Multiple reference tissue method for contrast agent arterial input function estimation

Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling

PURPOSE

Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied.

METHODS

The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution.

RESULTS

The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s.

CONCLUSION

We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma

OBJECTIVE

Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI.

MATERIALS AND METHODS

The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome.

RESULTS

Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases.

CONCLUSION

The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.

Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [K^trans ], fractional volume of the extravascular extracellular space [v_e ], and blood plasma volume fraction [v_p ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of K^trans and v_e , and less than 11% in the estimation of v_p . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.

Comparison of blind deconvolution- and Patlak analysis-based methods for determining vascular permeability

This study describes a computational algorithm to determine vascular permeability constants from time-lapse imaging data without concurrent knowledge of the arterial input function. The algorithm is based on "blind" deconvolution of imaging data, which were generated with analytical and finite-element models of bidirectional solute transport between a capillary and its surrounding tissue. Compared to the commonly used Patlak analysis, the blind algorithm is substantially more accurate in the presence of solute delay and dispersion. We also compared the performance of the blind algorithm with that of a simpler one that assumed unidirectional transport from capillary to tissue [as described in Truslow et al., Microvasc. Res. 90, 117-120 (2013)]. The algorithm based on bidirectional transport was more accurate than the one based on unidirectional transport for more permeable vessels and smaller extravascular distribution volumes, and less accurate for less permeable vessels and larger extravascular distribution volumes. Our results indicate that blind deconvolution is superior to Patlak analysis for permeability mapping under clinically relevant conditions, and can thus potentially improve the detection of tissue regions with a compromised vascular barrier.

Reference region extraction by clustering for the pharmacokinetic analysis of dynamic contrast-enhanced MRI in prostate cancer

PURPOSE

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures changes in the concentration of an administered contrast agent to quantitatively evaluate blood circulation in a tumor or normal tissues. This method uses a pharmacokinetic analysis based on the time course of a reference region, such as muscle, rather than arterial input function. However, it is difficult to manually define a homogeneous reference region. In the present study, we developed a method for automatic extraction of the reference region using a clustering algorithm based on a time course pattern for DCE-MRI studies of patients with prostate cancer.

METHODS

Two feature values related to the shape of the time course were extracted from the time course of all voxels in the DCE-MRI images. Each voxel value of T1-weighted images acquired before administration were also added as anatomical data. Using this three-dimensional feature vector, all voxels were segmented into five clusters by the Gaussian mixture model, and one of these clusters that included the gluteus muscle was selected as the reference region.

RESULTS

Each region of arterial vessel, muscle, and fat was segmented as a different cluster from the tumor and normal tissues in the prostate. In the extracted reference region, other tissue elements including scattered fat and blood vessels were removed from the muscle region.

CONCLUSIONS

Our proposed method can automatically extract the reference region using the clustering algorithm with three types of features based on the time course pattern and anatomical data. This method may be useful for evaluating tumor circulatory function in DCE-MRI studies.

Pharmacokinetic modeling of dynamic contrast-enhanced MRI using a reference region and input function tail

PURPOSE

Quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF.

METHODS

RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5-30 s) and noise levels (σ = 0.01-0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient-specific AIF tails, and another assuming a literature-based AIF tail.

RESULTS

RRIFT estimated the transfer constant K trans and interstitial volume v e with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of K trans had a median and interquartile range of -9 ± 45% with the ETM and -2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for K trans , 0.96 for v e , and 0.73 for the plasma volume v p using a measured AIF tail. With the literature-based AIF tail, the CCC was 0.89 for K trans , 0.93 for v e and 0.78 for v p .

CONCLUSIONS

Quantitative DCE-MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.

An extended reference region model for DCE-MRI that accounts for plasma volume

The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated K^trans by over 10% at v_p  = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible v_p where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.

Image-guided chemotherapy with specifically tuned blood brain barrier permeability in glioma margins

Blood-brain barrier (BBB) disruption is frequently observed in the glioma region. However, the tumor uptake of drugs is still too low to meet the threshold of therapeutic purpose.

Method: A tumor vasculature-targeted nanoagonist was developed. Glioma targeting specificity of the nanoagonist was evaluated by in vivo optical imaging. BBB permeability at the glioma margin was quantitatively measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Single-photon emission computed tomography imaging/computed tomography (SPECT/CT) quantitatively determined the glioma uptake of the radiolabeled model drug. T2-weighted MRI monitored the tumor volume.

Results: Immunostaining studies demonstrated that the BBB remained partially intact in the invasive margin of patients' gliomas regardless of their malignancies. DCE-MRI showed that vascular permeability in the glioma margin reached its maximum at 45 min post nanoagonist administration. In vivo optical imaging indicated the high glioma targeting specificity of the nanoagonist. SPECT/CT showed the significantly enhanced glioma uptake of the model drug after pre-treatment with the nanoagonist. Image-guided paclitaxel injection after nanoagonist-mediated BBB modulation more efficiently attenuated tumor growth and extended survival than in animal models treated with paclitaxel or temozolomide alone.

