity arises from quadrature detection and excitation flip angle inconsistency. The noise power was measured in 11 phantoms of contrast agent concentration in the range [0-1] mM (in steps of 0.1 mM) and in onein vivo acquisition of a tumor-bearing mouse. The distribution of the flip angle was determined in a uniform 10 mM CuSO4 phantom using the spin echo double angle method. The PDF of a wide range of T1 values measured with the varying flip angle (VFA) technique was estimated through numerical simulations of the SPGR equation. The resultant uncertainty in contrast agent concentration was incorporated in the most common model of tracer exchange kinetics and the PDF of the derived pharmacokinetic parameters was studied numerically.

RESULTS

The VFA method is an unbiased technique for measuringT1 only in the absence of bias in excitation flip angle. The time-dependent concentration of the contrast agent measured in vivo is within the theoretically predicted uncertainty. The uncertainty in measuring K(trans) with SPGR pulse sequences is of the same order, but always higher than, the uncertainty in measuring the pre-injection longitudinal relaxation time (T10). The lowest achievable bias/uncertainty in estimating this parameter is approximately 20%-70% higher than the bias/uncertainty in the measurement of the pre-injection T1 map. The fractional volume parameters derived from the extended Tofts model were found to be extremely sensitive to the variance in signal intensity. The SNR of the pre-injection T1 map indicates the limiting precision with which K(trans) can be calculated.

CONCLUSIONS

Current small-animal imaging systems and pulse sequences robust to motion artifacts have the capacity for reproducible quantitative acquisitions with DCE-MRI. In these circumstances, it is feasible to achieve a level of precision limited only by physiologic variability.

Spectral clustering applied for dynamic contrast-enhanced MR analysis of time-intensity curves

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) represents an emerging method for the prediction of biomarker responses in cancer. However, DCE images remain difficult to analyze and interpret. Although pharmacokinetic approaches, which involve multi-step processes, can provide a general framework for the interpretation of these data, they are still too complex for robust and accurate implementation. Therefore, statistical data analysis techniques were recently suggested as another valid interpretation strategy for DCE-MRI. In this context, we propose a spectral clustering approach for the analysis of DCE-MRI time-intensity signals. This graph theory-based method allows for the grouping of signals after spatial transformation. Subsequently, these data clusters can be labeled following comparison to arterial signals. Here, we have performed experiments with simulated (i.e., generated via pharmacokinetic modeling) and clinical (i.e., obtained from patients scanned during prostate cancer diagnosis) data sets in order to demonstrate the feasibility and applicability of this kind of unsupervised and non-parametric approach.

Advanced techniques in pediatric abdominopelvic oncologic magnetic resonance imaging

Advances in the treatment of pediatric abdominopelvic malignancies have increased survival drastically. Imaging is critical in initial tumor characterization/staging, assessment of treatment response, and surveillance following therapy. Magnetic resonance imaging (MRI) is playing an increasing role in the care of these patients due to its lack of ionizing radiation, superior contrast resolution and the ability to characterize tumors based on tissue characteristics (e.g., T1 and T2 relaxation times). Modern MR techniques also allow for assessment of tumors based on functional characteristics. This article is focused on emerging MRI technologies and potential applications in the imaging of pediatric abdominopelvic malignancies.

Detection of liver micrometastases from colorectal origin by perfusion CT in a rat model

BACKGROUND

Some patients with colorectal carcinoma have liver metastases (LMs) which cannot be detected by conventional imaging. This study aimed to assess whether hepatic perfusion changes induced by micrometastases can be detected by perfusion computed tomography (CT).

METHODS

LMs were produced in rats by injecting carcinoma cells into the portal vein. Perfusion CT was performed at microscopic (day 10), interval (day 17), and macroscopic stage (day 34). Perfusion parameters were computed using a dual-input one-compartmental model.

