A new method for imaging perfusion and contrast extraction fraction: input functions derived from reference tissues

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.

Dynamic Contrast-Enhanced (DCE) MRI

The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.

Tumour status prediction by means of carbon-ion beam irradiation: comparison of washout rates between in-beam PET and DCE-MRI in rats

Objective.Tumour response to radiation therapy appears as changes in tumour vascular condition. There are several methods for analysing tumour blood circulatory changes one of which is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but there is no method that can observe the tumour vascular condition and physiological changes at the site of radiation therapy. Positron emission tomography (PET) has been applied for treatment verification in charged particle therapy, which is based on the detection of positron emitters produced through nuclear fragmentation reactions in a patient's body. However, the produced positron emitters are washed out biologically depending on the tumour vascular condition. This means that measuring the biological washout rate may allow evaluation of the tumour radiation response, in a similar manner to DCE-MRI. Therefore, this study compared the washout rates in rats between in-beam PET during12C ion beam irradiation and DCE-MRI.Approach.Different vascular conditions of the tumour model were prepared for six nude rats. The tumour of each nude rat was irradiated by a12C ion beam with simultaneous in-beam PET measurement. In 10-12 h, the DCE-MRI experiment was performed for the same six nude rats. The biological washout rate of the produced positron emitters (k2,1st) and the MRI contrast agent (k2a) were derived using the single tissue compartment model.Main results.A linear correlation was observed betweenk2,1standk2a, and they were inversely related to fractional necrotic volume.Significance.This is the first animal study which confirmed the biological washout rate of in-beam PET correlates closely with tumour vascular condition measured with the MRI contrast agent administrated intravenously.

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.

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach

PURPOSE

To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI.

METHODS

A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy.

RESULT

The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81.

CONCLUSION

This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.

Detection of early changes in the post-radiosurgery vestibular schwannoma microenvironment using multinuclear MRI

Stereotactic radiosurgery (SRS) is an established, effective therapy against vestibular schwannoma (VS). The mechanisms of tumour response are, however, unknown and in this study we sought to evaluate changes in the irradiated VS tumour microenvironment through a multinuclear MRI approach. Five patients with growing sporadic VS underwent a multi-timepoint comprehensive MRI protocol, which included diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE) MRI and a spiral 23Na-MRI acquisition for total sodium concentration (TSC) quantification. Post-treatment voxelwise changes in TSC, DTI metrics and DCE-MRI derived microvascular biomarkers (Ktrans, ve and vp) were evaluated and compared against pre-treatment values. Changes in tumour TSC and microvascular parameters were observable as early as 2 weeks post-treatment, preceding changes in structural imaging. At 6 months post-treatment there were significant voxelwise increases in tumour TSC (p < 0.001) and mean diffusivity (p < 0.001, repeated-measures ANOVA) with marked decreases in tumour microvascular parameters (p < 0.001, repeated-measures ANOVA). This study presents the first in vivo evaluation of alterations in the VS tumour microenvironment following SRS, demonstrating that changes in tumour sodium homeostasis and microvascular parameters can be imaged as early as 2 weeks following treatment. Future studies should seek to investigate these clinically relevant MRI metrics as early biomarkers of SRS response.

Improving Tumor Hypoxia Location in 18F-Misonidazole PET with Dynamic Contrast-enhanced MRI Using Quantitative Electron Paramagnetic Resonance Partial Oxygen Pressure Images

Purpose

To enhance the spatial accuracy of fluorine 18 (18F) misonidazole (MISO) PET imaging of hypoxia by using dynamic contrast-enhanced (DCE) MR images as a basis for modifying PET images and by using electron paramagnetic resonance (EPR) partial oxygen pressure (pO2) as the reference standard.

Materials and Methods

Mice (n = 10) with leg-borne MCa4 mammary carcinomas underwent EPR imaging, T2-weighted and DCE MRI, and 18F-MISO PET/CT. Images were registered to the same space for analysis. The thresholds of hypoxia for PET and EPR images were tumor-to-muscle ratios greater than or equal to 2.2 mm Hg and less than or equal to 14 mm Hg, respectively. The Dice similarity coefficient (DSC) and Hausdorff distance (d H ) were used to quantify the three-dimensional overlap of hypoxia between pO2 EPR and 18F-MISO PET images. A training subset (n = 6) was used to calculate optimal DCE MRI weighting coefficients to relate EPR to the PET signal; the group average weights were then applied to all tumors (from six training mice and four test mice). The DSC and d H were calculated before and after DCE MRI-corrected PET images were obtained to quantify the improvement in overlap with EPR pO2 images for measuring tumor hypoxia.

Results

The means and standard deviations of the DSC and d H between hypoxic regions in original PET and EPR images were 0.35 mm ± 0.23 and 5.70 mm ± 1.7, respectively, for images of all 10 mice. After implementing a preliminary DCE MRI correction to PET data, the DSC increased to 0.86 mm ± 0.18 and the d H decreased to 2.29 mm ± 0.70, showing significant improvement (P < .001) for images of all 10 mice. Specifically, for images of the four independent test mice, the DSC improved with correction from 0.19 ± 0.28 to 0.80 ± 0.29 (P = .02), and the d H improved from 6.40 mm ± 2.5 to 1.95 mm ± 0.63 (P = .01).

Conclusion

Using EPR information as a reference standard, DCE MRI information can be used to correct 18F-MISO PET information to more accurately reflect areas of hypoxia.Keywords: Animal Studies, Molecular Imaging, Molecular Imaging-Cancer, PET/CT, MR-Dynamic Contrast Enhanced, MR-Imaging, PET/MR, Breast, Oncology, Tumor Mircoenvironment, Electron Paramagnetic ResonanceSupplemental material is available for this article.© RSNA, 2021.