Conclusion: Thus, image-guided drug delivery following BBB permeability modulation holds promise to enhance the efficacy of chemotherapeutics to glioma.

Meningioma assessment: Kinetic parameters in dynamic contrast-enhanced MRI appear independent from microvascular anatomy and VEGF expression

BACKGROUND AND PURPOSE

Kinetic parameters of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are considered to be influenced by microvessel environment. This study was performed to explore the extent of this association for meningiomas.

MATERIALS AND METHODS

DCE-MRI kinetic parameters (contrast agent transfer constants K^trans and k_ep, volume fractions v_p and v_e) were determined in pre-operative 3T MRI of meningioma patients for later biopsy sites (19 patients; 15 WHO I^o, no previous radiation, and 4 WHO III^o pre-radiated recurrent tumors). Sixty-three navigated biopsies were consecutively retrieved. Biopsies were immunohistochemically investigated with endothelial marker CD34 and VEGF antibodies, stratified in a total of 4383 analysis units and computationally assessed for VEGF expression and vascular parameters (vessel density, vessel quantity, vascular fraction within tissue [vascular area ratio], vessel wall thickness). Derivability of kinetic parameters from VEGF expression or microvascularization was determined by mixed linear regression analysis. Tissue kinetic and microvascular parameters were tested for their capacity to identify the radiation status in a subanalysis.

RESULTS

Kinetic parameters were neither significantly related to the corresponding microvascular parameters nor to tissue VEGF expression. There was no significant association between microvessel density and its presumed correlate v_p (P=0.07). The subgroup analysis of high-grade radiated meningiomas showed a significantly reduced microvascular density (AUC 0.91; P<0.0001) and smaller total vascular fraction (AUC 0.73; P=0.01).

CONCLUSIONS

In meningioma, DCE-MRI kinetic parameters neither allow for a reliable prediction of tumor microvascularization, nor for a prediction of VEGF expression. Kinetic parameters seem to be determined from different independent factors.

Linearization improves the repeatability of quantitative dynamic contrast-enhanced MRI

PURPOSE

The purpose of this study was to compare the repeatabilities of the linear and nonlinear Tofts and reference region models (RRM) for dynamic contrast-enhanced MRI (DCE-MRI).

MATERIALS AND METHODS

Simulated and experimental DCE-MRI data from 12 rats with a flank tumor of C6 glioma acquired over three consecutive days were analyzed using four quantitative and semi-quantitative DCE-MRI metrics. The quantitative methods used were: 1) linear Tofts model (LTM), 2) non-linear Tofts model (NTM), 3) linear RRM (LRRM), and 4) non-linear RRM (NRRM). The following semi-quantitative metrics were used: 1) maximum enhancement ratio (MER), 2) time to peak (TTP), 3) initial area under the curve (iauc64), and 4) slope. LTM and NTM were used to estimate K^trans, while LRRM and NRRM were used to estimate K^trans relative to muscle (R^Ktrans). Repeatability was assessed by calculating the within-subject coefficient of variation (wSCV) and the percent intra-subject variation (iSV) determined with the Gage R&R analysis.

RESULTS

The iSV for R^Ktrans using LRRM was two-fold lower compared to NRRM at all simulated and experimental conditions. A similar trend was observed for the Tofts model, where LTM was at least 50% more repeatable than the NTM under all experimental and simulated conditions. The semi-quantitative metrics iauc64 and MER were as equally repeatable as K^trans and R^Ktrans estimated by LTM and LRRM respectively. The iSV for iauc64 and MER were significantly lower than the iSV for slope and TTP.

CONCLUSION

In simulations and experimental results, linearization improves the repeatability of quantitative DCE-MRI by at least 30%, making it as repeatable as semi-quantitative metrics.

Semi-parametric arterial input functions for quantitative dynamic contrast enhanced magnetic resonance imaging in mice

OBJECTIVE

An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

METHODS

The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model.

RESULTS

In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust.

CONCLUSION

When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries.

SIGNIFICANCE

This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.

Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI

PURPOSE

Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumor DCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model.

MATERIAL AND METHODS

74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux K^trans and k_ep and, their ratio, v_e, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction v_p] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)].

RESULTS

Arterial AIF more often led to implausible v_e >100% values (p<0.0001). AIF source alteration rendered different absolute kinetic parameters (p<0.0001), except for k_ep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08-0.87) and only consistent >0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with v_p derived from an AIF in the SSS (p=0.03; AUC 0.74).

CONCLUSION

The AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumor DCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by v_p) and showed the least number of implausible v_e values.

Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches

PURPOSE

Reference region models (RRMs) can quantify tumor perfusion in dynamic contrast-enhanced MRI without an arterial input function. Inspection of the RRM reveals that one of the free parameters in the fit is uniquely linked to the reference region and is common to all voxels. A two-step approach is proposed that takes this constraint into account.

