A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo results

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.

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.

Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model

PURPOSE

Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-vascular cancer therapies. However, there is no consensus on the vascular input function estimation method, which is critical to kinetic modeling and K ^trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system.

PROCEDURES

In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel's best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-vascular drug treatment.

RESULTS

Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo, significant reductions in K ^trans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded K ^trans consistent with published data in xenograft models.

CONCLUSION

The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield K ^trans results consistent with literature values and suitable for compound studies.

Comparison of arterial input functions measured from ultra-fast dynamic contrast enhanced MRI and dynamic contrast enhanced computed tomography in prostate cancer patients

The purpose of this study was to evaluate the accuracy of arterial input functions (AIFs) measured from dynamic contrast enhanced (DCE) MRI following a low dose of contrast media injection. The AIFs measured from DCE computed tomography (CT) were used as 'gold standard'. A total of twenty patients received CT and MRI scans on the same day. Patients received 120 ml Iohexol in DCE-CT and a low dose of (0.015 mM kg^-1) of gadobenate dimeglumine in DCE-MRI. The AIFs were measured in the iliac artery and normalized to the CT and MRI contrast agent doses. To correct for different temporal resolution and sampling periods of CT and MRI, an empirical mathematical model (EMM) was used to fit the AIFs first. Then numerical AIFs (AIF_CT and AIF_MRI) were calculated based on fitting parameters. The AIF_MRI was convolved with a 'contrast agent injection' function ([Formula: see text]) to correct for the difference between MRI and CT contrast agent injection times (~1.5 s versus 30 s). The results show that the EMMs accurately fitted AIFs measured from CT and MRI. There was no significant difference (p  >  0.05) between the maximum peak amplitude of AIFs from CT (22.1  ±  4.1 mM/dose) and MRI after convolution (22.3  ±  5.2 mM/dose). The shapes of the AIF_CT and [Formula: see text] were very similar. Our results demonstrated that AIFs can be accurately measured by MRI following low dose contrast agent injection.

Improved Evaluation of Antivascular Cancer Therapy Using Constrained Tracer-Kinetic Modeling for Multiagent Dynamic Contrast-Enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a promising technique for assessing the response of tumor vasculature to antivascular therapies. Multiagent DCE-MRI employs a combination of low and high molecular weight contrast agents, which potentially improves the accuracy of estimation of tumor hemodynamic and vascular permeability parameters. In this study, we used multiagent DCE-MRI to assess changes in tumor hemodynamics and vascular permeability after vascular-disrupting therapy. Multiagent DCE-MRI (sequential injection of G5 dendrimer, G2 dendrimer, and Gd-DOTA) was performed in tumor-bearing mice before, 2 and 24 hours after treatment with vascular disrupting agent DMXAA or placebo. Constrained DCE-MRI gamma capillary transit time modeling was used to estimate flow F, blood volume fraction v_b, mean capillary transit time t_c, bolus arrival time t_d, extracellular extravascular fraction v_e, vascular heterogeneity index α^-1 (all identical between agents) and extraction fraction E (reflective of permeability), and transfer constant K^trans (both agent-specific) in perfused pixels. F, v_b, and α^-1 decreased at both time points after DMXAA, whereas t_c increased. E (G2 and G5) showed an initial increase, after which, both parameters restored. K^trans (G2 and Gd-DOTA) decreased at both time points after treatment. In the control, placebo-treated animals, only F, t_c, and K^trans Gd-DOTA showed significant changes. Histologic perfused tumor fraction was significantly lower in DMXAA-treated versus control animals. Our results show how multiagent tracer-kinetic modeling can accurately determine the effects of vascular-disrupting therapy by separating simultaneous changes in tumor hemodynamics and vascular permeability.Significance: These findings describe a new approach to measure separately the effects of antivascular therapy on tumor hemodynamics and vascular permeability, which could help more rapidly and accurately assess the efficacy of experimental therapy of this class. Cancer Res; 78(6); 1561-70. ©2018 AACR.

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.

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.

Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method

PURPOSE

To develop and evaluate a prostate-based method (PBM) for estimating pharmacokinetic parameters on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) by leveraging inherent differences in pharmacokinetic characteristics between the peripheral zone (PZ) and transition zone (TZ).

MATERIALS AND METHODS

This retrospective study, approved by the Institutional Review Board, included 40 patients who underwent a multiparametric 3T MRI examination and subsequent radical prostatectomy. A two-step PBM for estimating pharmacokinetic parameters exploited the inherent differences in pharmacokinetic characteristics associated with the TZ and PZ. First, the reference region model was implemented to estimate ratios of K^trans between normal TZ and PZ. Subsequently, the reference region model was leveraged again to estimate values for K^trans and v_e for every prostate voxel. The parameters of PBM were compared with those estimated using an arterial input function (AIF) derived from the femoral arteries. The ability of the parameters to differentiate prostate cancer (PCa) from benign tissue was evaluated on a voxel and lesion level. Additionally, the effect of temporal downsampling of the DCE MRI data was assessed.