Comparison of DCE-MRI of murine model cancers with a low dose and high dose of contrast agent

There are increasing concerns regarding intracellular accumulation of gadolinium (Gd) after multiple dynamic contrast enhanced (DCE) MRI scans. We investigated whether a low dose (LD) of Gd-based contrast agent is as effective as a high dose (HD) for quantitative analysis of DCE-MRI data, and evaluated the use of a split dose protocol to obtain new diagnostic parameters. Female C3H mice (n = 6) were injected with mammary carcinoma cells in the hind leg. MRI experiments were performed on 9.4 T scanner. DCE-MRI data were acquired with 1.5 s temporal resolution before and after a LD (0.04 mmol/kg), then again after 30 min followed by a HD (0.2 mmol/kg) bolus injection of Omniscan. The standard Tofts model was used to extract physiological parameters (K^trans and v_e) with the arterial input function derived from muscle reference tissue. In addition, an empirical mathematical model was used to characterize maximum contrast agent uptake (A), contrast agent uptake rate (α) and washout rate (β and γ). There were moderate to strong correlations (r = 0.69-0.97, p < 0001) for parameters K^trans, v_e, A, α and β from LD versus HD data. On average, tumor parameters obtained from LD data were significantly larger (p < 0.05) than those from HD data. The parameter ratios, K^trans, v_e, A and α calculated from the LD data divided by the HD data, were all significantly larger than 1.0 (p < 0.003) for tumor. T₂* changes following contrast agent injection affected parameters calculated from HD data, but this was not the case for LD data. The results suggest that quantitative analysis of LD data may be at least as effective for cancer characterization as quantitative analysis of HD data. In addition, the combination of parameters from two different doses may provide useful diagnostic information.

Microbubble formulation influences inflammatory response to focused ultrasound exposure in the brain

Focused ultrasound and microbubble (FUS + MB)-mediated blood-brain barrier (BBB) permeability enhancement can facilitate targeted brain-drug delivery. While controlling the magnitude of BBB permeability enhancement is necessary to limit tissue damage, little work has attempted to decouple these concepts. This work investigated the relationship between BBB permeability enhancement and the relative transcription of inflammatory mediators 4 h following sonication. Three microbubble formulations, Definity, BG8774, and MSB4, were compared, with the dose of each formulation normalized to gas volume. While changes in the transcription of key proinflammatory mediators, such as Il1b, Ccl2, and Tnf, were correlated to the magnitude of BBB permeability enhancement, these correlations were not independent of microbubble formulation; microbubble size distribution may play an important role, as linear regression analyses of BBB permeability magnitude versus differential gene expression for these proinflammatory mediators revealed significantly greater slopes for MSB4, a monodisperse microbubble with mean diameter of 4 μm, compared to Definity or BG8774, both polydisperse microbubbles with mean diameters below 2 μm. Additionally, the function of an acoustic feedback control algorithm, based on the detection threshold of ultraharmonic emissions, was assessed. While this control strategy was effective in limiting both wideband emissions and red blood cell extravasation, microbubble formulation was found to influence the magnitude of BBB leakage and correlations to acoustic emissions. This work demonstrates that while the initial magnitude of FUS + MB-mediated BBB permeability enhancement has a clear influence on the subsequent inflammatory responses, microbubble characteristics influence these relationships and must also be considered.

Evaluation of chronic lead effects in the blood brain barrier system by DCE-CT

BACKGROUND

Lead (Pb) is an environmental factor has been suspected of contributing to the dementia including Alzheimer's disease (AD). Our previous studies have shown that Pb exposure at the subtoxic dose increased brain levels of beta-amyloid (Aβ) and amyloid plaques, a pathological hallmark for AD, in amyloid precursor protein (APP) transgenic mice, and is hypothesized to inhibit Aβ clearance in the blood- cerebrospinal fluid (CSF) barrier. However, it remains unclear how different levels of Pb affect Aβ clearance in the whole blood-brain barrier system. This study was designed to investigate whether chronic exposure of Pb affected the permeability of the blood-brain barrier system by using the Dynamic Contrast-Enhanced Computerized Tomography (DCE-CT) method.

METHODS

DEC-CT was used to investigate whether chronic exposure of toxic Pb affected the permeability of the real-time blood brain barrier system.

RESULTS

Data showed that Pb exposure increased permeability surface area product, and also significantly induced brain perfusion. However, Pb exposure did not alter extracellular volumes or fractional blood volumes of mouse brain.

CONCLUSION

Our data suggest that Pb exposure at subtoxic and toxic levels directly targets the brain vasculature and damages the blood brain barrier system.

Test-Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts

Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 μL in volume, with region of interest–averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 μm²/ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test–retest coefficient-of-variation (CV_WS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CV_WS. Determination of T2 is also robust with a ∼3% CV_WS. Measurements of T1 and ADC are less robust with CV_WS values in the 6%–11% range. Preliminary DCE test–retest time-course determinations, as quantified by area under the curve and K^trans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%–40% CV_WS.

Assessment of tumor treatment response using active contrast encoding (ACE)-MRI: Comparison with conventional DCE-MRI

Purpose

To investigate the validity of contrast kinetic parameter estimates from Active Contrast Encoding (ACE)-MRI against those from conventional Dynamic Contrast-Enhanced (DCE)-MRI for evaluation of tumor treatment response in mouse tumor models.