METHODS

Three constrained RRM (CRRM) approaches were devised and evaluated. Simulations were performed to compare their accuracy and precision over a range of noise and temporal resolutions. The CRRM was also applied on a virtual phantom that simulates different perfusion values. In vivo evaluation was performed on data from breast cancer and soft tissue sarcoma.

RESULTS

In simulations, the CRRM consistently improved precision and had better accuracy at low signal-to-noise ratio (SNR). In virtual phantom, the CRRMs were able to fit voxels that had similar kinetics to the reference tissue, whereas the unconstrained models failed to accurately fit these voxels. In the in vivo data, the constrained approaches produced parameter maps that had less variability and were in better agreement with the Tofts model.

CONCLUSION

These findings indicate that the two-step fitting approach of the CRRM can reduce the variability of perfusion estimates for quantifying perfusion with dynamic contrast-enhanced (DCE) MRI. Magn Reson Med 78:1547-1557, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Assessment of DCE-MRI parameters for brain tumors through implementation of physiologically-based pharmacokinetic model approaches for Gd-DOTA

Dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI) is used for detailed characterization of pathology of lesions sites, such as brain tumors, by quantitative analysis of tracer's data through the use of pharmacokinetic (PK) models. A key component for PK models in DCE-MRI is the estimation of the concentration-time profile of the tracer in a nearby vessel, referred as Arterial Input Function (AIF). The aim of this work was to assess through full body physiologically-based pharmacokinetic (PBPK) model approaches the PK profile of gadoteric acid (Gd-DOTA) and explore potential application for parameter estimation in DCE-MRI based on PBPK-derived AIFs. The PBPK simulations were generated through Simcyp(®) platform and the predicted PK parameters for Gd-DOTA were compared with available clinical data regarding healthy volunteers and renal impairment patients. The assessment of DCE-MRI parameters was implemented by utilizing similar virtual profiles based on gender, age and weight to clinical profiles of patients diagnosed with glioblastoma multiforme. The PBPK-derived AIFs were then used to compute DCE-MRI parameters through the Extended Tofts Model and compared with the corresponding ones derived from image-based AIF computation. The comparison involved: (i) image measured AIF of patients vs AIF of in silico profile, and, (ii) population average AIF vs in silico mean AIFs. The results indicate that PBPK-derived AIFs allowed the estimation of comparable imaging biomarkers with those calculated from typical DCE-MRI image analysis. The incorporation of PBPK models and potential utilization of in silico profiles to real patient data, can provide new perspectives in DCE-MRI parameter estimation and data analysis.

Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (K(trans)) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as K^trans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and v_e (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T₁ were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of K^trans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in K^trans than v_e and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, k_ep (= K^trans/v_e), was less sensitive to AIF variation than K^trans (wCV for unadjusted AIFs: 0.45 for k_epvs. 0.74 for K^trans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than K^trans.

Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues

Measurements of arterial input function (AIF) can have large systematic errors at standard contrast agent doses in dynamic contrast enhanced MRI (DCE-MRI). We compared measured AIFs from low dose (AIFLD) and standard dose (AIFSD) contrast agent injections, as well as the AIF derived from a muscle reference tissue and artery (AIFref). Twenty-two prostate cancer patients underwent DCE-MRI. Data were acquired on a 3T scanner using an mDixon sequence. Gadobenate dimeglumine was injected twice, at doses of 0.015 and 0.085 mmol/kg. Directly measured AIFs were fitted with empirical mathematical models (EMMs) and compared to the AIF derived from a muscle reference tissue (AIFref). EMMs accurately fitted the AIFs. The 1st and 2nd pass peaks were visualized in AIFLD, but not in AIFSD, thus the peak and shape of AIFSD could not be accurately measured directly. The average scaling factor between AIFSD and AIFLD in the washout phase was only 56% of the contrast dose ratio (~6:1). The shape and magnitude of AIFref closely approximated that of AIFLD after empirically determined dose-dependent normalization. This suggests that AIFref may be a good approximation of the local AIF.

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.

Effect of T2* correction on contrast kinetic model analysis using a reference tissue arterial input function at 7 T

OBJECTIVES

We aimed to investigate the effect of T2* correction on estimation of kinetic parameters from T1-weighted dynamic contrast enhanced (DCE) MRI data when a reference-tissue arterial input function (AIF) is used.

MATERIALS AND METHODS

DCE-MRI data were acquired from seven mice with 4T1 mouse mammary tumors using a double gradient echo sequence at 7 T. The AIF was estimated from a region of interest in the muscle. The extended Tofts model was used to estimate pharmacokinetic parameters in the enhancing part of the tumor, with and without T2* correction of the lesion and AIF. The parameters estimated with T2* correction of both the AIF and lesion time-intensity curve were assumed to be the reference standard.

RESULTS

For the whole population, there was significant difference (p < 0.05) in transfer constant (K(trans)) between T2* corrected and not corrected methods, but not in interstitial volume fraction (ve). Individually, no significant differences were found in K(trans) and ve of four and six tumors, respectively, between the T2* corrected and not corrected methods. In contrast, K(trans) was significantly underestimated, if the T2* correction was not used, in other tumors for which the median K(trans) was larger than 0.4 min(-1).