RESULTS

Significant differences (P < 0.05) in PBM K^trans between PCa lesions and benign tissue were found in 26/27 patients with TZ lesions and in 33/38 patients with PZ lesions; significant differences in AIF-based K^trans occurred in 26/27 and 30/38 patients, respectively. The 75^th and 100^th percentiles of K^trans and v_e estimated using PBM positively correlated with lesion size (P < 0.05).

CONCLUSION

Pharmacokinetic parameters estimated via PBM outperformed AIF-based parameters in PCa detection. J. Magn. Reson. Imaging 2016;44:1405-1414.

Synergistic Antivascular and Antitumor Efficacy with Combined Cediranib and SC6889 in Intracranial Mouse Glioma

Prognosis remains extremely poor for malignant glioma. Targeted therapeutic approaches, including single agent anti-angiogenic and proteasome inhibition strategies, have not resulted in sustained anti-glioma clinical efficacy. We tested the anti-glioma efficacy of the anti-angiogenic receptor tyrosine kinase inhibitor cediranib and the novel proteasome inhibitor SC68896, in combination and as single agents. To assess anti-angiogenic effects and evaluate efficacy we employed 4C8 intracranial mouse glioma and a dual-bolus perfusion MRI approach to measure K^trans, relative cerebral blood flow and volume (rCBF, rCBV), and relative mean transit time (rMTT) in combination with anatomical MRI measurements of tumor growth. While single agent cediranib or SC68896 treatment did not alter tumor growth or survival, combined cediranib/SC68896 significantly delayed tumor growth and increased median survival by 2-fold, compared to untreated. This was accompanied by substantially increased tumor necrosis in the cediranib/SC68896 group (p<0.01), not observed with single agent treatments. Mean vessel density was significantly lower, and mean vessel lumen area was significantly higher, for the combined cediranib/SC68896 group versus untreated. Consistent with our previous findings, cediranib alone did not significantly alter mean tumor rCBF, rCBV, rMTT, or K^trans. In contrast, SC68896 reduced rCBF in comparison to untreated, but without concomitant reductions in rCBV, rMTT, or K^trans. Importantly, combined cediranib/SC68896 substantially reduced rCBF, rCBV. rMTT, and K^trans. A novel analysis of K^trans/rCBV suggests that changes in K^trans with time and/or treatment are related to altered total vascular surface area. The data suggest that combined cediranib/SC68896 induced potent anti-angiogenic effects, resulting in increased vascular efficiency and reduced extravasation, consistent with a process of vascular normalization. The study represents the first demonstration that the combination of cediranib with a proteasome inhibitor substantially increases the anti-angiogenic efficacy produced from either agent alone, and synergistically slows glioma tumor growth and extends survival, suggesting a promising treatment which warrants further investigation.

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.

A novel approach to tracer-kinetic modeling for (macromolecular) dynamic contrast-enhanced MRI

PURPOSE

To develop a novel tracer-kinetic modeling approach for multi-agent dynamic contrast-enhanced MRI (DCE-MRI) that facilitates separate estimation of parameters characterizing blood flow and microvascular permeability within one individual.

METHODS

Monte Carlo simulations were performed to investigate the performance of the constrained multi-agent model. Subsequently, multi-agent DCE-MRI was performed on tumor-bearing mice (n = 5) on a 7T Bruker scanner on three measurement days, in which two dendrimer-based contrast agents having high and intermediate molecular weight, respectively, along with gadoterate meglumine, were sequentially injected within one imaging session. Multi-agent data were simultaneously fit with the gamma capillary transit time model. Blood flow, mean capillary transit time, and bolus arrival time were constrained to be identical between the boluses, while extraction fractions and washout rate constants were separately determined for each agent.

RESULTS

Simulations showed that constrained multi-agent model regressions led to less uncertainty and bias in estimated tracer-kinetic parameters compared with single-bolus modeling. The approach was successfully applied in vivo, and significant differences in the extraction fraction and washout rate constant between the agents, dependent on their molecular weight, were consistently observed.

CONCLUSION

A novel multi-agent tracer-kinetic modeling approach that enforces self-consistency of model parameters and can robustly characterize tumor vascular status was demonstrated.

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.

Preoperative dynamic contrast-enhanced MRI correlates with molecular markers of hypoxia and vascularity in specific areas of intratumoral microenvironment and is predictive of patient outcome

BACKGROUND

Measures of tumor vascularity and hypoxia have been correlated with glioma grade and outcome. Dynamic contrast-enhanced (DCE) MRI can noninvasively map tumor blood flow, vascularity, and permeability. In this prospective observational cohort pilot study, preoperative imaging was correlated with molecular markers of hypoxia, vascularity, proliferation, and progression-free and overall patient survival.