Methods

The ACE-MRI method that incorporates measurement of T₁ and B₁ into the enhancement curve washout region, was implemented on a 7T MRI scanner to measure tracer kinetic model parameters of 4T1 and GL261 tumors with treatment using bevacizumab and 5FU. A portion of the same ACE-MRI data was used for conventional DCE-MRI data analysis with a separately measured pre-contrast T₁ map. Tracer kinetic model parameters, such as K^trans (permeability area surface product) and v_e (extracellular space volume fraction), estimated from ACE-MRI were compared with those from DCE-MRI, in terms of correlation and Bland-Altman analyses.

Results

A three-fold increase of the median K^trans by treatment was observed in the flank 4T1 tumors by both ACE-MRI and DCE-MRI. In contrast, the brain tumors did not show a significant change by the treatment in either ACE-MRI or DCE-MRI. K^trans and v_e values of the tumors from ACE-MRI were strongly correlated with those from DCE-MRI methods with correlation coefficients of 0.92 and 0.78, respectively, for the median values of 17 tumors. The Bland-Altman plot analysis showed a mean difference of -0.01 min^-1 for K^trans with the 95% limits of agreement of -0.12 min^-1 to 0.09 min^-1, and -0.05 with -0.37 to 0.26 for v_e.

Conclusion

The tracer kinetic model parameters estimated from ACE-MRI and their changes by treatment closely matched those of DCE-MRI, which suggests that ACE-MRI can be used in place of conventional DCE-MRI for tumor progression monitoring and treatment response evaluation with a reduced scan time.

Investigating the effects of dexamethasone on blood-brain barrier permeability and inflammatory response following focused ultrasound and microbubble exposure

Rationale: Clinical trials are currently underway to test the safety and efficacy of delivering therapeutic agents across the blood-brain barrier (BBB) using focused ultrasound and microbubbles (FUS+MBs). While acoustic feedback control strategies have largely minimized the risk of overt tissue damage, transient induction of inflammatory processes have been observed following sonication in preclinical studies. The goal of this work was to explore the potential of post-sonication dexamethasone (DEX) administration as a means to mitigate treatment risk. Vascular permeability, inflammatory protein expression, blood vessel growth, and astrocyte activation were assessed. Methods: A single-element focused transducer (transmit frequency = 580 kHz) and Definity^TM microbubbles were used to increase BBB permeability unilaterally in the dorsal hippocampi of adult male rats. Sonicating pressure was calibrated based on ultraharmonic emissions. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was used to quantitatively assess BBB permeability at 15 min (baseline) and 2 hrs following sonication. DEX was administered following baseline imaging and at 24 hrs post-FUS+MB exposure. Expression of key inflammatory proteins were assessed at 2 days, and astrocyte activation and blood vessel growth were assessed at 10 days post-FUS+MB exposure. Results: Compared to saline-treated control animals, DEX administration expedited the restoration of BBB integrity at 2 hrs, and significantly limited the production of key inflammation-related proteins at 2 days, following sonication. Indications of FUS+MB-induced astrocyte activation and vascular growth were diminished at 10 days in DEX-treated animals, compared to controls. Conclusions: These results suggest that DEX provides a means of modulating the duration of BBB permeability enhancement and may reduce the risk of inflammation-induced tissue damage, increasing the safety profile of this drug-delivery strategy. This effect may be especially relevant in scenarios for which the goal of treatment is to restore or preserve neural function and multiple sonications are required.

Preclinical Molecular Imaging for Precision Medicine in Breast Cancer Mouse Models

Precision and personalized medicine is gaining importance in modern clinical medicine, as it aims to improve diagnostic precision and to reduce consequent therapeutic failures. In this regard, prior to use in human trials, animal models can help evaluate novel imaging approaches and therapeutic strategies and can help discover new biomarkers. Breast cancer is the most common malignancy in women worldwide, accounting for 25% of cases of all cancers and is responsible for approximately 500,000 deaths per year. Thus, it is important to identify accurate biomarkers for precise stratification of affected patients and for early detection of responsiveness to the selected therapeutic protocol. This review aims to summarize the latest advancements in preclinical molecular imaging in breast cancer mouse models. Positron emission tomography (PET) imaging remains one of the most common preclinical techniques used to evaluate biomarker expression in vivo, whereas magnetic resonance imaging (MRI), particularly diffusion-weighted (DW) sequences, has been demonstrated as capable of distinguishing responders from nonresponders for both conventional and innovative chemo- and immune-therapies with high sensitivity and in a noninvasive manner. The ability to customize therapies is desirable, as this will enable early detection of diseases and tailoring of treatments to individual patient profiles. Animal models remain irreplaceable in the effort to understand the molecular mechanisms and patterns of oncologic diseases.

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.

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

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate K^trans (volume transfer rate constant), v_e (extravascular, extracellular volume fraction), k_ep (efflux rate constant), and τ_i (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T₁ values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for K^trans, v_e, k_ep, and τ_i, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in K^trans and v_e (wCV = 0.50 and 0.10, respectively), but had smaller effects on k_ep and τ_i (wCV = 0.39 and 0.22, respectively). k_ep is less sensitive to AIF variation than K^trans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τ_i parameter may have advantages over the conventional PK parameters in a longitudinal study.