CONCLUSION

T2* effect on tumors with high K(trans) may not be negligible in kinetic model analysis, even if AIF is estimated from reference tissue where the concentration of contrast agent is relatively low.

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.

Simulation study of the effect of golden-angle KWIC with generalized kinetic model analysis on diagnostic accuracy for lesion discrimination

PURPOSE

To quantitatively evaluate temporal blurring of dynamic contrast-enhanced MRI data generated using a k-space weighted image contrast (KWIC) image reconstruction technique with golden-angle view-ordering.

METHODS

K-space data were simulated using golden-angle view-ordering and reconstructed using a KWIC algorithm with a Fibonacci number of views enforced for each annulus in k-space. Temporal blurring was evaluated by comparing pharmacokinetic model parameters estimated from the simulated data with the true values. Diagnostic accuracy was quantified using receiver operator characteristic curves (ROC) and the area under the ROC curves (AUC).

RESULTS

Estimation errors of pharmacokinetic model parameters were dependent on the true curve type and the lesion size. For 10mm benign and malignant lesions, estimated AUC values using the true and estimate AIFs were consistent with the true AUC value. For 5mm benign and 20mm malignant lesions, estimated AUC values using the true and estimated AIFs were 0.906±0.020 and 0.905±0.021, respectively, as compared with the true AUC value of 0.896.

CONCLUSIONS

Although the investigated reconstruction algorithm does impose errors in pharmacokinetic model parameter estimation, they are not expected to significantly impact clinical studies of diagnostic accuracy.

Assessment of tumor necrotic fraction by dynamic contrast-enhanced MRI: a preclinical study of human tumor xenografts with histopathologic correlation

Contrary to the common notion that tumor necrotic regions are non-enhancing after contrast administration, recent evidence has shown that necrotic regions exhibit delayed and slow uptake of gadolinium tracer on dynamic contrast-enhanced MRI (DCE MRI). The purpose of this study is to explore whether the mapping of tumor voxels with delayed and slow enhancement on DCE MRI can be used to derive estimates of tumor necrotic fraction. Patient-derived tumor xenograft lines of seven human cancers were implanted in 26 mice which were subjected to DCE MRI performed using a spoiled gradient recalled sequence. Gadolinium tracer concentration was estimated using the variable flip angle technique. To identify tumor voxels exhibiting delayed and slow uptake of contrast medium, clustering analysis was performed using a k-means clustering algorithm that classified tumor voxels according to their contrast enhancement patterns. Comparison of the percentage of tumor voxels exhibiting delayed and slow enhancement with the tumor necrotic fraction estimated on histology showed a strong correlation (r = 0.962, p < 0.001). The mapping of tumor regions with delayed and slow contrast uptake on DCE MRI correlated strongly with tumor necrotic fraction, and can potentially serve as a non-invasive imaging surrogate for the in vivo assessment of necrotic fraction.

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.

Assessment of myocardial perfusion with MRI using a modified dual bolus method

Quantification of regional myocardial blood flow (rMBF) with first-pass magnetic resonance imaging (FP-MRI) requires two contrast agent injections (dual bolus technique), inducing error in the determined rMBF if the injections differ. We hypothesize that using input and residue curves of the same injection would be more reliable. We aim to introduce and evaluate a novel method to correct the high concentration arterial input function (AIF) for determination of rMBF. Sixteen patients with non-Hodgkin's lymphoma were examined before and after chemotherapy. The input function was solved by correcting initial high concentration AIF using the ratio of low and high contrast AIF areas, normalized by corresponding heart rates (modified dual bolus method). For comparison, the scaled low contrast AIF was used as an input function (dual bolus method). Unidirectional transfer coefficient K(trans) was calculated using both methods. K(trans)-values determined with the dual bolus (0.81 ± 0.32 ml g(-1) min(-1)) and modified dual bolus (0.77 ± 0.42 ml g(-1) min(-1)) methods were in agreement (p = 0.21). Mean K(trans)-values increased from 0.76 ± 0.43 to 0.89 ± 0.55 ml g(-1) min(-1) after chemotherapy (p = 0.17). The modified dual bolus technique agrees with the dual bolus technique (R2 = 0.899) when the low and high contrast injections are similar. However, when this is not the case, the modified dual bolus technique may be more reliable.

High-field small animal magnetic resonance oncology studies

This review focuses on the applications of high magnetic field magnetic resonance imaging (MRI) and spectroscopy (MRS) to cancer studies in small animals. High-field MRI can provide information about tumor physiology, the microenvironment, metabolism, vascularity and cellularity. Such studies are invaluable for understanding tumor growth and proliferation, response to treatment and drug development. The MR techniques reviewed here include (1)H, (31)P, chemical exchange saturation transfer imaging and hyperpolarized (13)C MRS as well as diffusion-weighted, blood oxygen level dependent contrast imaging and dynamic contrast-enhanced MRI. These methods have been proven effective in animal studies and are highly relevant to human clinical studies.