RESULTS

Micro and macro LMs presented a mean diameter of 0.5 and 2.6 mm, respectively. Compared to controls, LMs at interval (1.1 mm) and macroscopic stage induced significant perfusion changes: a decrease of 42% (P=0.004) and 41% (P=0.029) in hepatic transit time and an increase of 292% (P=0.073) and 240% (P=0.001) in portal delay, respectively.

CONCLUSIONS

LMs with a mean diameter between 1.1 and 2.6 mm induced significant hepatic perfusion changes, detected by CT. Such detection may help to select patients and propose chemotherapy at the time of primary tumor resection.

A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors

In the last few years, several imaging methods, such as magnetic resonance imaging (MRI) and computed tomography, have been used to investigate the degree of blood-brain barrier (BBB) permeability in patients with neurological diseases including multiple sclerosis, ischemic stroke, and brain tumors. One promising MRI method for assessing the BBB permeability of patients with neurological diseases in vivo is T1-weighted dynamic contrast-enhanced (DCE)-MRI. Here we review the technical issues involved in DCE-MRI in the study of human brain tumors. In the first part of this paper, theoretical models for the DCE-MRI analysis will be described, including the Toft-Kety models, the adiabatic approximation to the tissue homogeneity model and the two-compartment exchange model. These models can be used to estimate important kinetic parameters related to BBB permeability. In the second part of this paper, details of the data acquisition, issues related to the arterial input function, and procedures for DCE-MRI image analysis are illustrated.

Optimization of the reference region method for dual pharmacokinetic modeling using Gd-DTPA/MRI and (18) F-FDG/PET

PURPOSE

The combination of MRI and positron emission tomography (PET) offers new possibilities for the development of novel methodologies. In pharmacokinetic image analysis, the blood concentration of the imaging compound as a function of time, [i.e., the arterial input function (AIF)] is required for MRI and PET. In this study, we tested whether an AIF extracted from a reference region (RR) in MRI can be used as a surrogate for the manually sampled (18) F-FDG AIF for pharmacokinetic modeling.

METHODS

An MRI contrast agent, gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) and a radiotracer, (18) F-fluorodeoxyglucose ((18) F-FDG), were simultaneously injected in a F98 glioblastoma rat model. A correction to the RR AIF for Gd-DTPA is proposed to adequately represent the manually sampled AIF. A previously published conversion method was applied to convert this AIF into a (18) F-FDG AIF.

RESULTS

The tumor metabolic rate of glucose (TMRGlc) calculated with the manually sampled (18) F-FDG AIF, the (18) F-FDG AIF converted from the RR AIF and the (18) F-FDG AIF converted from the corrected RR AIF were found not statistically different (P>0.05).

CONCLUSION

An AIF derived from an RR in MRI can be accurately converted into a (18) F-FDG AIF and used in PET pharmacokinetic modeling.

Uncertainty in MR tracer kinetic parameters and water exchange rates estimated from T1-weighted dynamic contrast enhanced MRI

PURPOSE

The aim of this study was to assess the uncertainty in estimation of MR tracer kinetic parameters and water exchange rates in T1-weighted dynamic contrast enhanced (DCE) MRI.

METHODS

Simulated DCE-MRI data were used to assess four kinetic models; general kinetic model with a vascular compartment (GKM2), GKM2 combined with water exchange (SSM2), adiabatic approximation of the tissue homogeneity model (ATH), and ATH combined with water exchange (ATHX).

RESULTS

In GKM2 and SSM2, increase in transfer constant (K(trans)) led to underestimation of vascular volume fraction (vb), and increase in vb led to overestimation of K(trans). Such coupling between K(trans) and vb was not observed in ATH and ATHX. The precision of estimated intracellular water lifetime (τi) was substantially improved in both SSM2 and ATHX when K(trans) > 0.3 min(-1). K(trans) and vb from ATHX model had significantly smaller errors than those from ATH model (P < 0.05).

CONCLUSION

The results of this study demonstrated the feasibility of measuring τi from DCE-MRI data albeit low precision. While the inclusion of water exchange improved the accuracy of K(trans), vb, and the interstitial volume fraction estimation (ve), it lowered the precision of other kinetic model parameters within the conditions investigated in this study.