Applying dynamic contrast enhanced MSOT imaging to intratumoral pharmacokinetic modeling

Examining the dynamics of an agent in the tumor microenvironment can offer critical insights to the influx rate and accumulation of the agent. Intratumoral kinetic characterization in the in vivo setting can further elicudate distribution patterns and tumor microenvironment. Dynamic contrast-enhanced Multispectral Optoacoustic Tomographic imaging (DCE-MSOT) acquires serial MSOT images with the administration of an exogenous contrast agent over time. We tracked the dynamics of a tumor-targeted contrast agent, HypoxiSense 680 (HS680), in breast xenograft mouse models using MSOT. Arterial input function (AIF) approach with MSOT imaging allowed for tracking HS680 dynamics within the mouse. The optoacoustic signal for HS680 was quantified using the ROI function in the ViewMSOT software. A two-compartment pharmacokinetics (PK) model constructed in MATLAB to fit rate parameters. The contrast influx (k_in) and outflux (k_out) rate constants predicted are k_in = 1.96 × 10^-2 s^-1 and k_out = 9.5 × 10^-3 s^-1 (R = 0.9945).

Imaging biomarkers in oncology: Basics and application to MRI

Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called "precision cancer medicine." Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI).

LEVEL OF EVIDENCE

5 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2018;48:13-26.

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.

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.

Assessment of pharmacokinetics for microvessel proliferation by DCE-MRI for early detection of physeal bone bridge formation in an animal model

OBJECTIVES

Bone bridge formation occurs after physeal lesions and can lead to growth arrest if not reversed. Previous investigations on the underlying mechanisms of this formation used histological methods. Therefore, this study aimed to apply a minimally invasive method using dynamic contrast-enhanced MRI (DCE-MRI).

MATERIALS AND METHODS

Changes in functional parameters related to the microvessel system were assessed in a longitudinal study of a cohort of an animal model applying a reference region model. The development of morphology of the injured physis was investigated with 3D high-resolution MRI. To acquire complementary information for MRI-related findings qRT-PCR and immunohistochemical data were acquired for a second cohort of the animal model.

RESULTS

The evaluation of the pharmacokinetic parameters showed a first rise of the transfer coefficient 7 days post-lesion and a maximum 42 days after operation. The analysis of the complementary data showed a connection of the first rise to microvessel proliferation while the maximum value was linked to bone remodeling.

CONCLUSION

The pharmacokinetic analysis of DCE-MRI provides information on a proliferation of microvessels during the healing process as a sign for bone bridge formation. Thereby, DCE-MRI could identify details, which up to now required analyses of highly invasive methods.

Simultaneous measurement of T1 /B1 and pharmacokinetic model parameters using active contrast encoding (ACE)-MRI

The aim of this study was to assess the feasibility of combining dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with the measurement of the radiofrequency (RF) transmit field B₁ and pre-contrast longitudinal relaxation time T₁₀ . A novel approach has been proposed to simultaneously estimate B₁ and T₁₀ from a modified DCE-MRI scan that actively encodes the washout phase of the curve with different amounts of T₁ and B₁ weighting using multiple flip angles and repetition times, hence referred to as active contrast encoding (ACE)-MRI. ACE-MRI aims to simultaneously measure B₁ and T₁₀ , together with contrast kinetic parameters, such as the transfer constant K^trans , interstitial space volume fraction v_e and vascular space volume fraction v_p . The proposed method was tested using numerical simulations and in vivo studies with mouse models of breast cancer implanted in the flank and mammary fat pad, and glioma in the brain. In the numerical simulation study with a signal-to-noise ratio of 10, both B₁ and T₁₀ were estimated accurately with errors of 5.1 ± 3.5% and 12.3 ± 8.8% and coefficients of variation (CV) of 14.9 ± 8.6% and 15.0 ± 5.0%, respectively. Using the same ACE-MRI data, the kinetic parameters K^trans , v_e and v_p were also estimated with errors of 14.2 ± 8.3% (CV = 13.5 ± 4.6%), 14.7 ± 9.9% (CV = 13.3 ± 4.5%) and 14.0 ± 9.3% (CV = 14.0 ± 4.5%), respectively. For the in vivo tumor data from 11 mice, voxel-wise comparisons between ACE-MRI and DCE-MRI methods showed that the mean differences for the five parameters were as follows: ΔK^trans  = 0.006 (/min), Δv_e  = 0.016, Δv_p  = 0.000, ΔB₁  = -0.014 and ΔT₁  = -0.085 (s), which suggests a good agreement between the two methods. When compared with separately measured B₁ and T₁₀ , and DCE-MRI estimated kinetic parameters as a reference, the mean relative errors of ACE-MRI estimation were B₁  = -0.3%, T₁₀  = -8.5%, K^trans  = 11.4%, v_e  = 14.5% and v_p  = 4.5%. This proof-of-concept study demonstrates that the proposed ACE-MRI method can be used to estimate B₁ and T₁₀ , together with contrast kinetic model parameters.

Propagation of error from parameter constraints in quantitative MRI: Example application of multiple spin echo T2 mapping

PURPOSE

Quantitative MRI may require correcting for nuisance parameters which can or must be constrained to independently measured or assumed values. The noise and/or bias in these constraints propagate to fitted parameters. For example, the case of refocusing pulse flip angle constraint in multiple spin echo T₂ mapping is explored.

METHODS

An analytical expression for the mean-squared error of a parameter of interest was derived as a function of the accuracy and precision of an independent estimate of a nuisance parameter. The expression was validated by simulations and then used to evaluate the effects of flip angle (θ) constraint on the accuracy and precision of T⁁2 for a variety of multi-echo T₂ mapping protocols.

RESULTS

Constraining θ improved T⁁2 precision when the θ-map signal-to-noise ratio was greater than approximately one-half that of the first spin echo image. For many practical scenarios, constrained fitting was calculated to reduce not just the variance but the full mean-squared error of T⁁2, for bias in θ⁁≲6%.