Arterial input function in perfusion MRI: a comprehensive review

Cerebral perfusion, also referred to as cerebral blood flow (CBF), is one of the most important parameters related to brain physiology and function. The technique of dynamic-susceptibility contrast (DSC) MRI is currently the most commonly used MRI method to measure perfusion. It relies on the intravenous injection of a contrast agent and the rapid measurement of the transient signal changes during the passage of the bolus through the brain. Central to quantification of CBF using this technique is the so-called arterial input function (AIF), which describes the contrast agent input to the tissue of interest. Due to its fundamental role, there has been a lot of progress in recent years regarding how and where to measure the AIF, how it influences DSC-MRI quantification, what artefacts one should avoid, and the design of automatic methods to measure the AIF. The AIF is also directly linked to most of the major sources of artefacts in CBF quantification, including partial volume effect, bolus delay and dispersion, peak truncation effects, contrast agent non-linearity, etc. While there have been a number of good review articles on DSC-MRI over the years, these are often comprehensive but, by necessity, with limited in-depth discussion of the various topics covered. This review article covers in greater depth the issues associated with the AIF and their implications for perfusion quantification using DSC-MRI.

Voxelwise comparison of perfusion parameters estimated using dynamic contrast enhanced (DCE) computed tomography and DCE-magnetic resonance imaging in locally advanced cervical cancer

PURPOSE

Dynamic contrast enhanced (DCE) imaging has gained interest as an imaging modality for assessment of tumor characteristics and response to cancer treatment. However, for DCE-magnetic resonance imaging (MRI) tissue contrast enhancement may vary depending on imaging sequence and temporal resolution. The aim of this study is to compare DCE-MRI to DCE-computed tomography (DCE-CT) as the gold standard.

MATERIAL AND METHODS

Thirteen patients with advanced cervical cancer were scanned once prior to chemo-radiation and during chemo-radiation with DCE-CT and -MRI in immediate succession. A total of 22 paired DCE-CT and -MRI scans were acquired for comparison. Kinetic modeling using the extended Tofts model was applied to both image series. Furthermore the similarity of the spatial distribution was evaluated using a Γ analysis. The correlation between the two imaging techniques was evaluated using Pearson's correlation and the parameter means were compared using a Student's t-test (p < 0.05).

RESULTS

A significant positive correlation between DCE-CT and -MRI was found for all kinetic parameters. The results showing the best correlation with the DCE-CT-derived parameters were obtained using a population-based input function for MRI. The median Pearson's correlations were: volume transfer constant K(trans) (r = 0.9), flux rate constant kep (r = 0.77), extracellular volume fraction ve (r = 0.58) and blood plasma volume fraction vp (r = 0.83). All quantitative parameters were found to be significantly different as estimated by DCE-CT and -MRI. The Γ analysis in normalized maps revealed that 45% of the voxels failed to find a voxel with the corresponding value allowing for an uncertainty of 3 mm in position and 3% in value (Γ3,3). By reducing the criteria, the Γ-failure rates were: Γ3,5 (37% failure), Γ3,10 (26% failure) and at Γ3,15 (19% failure).

CONCLUSION

Good to excellent correlations but significant bias was found between DCE-CT and -MRI. Both the Pearson's correlation and the Γ analysis proved that the spatial information was similar when analyzing the two sets of DCE data using the extended Tofts model. Improvement of input function sampling is needed to improve kinetic quantification using DCE-MRI.

A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings

Three dimensional bilateral imaging is the standard for most clinical breast dynamic contrast-enhanced (DCE) MRI protocols. Because of high spatial resolution (sRes) requirement, the typical 1-2 min temporal resolution (tRes) afforded by a conventional full-k-space-sampling gradient echo (GRE) sequence precludes meaningful and accurate pharmacokinetic analysis of DCE time-course data. The commercially available, GRE-based, k-space undersampling and data sharing TWIST (time-resolved angiography with stochastic trajectories) sequence was used in this study to perform DCE-MRI exams on thirty one patients (with 36 suspicious breast lesions) before their biopsies. The TWIST DCE-MRI was immediately followed by a single-frame conventional GRE acquisition. Blinded from each other, three radiologist readers assessed agreements in multiple lesion morphology categories between the last set of TWIST DCE images and the conventional GRE images. Fleiss' κ test was used to evaluate inter-reader agreement. The TWIST DCE time-course data were subjected to quantitative pharmacokinetic analyses. With a four-channel phased-array breast coil, the TWIST sequence produced DCE images with 20 s or less tRes and ~ 1.0×1.0×1.4 mm(3) sRes. There were no significant differences in signal-to-noise (P=.45) and contrast-to-noise (P=.51) ratios between the TWIST and conventional GRE images. The agreements in morphology evaluations between the two image sets were excellent with the intra-reader agreement ranging from 79% for mass margin to 100% for mammographic density and the inter-reader κ value ranging from 0.54 (P<.0001) for lesion size to 1.00 (P<.0001) for background parenchymal enhancement. Quantitative analyses of the DCE time-course data provided higher breast cancer diagnostic accuracy (91% specificity at 100% sensitivity) than the current clinical practice of morphology and qualitative kinetics assessments. The TWIST sequence may be used in clinical settings to acquire high spatiotemporal resolution breast DCE-MRI images for both precise lesion morphology characterization and accurate pharmacokinetic analysis.