Dynamic contrast-enhanced MRI in patients with muscle-invasive transitional cell carcinoma of the bladder can distinguish between residual tumour and post-chemotherapy effect

INTRODUCTION

Treatment of muscle-invasive bladder cancer with chemotherapy results in haemorrhagic inflammation, mimicking residual tumour on conventional MR images and making interpretation difficult. The aim of this study was to use dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to estimate descriptive and tracer kinetic parameters post-neoadjuvant chemotherapy and to investigate whether parameters differed in areas of residual tumour and chemotherapy-induced haemorrhagic inflammation (treatment effect, Tr-Eff).

METHODS AND MATERIALS

Twenty-one patients underwent DCE-MRI scans with 2.5s temporal resolution before and following neoadjuvant chemotherapy. Regions-of-interest (ROIs) were defined in areas suspicious of residual tumour on T2-weighted MRI scans. Data were analysed semi-quantitatively and with a two-compartment exchange model to obtain parameters including relative signal intensity (rSI80s) and plasma perfusion (Fp) respectively. The bladder was subsequently examined histologically after cystectomy for evidence of residual tumour and/or Tr-Eff. Differences in parameters measured in areas of residual tumour and Tr-Eff were examined using Student's t-test.

RESULTS

Twenty-four abnormal sites were defined after neoadjuvant chemotherapy. On pathology, 10 and 14 areas were identified as residual tumour and Tr-Eff respectively. Median rSI80s and Fp were significantly higher in areas of residual tumour than Tr-Eff (rSI80s = 2.9 vs 1.7, p < 0.001; Fp = 20.7 vs 9.1 ml/100ml/min, p = 0.03). The sensitivity and specificity for differentiating residual tumour from Tr-Eff were 70% and 100% (rSI80s), 60% and 86% (Fp), and 75% and 100% when combined.

CONCLUSION

DCE-MRI parameters obtained post-treatment are capable of distinguishing between residual tumour and treatment effect in patients treated for bladder cancer with neoadjuvant chemotherapy.

Classic models for dynamic contrast-enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.

Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer

Dynamic contrast-enhanced computed tomography (DCE-CT) and magnetic resonance imaging (DCE-MRI) are functional imaging techniques. They aim to characterise the microcirculation by applying the principles of tracer-kinetic analysis to concentration-time curves measured in individual image pixels. In this paper, we review the basic principles of DCE-MRI and DCE-CT, with a specific emphasis on the use of tracer-kinetic modeling. The aim is to provide an introduction to the field for a broader audience of pharmacokinetic modelers. In a first part, we first review the key aspects of data acquisition in DCE-CT and DCE-MRI, including a review of basic measurement strategies, a discussion on the relation between signal and concentration, and the problem of measuring reference data in arterial blood. In a second part, we define the four main parameters that can be measured with these techniques and review the most common tracer-kinetic models that are used in this field. We first discuss the models for the capillary bed and then define the most general four-parameter models used today: the two-compartment exchange model, the tissue-homogeneity model, the "adiabatic approximation to the tissue-homogeneity model" and the distributed-parameter model. In simpler tissue types or when the data quality is inadequate to resolve all the features of the more complex models, it is often necessary to resort to simpler models, which are special cases of the general models and hence have less parameters. We discuss the most common of these special cases, i.e. the uptake models, the extended Tofts model, and the one-compartment model. Models for two specific tissue types, liver and kidney, are discussed separately. We conclude with a review of practical aspects of DCE-CT and DCE-MRI data analysis, including the problem of identifying a suitable model for any given data set, and a brief discussion of the application of tracer-kinetic modeling in the context of drug development. Here, an important application of DCE techniques is the derivation of quantitative imaging biomarkers for the assessment of effects of targeted therapeutics on tumors.