CONCLUSION

The analytical expression derived in this work can be applied to inform experimental design in quantitative MRI. The example application to T₂ mapping provided specific cases, depending on θ⁁ accuracy and precision, in which θ⁁ measurement and constraint would be beneficial to T⁁2 variance or mean-squared error. Magn Reson Med 79:673-682, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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.

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.

A longitudinal study of placental perfusion using dynamic contrast enhanced magnetic resonance imaging in murine pregnancy

INTRODUCTION

To evaluate changes in placental perfusion with advancing gestation in normal murine pregnancy using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

METHODS

Seven timed-pregnant CD-1 mice underwent DCE-MRI scanning longitudinally on gestational days (GD) 13, 15 and 17. Placentas were segmented into high (HPZ) and low perfusion zones (LPZ) using tissue similarity mapping. Blood perfusion of the respective regions and the whole placenta was quantified using the steepest slope method. The diameter of the maternal central canal (CC) was also measured.

RESULTS

An increase in perfusion was observed between GD13 and GD17 in the overall placenta (p = 0.04) and in the HPZ (p = 0.02). Although perfusion in the LPZ showed a slight increasing trend, it was not significant (p = 0.07). Perfusion, in units of ml/min/100 ml, in the overall placenta and the HPZ was respectively 61.2 ± 31.2 and 106.2 ± 56.3 at GD13 (n = 19 placentas); 90.3 ± 43.7 and 139 ± 55.4 at GD15 (n = 20); and 104.9 ± 76.1 and 172.2 ± 85.6 at GD17 (n = 14). The size of the CC increased with advancing gestation (p < 0.05).

DISCUSSION

Using longitudinal DCE-MRI, the gestational age-dependent perfusion change in the normal murine placenta and in its regional compartments was quantified. In mid and late gestations, placental constituent regions differ significantly in their perfusion rates. The CC diameter also showed increase with advancing gestation, which may be playing an important role toward the gestational age-dependent increase in placental perfusion.

Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?

OBJECTIVE

To evaluate the performance of six models of population arterial input function (AIF) in the setting of primary breast cancer and neoadjuvant chemotherapy (NAC). The ability to fit patient dynamic contrast-enhanced MRI (DCE-MRI) data, provide physiological plausible data and detect pathological response was assessed.

METHODS

Quantitative DCE-MRI parameters were calculated for 27 patients at baseline and after 2 cycles of NAC for 6 AIFs. Pathological complete response detection was compared with change in these parameters from a reproduction cohort of 12 patients using the Bland-Altman approach and receiver-operating characteristic analysis.

RESULTS

There were fewer fit failures pre-NAC for all models, with the modified Fritz-Hansen having the fewest pre-NAC (3.6%) and post-NAC (18.8%), contrasting with the femoral artery AIF (19.4% and 43.3%, respectively). Median transfer constant values were greatest for the Weinmann function and also showed greatest reductions with treatment (-68%). Reproducibility (r) was the lowest for the Weinmann function (r = -49.7%), with other AIFs ranging from r = -27.8 to -39.2%.

CONCLUSION

Using the best performing AIF is essential to maximize the utility of quantitative DCE-MRI parameters in predicting response to NAC treatment. Applying our criteria, the modified Fritz-Hansen and cosine bolus approximated Parker AIF models performed best. The Fritz-Hansen and biexponential approximated Parker AIFs performed less well, and the Weinmann and femoral artery AIFs are not recommended.

ADVANCES IN KNOWLEDGE

We demonstrate that using the most appropriate AIF can aid successful prediction of response to NAC in breast cancer.

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.

Techniques and applications of dynamic contrast enhanced magnetic resonance imaging in cancer

We first discuss several key technical issues associated with quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), and then provide examples of DCE-MRI in oncology. In particular, we examine the importance of both active and passive delivery of the contrast agent to the tissue under investigation, and repeatability/reproducibility in DCE-MRI studies. We then discuss examples of how DCE-MRI can assist in assessing and predicting therapeutic response in the neoadjuvant setting.

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.

1.5-T multiparametric MRI using PI-RADS: a region by region analysis to localize the index-tumor of prostate cancer in patients undergoing prostatectomy

BACKGROUND

The use of multiparametric magnetic resonance imaging (mpMRI) to detect and localize prostate cancer has increased in recent years. In 2010, the European Society of Urogenital Radiology (ESUR) published guidelines for mpMRI and introduced the Prostate Imaging Reporting and Data System (PI-RADS) for scoring the different parameters.

PURPOSE

To evaluate the reliability and diagnostic performance of endorectal 1.5-T mpMRI using the PI-RADS to localize the index tumor of prostate cancer in patients undergoing prostatectomy.

MATERIAL AND METHODS

This institutional review board IRB-approved, retrospective study included 63 patients (mean age, 60.7 years, median PSA, 8.0). Three observers read mpMRI parameters (T2W, DWI, and DCE) using the PI-RADS, which were compared with the results from whole-mount histopathology that analyzed 27 regions of interest. Inter-observer agreement was calculated as well as sensitivity, specificity, positive predictive value (PPV), and negative predicted value (NPV) by dichotomizing the PI-RADS criteria scores ≥3. A receiver-operating curve (ROC) analysis was performed for the different MR parameters and overall score.