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.

The effect of temporal sampling on quantitative pharmacokinetic and three-time-point analysis of breast DCE-MRI

The effects of temporal sampling on the previously published three-time-point (3TP) method are compared with those of a Tofts-Kety model using an arterial input function from the alternating minimization with model (AMM) method. Computer simulations are done to estimate the expected error in both the 3TP and Tofts-Kety models as a function of the temporal sampling rate of the data. The error in the 3TP model parameters remained essentially constant with respect to temporal sampling. The Tofts-Kety model showed a linear increase in parameter error with respect to temporal sampling. Both analysis methods were also applied to 87 clinically acquired breast scans. These scans were downsampled in time by a factor of 2 and 4, and the methods were reapplied. The spatial resolution was held constant throughout this study. At temporal resolutions less than 19.4 s, the Tofts-Kety model outperformed the 3TP model using receiver operating characteristic curve analysis (area under the ROC curve [AUC] of 0.94 compared to 0.91). As the temporal sampling rate decreased, the 3TP model outperformed the Tofts-Kety model (AUC of 0.89 versus 0.85). When the temporal sampling rate of the data was less than 20 s, the Tofts-Kety model with the AMM method had lower parameter error than the 3TP method.

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.

Blind estimation of the arterial input function in dynamic contrast-enhanced MRI using purity maximization

Uncertainty in arterial input function (AIF) estimation is one of the major errors in the quantification of dynamic contrast-enhanced MRI. A blind source separation algorithm was proposed to determine the AIF by selecting the voxel time course with maximum purity, which represents a minimal contamination from partial volume effects. Simulations were performed to assess the partial volume effect on the purity of AIF, the estimation accuracy of the AIF, and the influence of purity on the derived kinetic parameters. In vivo data were acquired from six patients with hypopharyngeal cancer and eight rats with brain tumor. Results showed that in simulation the AIF with the highest purity is closest to the true AIF. In patients, the manually selection had reduced purity, which could lead to underestimations of K(trans) and V(e) and an overestimation of V(p) when compared with those obtained by the proposed blind source separation algorithm. The derived kinetic parameters in the tumor were more susceptible to the changes in purity when compared with those in the muscle. The animal experiment demonstrated good reproducibility in blind source separation-AIF derived parameters. In conclusion, the blind source separation method is feasible and reproducible to identify the voxel with the tracer concentration time course closest to the true AIF.

Single-channel blind estimation of arterial input function and tissue impulse response in DCE-MRI

Multipass dynamic MRI and pharmacokinetic modeling are used to estimate perfusion parameters of leaky capillaries. Curve fitting and nonblind deconvolution are the established methods to derive the perfusion estimates from the observed arterial input function (AIF) and tissue tracer concentration function. These nonblind methods are sensitive to errors in the AIF, measured in some nearby artery or estimated by multichannel blind deconvolution. Here, a single-channel blind deconvolution algorithm is presented, which only uses a single tissue tracer concentration function to estimate the corresponding AIF and tissue impulse response function. That way, many errors affecting these functions are reduced. The validity of the algorithm is supported by simulations and tests on real data from mouse. The corresponding nonblind and multichannel methods are also presented.

Dynamic contrast-enhanced MR imaging findings of bone metastasis in patients with prostate cancer

AIM: To evaluate the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) findings of bone metastasis in prostate cancer patients.

METHODS: Sixteen men with a diagnosis of metastatic prostate cancer to bones were examined with DCE-MRI at 1.5 Tesla. The mean contrast agent concentration vs time curves for bone metastasis and normal bone were calculated and K^trans and ve values were estimated and compared.

RESULTS: An early significant enhancement (wash-out: n = 6, plateau: n = 8 and persistent: n = 2) was detected in all bone metastases (n = 16). Bone metastasis from prostate cancer showed significant enhancement and high K^trans and ve values compared to normal bone which does not enhance in the elderly population. The mean K^trans was 0.101/min and 0.0051/min (P < 0.001), the mean ve was 0.141 and 0.0038 (P < 0.001), for bone metastases and normal bone, respectively.

CONCLUSION: DCE-MRI and its quantitative perfusion parameters may have a role in improving the detection of skeletal metastasis in prostate cancer patients.

Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI

Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T(1) -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrast-enhanced MRI data.