Kinetic model optimization for characterizing tumour physiology by dynamic contrast-enhanced near-infrared spectroscopy

Dynamic contrast-enhanced (DCE) methods are widely used with magnetic resonance imaging and computed tomography to assess the vascular characteristics of tumours since these properties can affect the response to radiotherapy and chemotherapy. In contrast, there have been far fewer studies using optical-based applications despite the advantages of low cost and safety. This study investigated an appropriate kinetic model for optical applications to characterize tumour haemodynamics (blood flow, F, blood volume, V(b), and vascular heterogeneity) and vascular leakage (permeability surface-area product, PS). DCE data were acquired with two dyes, indocyanine green (ICG) and 800 CW carboxylate (IRD(cbx)), from a human colon tumour xenograph model in rats. Due to the smaller molecular weight of IRD(cbx) (1166 Da) compared to albumin-bound ICG (67 kDa), PS of IRD(cbx) was significantly larger; however, no significant differences in F and V(b) were found between the dyes as expected. Error analysis demonstrated that all parameters could be estimated with an uncertainty less than 5% due to the high temporal resolution and signal-to-noise ratio of the optical measurements. The next step is to adapt this approach to optical imaging to generate haemodynamics and permeability maps, which should enhance the clinical interest in optics for treatment monitoring.

Response of HT29 colorectal xenograft model to cediranib assessed with 18 F-fluoromisonidazole positron emission tomography, dynamic contrast-enhanced and diffusion-weighted MRI

Cediranib is a small-molecule pan-vascular endothelial growth factor receptor inhibitor. The tumor response to short-term cediranib treatment was studied using dynamic contrast-enhanced and diffusion-weighted MRI at 7 T, as well as (18) F-fluoromisonidazole positron emission tomography and histological markers. Rats bearing subcutaneous HT29 human colorectal tumors were imaged at baseline; they then received three doses of cediranib (3 mg/kg per dose daily) or vehicle (dosed daily), with follow-up imaging performed 2 h after the final cediranib or vehicle dose. Tumors were excised and evaluated for the perfusion marker Hoechst 33342, the endothelial cell marker CD31, smooth muscle actin, intercapillary distance and tumor necrosis. Dynamic contrast-enhanced MRI-derived parameters decreased significantly in cediranib-treated tumors relative to pretreatment values [the muscle-normalized initial area under the gadolinium concentration curve decreased by 48% (p=0.002), the enhancing fraction by 43% (p=0.003) and K(trans) by 57% (p=0.003)], but remained unchanged in controls. No change between the pre- and post-treatment tumor apparent diffusion coefficients in either the cediranib- or vehicle-treated group was observed over the course of this study. The (18) F-fluoromisonidazole mean standardized uptake value decreased by 33% (p=0.008) in the cediranib group, but showed no significant change in the control group. Histological analysis showed that the number of CD31-positive vessels (59 per mm(2) ), the fraction of smooth muscle actin-positive vessels (80-87%) and the intercapillary distance (0.17 mm) were similar in cediranib- and vehicle-treated groups. The fraction of perfused blood vessels in cediranib-treated tumors (81 ± 7%) was lower than that in vehicle controls (91 ± 3%, p=0.02). The necrotic fraction was slightly higher in cediranib-treated rats (34 ± 12%) than in controls (26 ± 10%, p=0.23). These findings suggest that short-term treatment with cediranib causes a decrease in tumor perfusion/permeability across the tumor cross-section, but changes in vascular morphology, vessel density or tumor cellularity are not manifested at this early time point.

Dynamic contrast-enhanced MRI assessment of hyperemic fractional microvascular blood plasma volume in peripheral arterial disease: initial findings

Objectives

The aim of the current study was to describe a method that assesses the hyperemic microvascular blood plasma volume of the calf musculature. The reversibly albumin binding contrast agent gadofosveset was used in dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) to assess the microvascular status in patients with peripheral arterial disease (PAD) and healthy controls. In addition, the reproducibility of this method in healthy controls was determined.