METHODS

Pharmacokinetic modeling methods were used to generate maps of tumor blood flow, extraction fraction, permeability-surface area product, transfer constant, washout rate, interstitial volume, blood volume, capillary transit time, and capillary heterogeneity from preoperative DCE-MRI data in human glioma patients. Tissue was obtained from areas of peritumoral edema, active tumor, hypoxic penumbra, and necrotic core and evaluated for vascularity, proliferation, and expression of hypoxia-regulated molecules. DCE-MRI parameter values were correlated with hypoxia-regulated protein expression at tissue sample sites.

RESULTS

Patient survival correlated with DCE parameters in 2 cases: capillary heterogeneity in active tumor and interstitial volume in areas of peritumoral edema. Statistically significant correlations were observed between several DCE parameters and tissue markers. In addition, MIB-1 index was predictive of overall survival (P = .044) and correlated with vascular endothelial growth factor expression in hypoxic penumbra (r = 0.7933, P = .0071) and peritumoral edema (r = 0.4546). Increased microvessel density correlated with worse patient outcome (P = .026).

CONCLUSIONS

Our findings suggest that DCE-MRI may facilitate noninvasive preoperative predictions of areas of tumor with increased hypoxia and proliferation. Both imaging and hypoxia biomarkers are predictive of patient outcome. This has the potential to allow unprecedented prognostic decisions and to guide therapies to specific tumor areas.

Quinacrine synergistically enhances the antivascular and antitumor efficacy of cediranib in intracranial mouse glioma

BACKGROUND

Despite malignant glioma vascularity, anti-angiogenic therapy is largely ineffective. We hypothesize that efficacy of the antiangiogenic agent cediranib is synergistically enhanced in intracranial glioma via combination with the late-stage autophagy inhibitor quinacrine.

METHODS

Relative cerebral blood flow and volume (rCBF, rCBV), vascular permeability (K(trans)), and tumor volume were assessed in intracranial 4C8 mouse glioma using a dual-bolus perfusion MRI approach. Tumor necrosis and tumor mean vessel density (MVD) were assessed immunohistologically. Autophagic vacuole accumulation and apoptosis were assessed via Western blot in 4C8 glioma in vitro.

RESULTS

Cediranib or quinacrine treatment alone did not alter tumor growth. Survival was only marginally improved by cediranib and unchanged by quinacrine. In contrast, combined cediranib/quinacrine reduced tumor growth by >2-fold (P < .05) and increased median survival by >2-fold, compared with untreated controls (P < .05). Cediranib or quinacrine treatment alone did not significantly alter mean tumor rCBF or K(trans) compared with untreated controls, while combined cediranib/quinacrine substantially reduced both (P < .05), indicating potent tumor devascularization. MVD and necrosis were unchanged by cediranib or quinacrine treatment. In contrast, MVD was reduced by nearly 2-fold (P < .01), and necrosis increased by 3-fold (P < .05, one-tailed), in cediranib + quinacrine treated vs untreated groups. Autophagic vacuole accumulation was induced by cediranib and quinacrine in vitro. Combined cediranib/quinacrine treatment under hypoxic conditions induced further accumulation and apoptosis.

CONCLUSION

Combined cediranib/quinacrine treatment synergistically increased antivascular/antitumor efficacy in intracranial 4C8 mouse glioma, suggesting a promising and facile treatment strategy for malignant glioma. Modulations in the autophagic pathway may play a role in the increased efficacy.

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.

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.

Mapping of low flip angles in magnetic resonance

Errors in the flip angle have to be corrected in many magnetic resonance imaging applications, especially for T1 quantification. However, the existing methods of B1 mapping fail to measure lower values of the flip angle despite the fact that these are extensively used in dynamic acquisition and 3D imaging. In this study, the nonlinearity of the radiofrequency (RF) transmit chain, especially for very low flip angles, is investigated and a simple method is proposed to accurately determine both the gain of the RF transmitter and the B1 field map for low flip angles. The method makes use of the spoiled gradient echo sequence with long repetition time (TR), such as applied in the double-angle method. It uses an image acquired with a flip angle of 90° as a reference image that is robust to B1 inhomogeneity. The ratio of the image at flip angle alpha to the image at a flip angle of 90° enables us to calculate the actual value of alpha. This study was carried out at 1.5 and 4.7 T, showing that the linearity of the RF supply system is highly dependent on the hardware. The method proposed here allows us to measure the flip angle from 1° to 60° with a maximal uncertainty of 10% and to correct T1 maps based on the variable flip angle method.

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.