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

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

Use of a reference tissue and blood vessel to measure the arterial input function in DCEMRI

Accurate measurement of the arterial input function is critical for quantitative evaluation of dynamic contrast enhanced magnetic resonance imaging data. Use of the reference tissue method to derive a local arterial input function avoided large errors associated with direct arterial measurements, but relied on literature values for K(trans) and v(e). We demonstrate that accurate values of K(trans) and v(e) in a reference tissue can be measured by comparing contrast media concentration in a reference tissue to plasma concentrations measured directly in a local artery after the 1-2 passes of the contrast media bolus-when plasma concentration is low and can be measured accurately. The values of K(trans) and v(e) calculated for the reference tissue can then be used to derive a more complete arterial input function including the first pass of the contrast bolus. This new approach was demonstrated using dynamic contrast enhanced magnetic resonance imaging data from rodent hind limb. Values obtained for K(trans) and v(e) in muscle, and the shape and amplitude of the derived arterial input function are consistent with published results.

A perspective on vascular disrupting agents that interact with tubulin: preclinical tumor imaging and biological assessment

The tumor microenvironment provides a rich source of potential targets for selective therapeutic intervention with properly designed anticancer agents. Significant physiological differences exist between the microvessels that nourish tumors and those that supply healthy tissue. Selective drug-mediated damage of these tortuous and chaotic microvessels starves a tumor of necessary nutrients and oxygen and eventually leads to massive tumor necrosis. Vascular targeting strategies in oncology are divided into two separate groups: angiogenesis inhibiting agents (AIAs) and vascular disrupting agents (VDAs). The mechanisms of action between these two classes of compounds are profoundly distinct. The AIAs inhibit the actual formation of new vessels, while the VDAs damage and/or destroy existing tumor vasculature. One subset of small-molecule VDAs functions by inhibiting the assembly of tubulin into microtubules, thus causing morphology changes to the endothelial cells lining the tumor vasculature, triggered by a cascade of cell signaling events. Ultimately this results in catastrophic damage to the vessels feeding the tumor. The rapid emergence and subsequent development of the VDA field over the past decade has led to the establishment of a synergistic combination of preclinical state-of-the-art tumor imaging and biological evaluation strategies that are often indicative of future clinical efficacy for a given VDA. This review focuses on an integration of the appropriate biochemical and biological tools necessary to assess (preclinically) new small-molecule, tubulin active VDAs for their potential to be clinically effective anticancer agents.

Reproducibility of the gadolinium concentration measurements and of the fitting parameters of the vascular input function in the superior sagittal sinus in a patient population

It is widely recognised that the measurement of the arterial input function (AIF) is a key issue and a major source of errors in the pharmacokinetic modelling of dynamic, contrast-enhanced magnetic resonance imaging (DCE-MRI) data, and the modality of the AIF determination is still a matter of debate. In this study we addressed the problem of the intrinsic variability of the AIF within the imaged volume of a DCE-MRI scan by systematically investigating the change in the concentration of contrast agent over time and the fit parameters of the derived vascular input function (VIF) obtained from the superior sagittal sinus (SSS) of a patient population that was scanned longitudinally during treatment for high grade glioma. From a total of 82 scanning sessions, we compared the results obtained with three different DCE-MRI protocols and between two different fitting functions. We applied a correction algorithm to the measured concentration-time curves to minimize the effect of the low temporal resolution on the VIF, and investigated the effect of this algorithm on the reproducibility. Finally, where possible, we compared the signal obtained in the SSS to the signal obtained in the middle cerebral artery. We found a good intrapatient reproducibility of both the measured gadolinium concentrations and VIF parameters, and that the variation of the parameters due to slice location within a patient was significantly lower than the intra patient variation. Intrapatient, interscan differences were significantly less marked than inter-patient differences showing a good intraclass correlation coefficient. We did encounter a MRI protocol dependence of the VIF fitting parameters. The correction algorithm significantly improved the reproducibility of the fitting parameters. These results support the idea that the use of a patient specific measured AIF, not necessarily averaged over a large volume, offers a significant benefit with respect to an external AIF or a measured cohort average AIF.

Constrained estimation of the arterial input function for myocardial perfusion cardiovascular magnetic resonance

Accurate quantification of myocardial perfusion remains challenging due to saturation of the arterial input function at high contrast concentrations. A method for estimating the arterial input function directly from tissue curves in the myocardium that avoids these difficulties is presented. In this constrained alternating minimization with model (CAMM) algorithm, a portion of the left ventricular blood pool signal is also used to constrain the estimation process. Extensive computer simulations assessing the accuracy of kinetic parameter estimation were performed. In 5000 noise realizations, the use of the AIF given by the estimation method returned kinetic parameters with mean Ktrans error of -2% and mean kep error of 0.4%. Twenty in vivo resting perfusion datasets were also processed with this method, and pharmacokinetic parameter values derived from the blind AIF were compared with those derived from a dual-bolus measured AIF. For 17 of the 20 datasets, there were no statistically significant differences in Ktrans estimates, and in aggregate the kinetic parameters were not significantly different from the dual-bolus method. The cardiac constrained alternating minimization with model method presented here provides a promising approach to quantifying perfusion of myocardial tissue with a single injection of contrast agent and without a special pulse sequence though further work is needed to validate the approach in a clinical setting.