Materials and Methods

Ten PAD patients with intermittent claudication and 10 healthy control subjects were included. Patients underwent contrast-enhanced MR angiography of the peripheral arteries, followed by one DCE MRI examination of the musculature of the calf. Healthy control subjects were examined twice on different days to determine normative values and the interreader and interscan reproducibility of the technique. The MRI protocol comprised dynamic imaging of contrast agent wash-in under reactive hyperemia conditions of the calf musculature. Using pharmacokinetic modeling the hyperemic fractional microvascular blood plasma volume (V_p, unit: %) of the anterior tibial, gastrocnemius and soleus muscles was calculated.

Results

V_p was significantly lower for all muscle groups in PAD patients (4.3±1.6%, 5.0±3.3% and 6.1±3.6% for anterior tibial, gastrocnemius and soleus muscles, respectively) compared to healthy control subjects (9.1±2.0%, 8.9±1.9% and 9.3±2.1%). Differences in V_p between muscle groups were not significant. The coefficient of variation of V_p varied from 10–14% and 11–16% at interscan and interreader level, respectively.

Conclusions

Using DCE MRI after contrast-enhanced MR angiography with gadofosveset enables reproducible assessment of hyperemic fractional microvascular blood plasma volume of the calf musculature. V_p was lower in PAD patients than in healthy controls, which reflects a promising functional (hemodynamic) biomarker for the microvascular impairment of macrovascular lesions.

Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging

Many therapeutic approaches to cancer affect the tumour vasculature, either indirectly or as a direct target. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important means of investigating this action, both pre-clinically and in early stage clinical trials. For such trials, it is essential that the measurement process (i.e. image acquisition and analysis) can be performed effectively and with consistency among contributing centres. As the technique continues to develop in order to provide potential improvements in sensitivity and physiological relevance, there is considerable scope for between-centre variation in techniques. A workshop was convened by the Imaging Committee of the Experimental Cancer Medicine Centres (ECMC) to review the current status of DCE-MRI and to provide recommendations on how the technique can best be used for early stage trials. This review and the consequent recommendations are summarised here. Key Points • Tumour vascular function is key to tumour development and treatment • Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumour vascular function • Thus DCE-MRI with pharmacokinetic models can assess novel treatments • Many recent developments are advancing the accuracy of and information from DCE-MRI • Establishing common methodology across multiple centres is challenging and requires accepted guidelines.

Diffusion-weighted MRI, dynamic susceptibility contrast MRI and ultrasound perfusion quantification of denervated muscle in rabbits

OBJECTIVE

The purpose of this study was to assess denervated muscle perfusion using dynamic susceptibility contrast MRI (DSCMRI) and contrast-enhanced ultrasound (CEUS), and to measure denervated muscle apparent diffusion coefficient (ADC) on b1000 diffusion-weighted MRI (DWMRI) at 3 T in order to clarify whether muscle denervation leads to an increase in the extracellular extravascular space, or an increase in blood flow-or both.

MATERIALS AND METHODS

Axotomy of the right sciatic nerve of six white rabbits was performed at day 0. At day 9, hind limb muscles MRI and CEUS were performed to assess the consequences of denervation and both semimembranosus muscles of each rabbit were explanted for histological studies. Signal intensity on T2- and T1-weighted MRI, ADC on DWMRI, maximum signal drop (MSD) on DSCMRI and the area under the curve (AUC) on CEUS were measured over circular regions of interest (ROI), in both semimembranosus muscles. Non-parametric Wilcoxon matched-pairs tests were used to assess the mean differences between denervated and normal muscles.

RESULTS

T2 fat-saturated (FS) MRI studies showed a strong signal in the right semimembranosus muscles compared with the left side, and gadolinium enhancement was observed on T1 FS MRI. Denervated muscles show a significant increase in ADC on DWMRI (p < 0.01) and a significant signal enhancement on DSCMR imaging (p < 0.05) and on first-pass CEUS (p < 0.05).

CONCLUSION

The results of this study--based on perfusion- and diffusion-weighted images--suggest that, after denervation, both increased blood flow through muscle tissue and expansion of the extracellular water volume are present.

Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability

The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter K(trans) that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.

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.