RESULTS

Inter-observer agreement on the overall score was 0.41. The overall score in the peripheral zone achieved sensitivities of 0.41, 0.60, and 0.55 with an NPV of 0.80, 0.84, and 0.83, and in the transitional zone, sensitivities of 0.26, 0.15, and 0.19 with an NPV of 0.92, 0.91, and 0.92 for Observers 1, 2, and 3, respectively. The ROC analysis showed a significantly increased area under the curve (AUC) for the overall score when compared to T2W alone for two of the three observers.

CONCLUSION

1.5 T mpMRI using the PI-RADS to localize the index tumor achieved moderate reliability and diagnostic performance.

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.

Dynamic contrast-enhanced magnetic resonance imaging: fundamentals and application to the evaluation of the peripheral perfusion

INTRODUCTION

The ability to ascertain information pertaining to peripheral perfusion through the analysis of tissues' temporal reaction to the inflow of contrast agent (CA) was first recognized in the early 1990's. Similar to other functional magnetic resonance imaging (MRI) techniques such as arterial spin labeling (ASL) and blood oxygen level-dependent (BOLD) MRI, dynamic contrast-enhanced MRI (DCE-MRI) was at first restricted to studies of the brain. Over the last two decades the spectrum of ailments, which have been studied with DCE-MRI, has been extensively broadened and has come to include pathologies of the heart notably infarction, stroke and further cerebral afflictions, a wide range of neoplasms with an emphasis on antiangiogenic treatment and early detection, as well as investigations of the peripheral vascular and musculoskeletal systems.

APPLICATIONS TO PERIPHERAL PERFUSION

DCE-MRI possesses an unparalleled capacity to quantitatively measure not only perfusion but also other diverse microvascular parameters such as vessel permeability and fluid volume fractions. More over the method is capable of not only assessing blood flowing through an organ, but in contrast to other noninvasive methods, the actual tissue perfusion. These unique features have recently found growing application in the study of the peripheral vascular system and most notably in the diagnosis and treatment of peripheral arterial occlusive disease (PAOD).

REVIEW OUTLINE

The first part of this review will elucidate the fundamentals of data acquisition and interpretation of DCE-MRI, two areas that often remain baffling to the clinical and investigating physician because of their complexity. The second part will discuss developments and exciting perspectives of DCE-MRI regarding the assessment of perfusion in the extremities. Emerging clinical applications of DCE-MRI will be reviewed with a special focus on investigation of physiology and pathophysiology of the microvascular and vascular systems of the extremities.

Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and tissue volume fractions, and is frequently employed in both preclinical and clinical investigations. However, the experimental and analytical subtleties of the technique are not frequently discussed in the literature, nor are its relationships to other commonly used quantitative imaging techniques. This review aims to provide practical information on the development, implementation, and validation of a DCE-MRI study in the context of a preclinical study (though we do frequently refer to clinical studies that are related to these topics).

Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications

A review of the selection of models in dynamic contrast-enhanced MRI (DCE-MRI) is conducted, with emphasis on the balance between the bias and variance required to produce stable and accurate estimates of vascular parameters. The vascular parameters considered as a first-order model are the forward volume transfer constant K(trans) , the plasma volume fraction vp and the interstitial volume fraction ve . To illustrate the critical issues in model selection, a data-driven selection of models in an animal model of cerebral glioma is followed. Systematic errors and extended models are considered. Studies with nested and non-nested pharmacokinetic models are reviewed; models considering water exchange are considered.

Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T

BACKGROUND

A challenge in the diagnosis of prostate cancer (PCa) is the accurate assessment of aggressiveness.

OBJECTIVE

To validate the performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the prostate at 3 tesla (T) for the assessment of PCa aggressiveness, with prostatectomy specimens as the reference standard.

DESIGN, SETTINGS, AND PARTICIPANTS

A total of 45 patients with PCa scheduled for prostatectomy were included. This study was approved by the institutional review board; the need for informed consent was waived.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Subjects underwent a clinical MRI protocol including DCE-MRI. Blinded to DCE-images, PCa was indicated on T2-weighted images based on histopathology results from prostatectomy specimens with the use of anatomical landmarks for the precise localization of the tumor. PCa was classified as low-, intermediate-, or high-grade, according to Gleason score. DCE-images were used as an overlay on T2-weighted images; mean and quartile values from semi-quantitative and pharmacokinetic model parameters were extracted per tumor region. Statistical analysis included Spearman's ρ, the Kruskal-Wallis test, and a receiver operating characteristics (ROC) analysis.

RESULTS AND LIMITATIONS

Significant differences were seen for the mean and 75th percentile (p75) values of wash-in (p = 0.024 and p = 0.017, respectively), mean wash-out (p = 0.044), and p75 of transfer constant (K(trans)) (p = 0.035), all between low-grade and high-grade PCa in the peripheral zone. ROC analysis revealed the best discriminating performance between low-grade versus intermediate-grade plus high-grade PCa in the peripheral zone for p75 of wash-in, K(trans), and rate constant (Kep) (area under the curve: 0.72). Due to a limited number of tumors in the transition zone, a definitive conclusion for this region of the prostate could not be drawn.

CONCLUSIONS

Quantitative parameters (K(trans) and Kep) and semi-quantitative parameters (wash-in and wash-out) derived from DCE-MRI at 3 T have the potential to assess the aggressiveness of PCa in the peripheral zone. P75 of wash-in, K(trans), and Kep offer the best possibility to discriminate low-grade from intermediate-grade plus high-grade PCa.