Toward local arterial input functions in dynamic contrast-enhanced MRI

PURPOSE

To present a method for estimating the local arterial input function (AIF) within a dynamic contrast-enhanced MRI scan, based on the alternating minimization with model (AMM) method.

MATERIALS AND METHODS

This method clusters a subset of data into representative curves, which are then input to the AMM algorithm to return a parameterized AIF and pharmacokinetic parameters. Computer simulations are used to investigate the accuracy with which the AMM is able to estimate the true AIF as a function of the input tissue curves.

RESULTS

Simulations show that a power law relates uncertainty in kinetic parameters and SNR and heterogeneity of the input. Kinetic parameters calculated with the measured AIF are significantly different from those calculated with either a global (P < 0.005) or a local input function (P = 0.0). The use of local AIFs instead of measured AIFs yield mean lesion-averaged parameter changes: K(trans): +24% [+15%, +70%], k(ep): +13% [-36%, +300%]. Globally estimated input functions yield mean lesion-averaged changes: K(trans): +9% [-38%, +65%], k(ep): +13% [-100%, +400%]. The observed improvement in fit quality with local AIFs was found to be significant when additional free parameters were accounted for using the Akaike information criterion.

CONCLUSION

Local AIFs result in significantly different kinetic parameter values. The statistically significant improvement in fit quality suggests that changes in parameter estimates using local AIFs reflect differences in underlying tissue physiology.

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

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

Quantitative analysis of clinical dynamic contrast-enhanced MR imaging for evaluating treatment response in human breast cancer

PURPOSE

To develop a method that combines a fixed-T1, fuzzy c-means (FCM) technique with a reference region (RR) model (T1-FCM method) to estimate pharmacokinetic parameters without measuring the arterial input function or baseline T1, or T1(0), and to demonstrate its feasibility in the assessment of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer by using data from dynamic contrast material-enhanced magnetic resonance (MR) imaging.

MATERIALS AND METHODS

This study was approved by the human investigation committees of the two participating institutions. All patients gave written informed consent. A conventional dual-flip-angle gradient-echo method was used to evaluate the effects of noise and the T1 in the tissue itself on the accuracy of T1 estimation. Both conventional RR and fixed-T1 methods were used to evaluate the effects of noise and preselected T1(0) on the estimation of pharmacokinetic parameters by means of a simulation study. Thirty-three women (age range, 32-66 years; mean age, 45 years) with pathologically proved breast tumors were examined to evaluate the feasibility of using the T1-FCM method as a means of assessing treatment response to NAC. A nonparametric Mann-Whitney U test was used to assess the difference in each of the MR imaging parameters between patients with a major histologic response to treatment and those with a nonmajor histologic response.

RESULTS

With use of the dual-flip-angle method, the accuracy and distribution of T1 estimation are dependent on the T1 in the tissue itself. The T1-FCM method is more accurate than other methods and is relatively insensitive to the effects of noise and incorrect T1(0) selection. Preliminary clinical data revealed a significant difference (P < .01) in the change of the volume transfer constant after two cycles of NAC between the major and nonmajor histologic response groups.

CONCLUSION

Results of the simulation study demonstrate that the T1-FCM method appears to be relatively insensitive to noisy dynamic contrast-enhanced MR imaging data. This method could prove useful in the evaluation of breast cancer therapy.

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.

Comparison of quantitative parameters in cervix cancer measured by dynamic contrast-enhanced MRI and CT

Cervical tumors of 38 cervix cancer patients were scanned by T(1)-weighted dynamic contrast enhanced (DCE) MRI and then by DCE-CT on the same day. Gadodiamide and iohexol were respectively used as the low-molecular-weight contrast agent in DCE-MRI and DCE-CT. Under an extended Tofts model, DCE-MRI data were analyzed using either individual arterial input functions estimated by a multiple reference tissue method or a population arterial input function by Parker et al., whereas DCE-CT data were analyzed using the arterial input function directly measured from the external iliac arteries. The derived quantitative parameters of cervical tumors were compared between DCE-MRI and DCE-CT. When using the individual multiple reference tissue method arterial input functions to analyze the DCE-MRI data, the correlation coefficients between DCE-MRI- and DCE-CT-derived parameters were, respectively, back-flux rate constant (r = 0.80), extravascular extracellular fractional volume (r = 0.73), contrast agent transfer rate (r = 0.62), and blood plasma volume (r = 0.32); when using the Parker population arterial input function, the correlation coefficients were back-flux rate constant (r = 0.79), extravascular extracellular fractional volume (r = 0.77), contrast agent transfer rate (r = 0.63), and blood plasma volume (r = 0.58). Tumor parametric maps derived by DCE-MRI and DCE-CT had very similar morphologies. However, the means of most derived quantitative parameters were significantly different between the two imaging methods. Close correlation of quantitative parameters derived from two independent imaging modalities suggests both are measuring similar tumor physiologic variables.