A reference agent model for DCE MRI can be used to quantify the relative vascular permeability of two MRI contrast agents

Dynamic Contrast Enhancement (DCE) MRI has been used to measure the kinetic transport constant, K(trans), which is used to assess tumor angiogenesis and the effects of anti-angiogenic therapies. Standard DCE MRI methods must measure the pharmacokinetics of a contrast agent in the blood stream, known as the Arterial Input Function (AIF), which is then used as a reference for the pharmacokinetics of the agent in tumor tissue. However, the AIF is difficult to measure in pre-clinical tumor models and in patients. Moreover the AIF is dependent on the Fahraeus effect that causes a highly variable hematocrit (Hct) in tumor microvasculature, leading to erroneous estimates of K(trans). To overcome these problems, we have developed the Reference Agent Model (RAM) for DCE MRI analyses, which determines the relative K(trans) of two contrast agents that are simultaneously co-injected and detected in the same tissue during a single DCE-MRI session. The RAM obviates the need to monitor the AIF because one contrast agent effectively serves as an internal reference in the tumor tissue for the other agent, and it also eliminates the systematic errors in the estimated K(trans) caused by assuming an erroneous Hct. Simulations demonstrated that the RAM can accurately and precisely estimate the relative K(trans) (R(Ktrans)) of two agents. To experimentally evaluate the utility of RAM for analyzing DCE MRI results, we optimized a previously reported multiecho (19)F MRI method to detect two perfluorinated contrast agents that were co-injected during a single in vivo study and selectively detected in the same tumor location. The results demonstrated that RAM determined R(Ktrans) with excellent accuracy and precision.

Calculation of intravascular signal in dynamic contrast enhanced-MRI using adaptive complex independent component analysis

Assessing tumor response to therapy is a crucial step in personalized treatments. Pharmacokinetic (PK) modeling provides quantitative information about tumor perfusion and vascular permeability that are associated with prognostic factors. A fundamental step in most PK analyses is calculating the signal that is generated in the tumor vasculature. This signal is usually inseparable from the extravascular extracellular signal. It was shown previously using in vivo and phantom experiments that independent component analysis (ICA) is capable of calculating the intravascular time-intensity curve in dynamic contrast enhanced (DCE)-MRI. A novel adaptive complex independent component analysis (AC-ICA) technique is developed in this study to calculate the intravascular time-intensity curve and separate this signal from the DCE-MR images of tumors. The use of the complex-valued DCE-MRI images rather than the commonly used magnitude images satisfied the fundamental assumption of ICA, i.e., linear mixing of the sources. Using an adaptive cost function in ICA through estimating the probability distribution of the tumor vasculature at each iteration resulted in a more robust and accurate separation algorithm. The AC-ICA algorithm provided a better estimate for the intravascular time-intensity curve than the previous ICA-based method. A simulation study was also developed in this study to realistically simulate DCE-MRI data of a leaky tissue mimicking phantom. The passage of the MR contrast agent through the leaky phantom was modeled with finite element analysis using a diffusion model. Once the distribution of the contrast agent in the imaging field of view was calculated, DCE-MRI data was generated by solving the Bloch equation for each voxel at each time point. The intravascular time-intensity curve calculation results were compared to the previously proposed ICA-based intravascular time-intensity curve calculation method that applied ICA to the magnitude of the DCE-MRI data (Mag-ICA) using both simulated and experimental tissue mimicking phantoms. The AC-ICA demonstrated superior performance compared to the Mag-ICA method. AC-ICA provided more accurate estimate of intravascular time-intensity curve, having smaller error between the calculated and actual intravascular time-intensity curves compared to the Mag-ICA. Furthermore, it showed higher robustness in dealing with datasets with different resolution by providing smaller variation between the results of each datasets and having smaller difference between the intravascular time-intensity curves of various resolutions. Thus, AC-ICA has the potential to be used as the intravascular time-intensity curve calculation method in PK analysis and could lead to more accurate PK analysis for tumors.

Multifunctional magnetic resonance imaging probes

Magnetic resonance imaging (MRI) is a key imaging modality in cancer diagnostics and therapy monitoring. MRI-based tumor detection and characterization is commonly achieved by exploiting the compositional, metabolic, cellular, and vascular differences between malignant and healthy tissue. Contrast agents are frequently applied to enhance this contrast. The last decade has witnessed an increasing interest in novel multifunctional MRI probes. These multifunctional constructs, often of nanoparticle design, allow the incorporation of multiple imaging agents for complementary imaging modalities as well as anti-cancer drugs for therapeutic purposes. The composition, size, and surface properties of such constructs can be tailored as to improve biodistribution and ensure optimal delivery to the tumor microenvironment by passive or targeted mechanisms. Multifunctional MRI probes hold great promise to facilitate more specific tumor diagnosis, patient-specific treatment planning, the monitoring of local drug delivery, and the early evaluation of therapy. This chapter reviews the state-of-the-art and new developments in the application of multifunctional MRI probes in oncology.

A linear algorithm of the reference region model for DCE-MRI is robust and relaxes requirements for temporal resolution

Dynamic contrast enhanced MRI (DCE-MRI) has utility for improving clinical diagnoses of solid tumors, and for evaluating the early responses of anti-angiogenic chemotherapies. The Reference Region Model (RRM) can improve the clinical implementation of DCE-MRI by substituting the contrast enhancement of muscle for the Arterial Input Function that is used in traditional DCE-MRI methodologies. The RRM is typically fitted to experimental results with a non-linear least squares algorithm. This report demonstrates that this algorithm produces inaccurate and imprecise results when DCE-MRI results have low SNR or slow temporal resolution. To overcome this limitation, a linear least-squares algorithm has been derived for the Reference Region Model. This new algorithm improves accuracy and precision of fitting the Reference Region Model to DCE-MRI results, especially for voxel-wise analyses. This linear algorithm is insensitive to injection speeds, and has 300- to 8000-fold faster calculation speed relative to the non-linear algorithm. The linear algorithm produces more accurate results for over a wider range of permeabilities and blood volumes of tumor vasculature. This new algorithm, termed the Linear Reference Region Model, has strong potential to improve clinical DCE-MRI evaluations.

Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance

PURPOSE

To determine the effect of computer-aided diagnosis (CAD) on less-experienced and experienced observer performance in differentiation of benign from malignant prostate lesions at 3-T multiparametric magnetic resonance (MR) imaging.

MATERIALS AND METHODS

The institutional review board waived the need for informed consent. Retrospectively, 34 patients were included who had prostate cancer and had undergone multiparametric MR imaging, including T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced MR imaging prior to radical prostatectomy. Six radiologists less experienced in prostate imaging and four radiologists experienced in prostate imaging were asked to characterize different regions suspicious for cancer as benign or malignant on multiparametric MR images first without and subsequently with CAD software. The effect of CAD was analyzed by using a multiple-reader, multicase, receiver operating characteristic analysis and a linear mixed-model analysis.

RESULTS

In 34 patients, 206 preannotated regions, including 67 malignant and 64 benign regions in the peripheral zone (PZ) and 19 malignant and 56 benign regions in the transition zone (TZ), were evaluated. Stand-alone CAD had an overall area under the receiver operating characteristic curve (AUC) of 0.90. For PZ and TZ lesions, the AUCs were 0.92 and 0.87, respectively. Without CAD, less-experienced observers had an overall AUC of 0.81, which significantly increased to 0.91 (P = .001) with CAD. For experienced observers, the AUC without CAD was 0.88, which increased to 0.91 (P = .17) with CAD. For PZ lesions, less-experienced observers increased their AUC from 0.86 to 0.95 (P < .001) with CAD. Experienced observers showed an increase from 0.91 to 0.93 (P = .13). For TZ lesions, less-experienced observers significantly increased their performance from 0.72 to 0.79 (P = .01) with CAD and experienced observers increased their performance from 0.81 to 0.82 (P = .42).

CONCLUSION

Addition of CAD significantly improved the performance of less-experienced observers in distinguishing benign from malignant lesions; when less-experienced observers used CAD, they reached similar performance as experienced observers. The stand-alone performance of CAD was similar to performance of experienced observers.

Monitoring anti-angiogenic therapy in colorectal cancer murine model using dynamic contrast-enhanced MRI: comparing pixel-by-pixel with region of interest analysis

Sorafenib is a multi-kinase inhibitor that blocks cell proliferation and angiogenesis. It is currently approved for advanced hepatocellular and renal cell carcinomas in humans, where its major mechanism of action is thought to be through inhibition of vascular endothelial growth factor and platelet-derived growth factor receptors. The purpose of this study was to determine whether pixel-by-pixel analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is better able to capture the heterogeneous response of Sorafenib in a murine model of colorectal tumor xenografts (as compared with region of interest analysis). MRI was performed on a 9.4 T pre-clinical scanner on the initial treatment day. Then either vehicle or drug were gavaged daily (3 days) up to the final image. Four days later, the mice were again imaged. The two-compartment model and reference tissue method of DCE-MRI were used to analyze the data. The results demonstrated that the contrast agent distribution rate constant (K(trans)) were significantly reduced (p < 0.005) at day-4 of Sorafenib treatment. In addition, the K(trans) of nearby muscle was also reduced after Sorafenib treatment. The pixel-by-pixel analysis (compared to region of interest analysis) was better able to capture the heterogeneity of the tumor and the decrease in K(trans) four days after treatment. For both methods, the volume of the extravascular extracellular space did not change significantly after treatment. These results confirm that parameters such as K(trans), could provide a non-invasive biomarker to assess the response to anti-angiogenic therapies such as Sorafenib, but that the heterogeneity of response across a tumor requires a more detailed analysis than has typically been undertaken.

DCE and DW-MRI monitoring of vascular disruption following VEGF-Trap treatment of a rat glioma model

Vascular-targeted therapies have shown promise as adjuvant cancer treatment. As these agents undergo clinical evaluation, sensitive imaging biomarkers are needed to assess drug target interaction and treatment response. In this study, dynamic contrast enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) were evaluated for detecting response of intracerebral 9 L gliosarcomas to the antivascular agent VEGF-Trap, a fusion protein designed to bind all forms of Vascular Endothelial Growth Factor-A (VEGF-A) and Placental Growth Factor (PGF). Rats with 9 L tumors were treated twice weekly for two weeks with vehicle or VEGF-Trap. DCE- and DW-MRI were performed one day prior to treatment initiation and one day following each administered dose. Kinetic parameters (K(trans), volume transfer constant; k(ep), efflux rate constant from extravascular/extracellular space to plasma; and v(p), blood plasma volume fraction) and the apparent diffusion coefficient (ADC) over the tumor volumes were compared between groups. A significant decrease in kinetic parameters was observed 24 hours following the first dose of VEGF-Trap in treated versus control animals (p < 0.05) and was accompanied by a decline in ADC values. In addition to the significant hemodynamic effect, VEGF-Trap treated animals exhibited significantly longer tumor doubling times (p < 0.05) compared to the controls. Histological findings were found to support imaging response metrics. In conclusion, kinetic MRI parameters and change in ADC have been found to serve as sensitive and early biomarkers of VEGF-Trap anti-vascular targeted therapy.