Contrast Media-driven Anthropogenic Gadolinium: Knowns and Unknowns.

Gadolinium-based contrast agents (GBCAs) have augmented the capabilities of MRI, which has led to their widespread and increasing use in radiology practice. GBCAs are introduced into the environment through disposal of unused product and elimination after intravenous injection, both primarily via liquid dispersion into the environment. This human introduction of gadolinium into the environment, referred to as anthropogenic gadolinium, is associated with the detection of gadolinium in water systems, raising concerns for potential adverse impact and prompting certain mitigation actions. This article summarizes the existing knowledge and problem scope, conveys the relevant underlying chemical principles of chelate dissociation, and offers an inferred perspective that the magnitude of the problem is most unlikely to cause human harm. The merits and limitations regarding possible mitigation tactics, such as collecting urine after GBCA administration, use of lower-dose high-relaxivity macrocyclic GBCAs, and the option for virtual contrast-enhanced examinations, will be discussed. Finally, the potential for monitoring gadolinium uptake in bone will be presented, and recommendations for future research will be offered. © RSNA, 2024 See also the article by Ibrahim et al in this issue. See also the article by McKee et al in this issue.

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.

A quantitative MRI-based approach to estimate the permeation and retention of nanomedicines in tumors.

Despite the potential of the enhanced permeability and retention (EPR) effect in tumor passive targeting, many nanotherapeutics have failed to produce meaningful clinical outcomes due to the variable and challenging nature of the tumor microenvironment (TME) and EPR effect. This EPR variability across tumors and inconsistent translation of nanomedicines from preclinical to clinical settings necessitates a reliable method to assess its presence in individual tumors. This study aimed to develop a reliable and non-invasive approach to estimate the EPR effect in tumors using a clinically compatible quantitative magnetic resonance imaging (qMRI) technique combined with a nano-sized MRI contrast agent. A quantitative MR imaging was developed using a dynamic contrast-enhanced (DCE) MRI protocol. Then, the permeability and retention of the nano-sized MRI contrast agent were evaluated in three different ovarian xenograft tumor models. Results showed significant differences in EPR effects among the tumor models, with tumor growth influencing the calculated parameters of permeability (Ktrans) and retention (Ve) based on Tofts pharmacokinetic (PK) modeling. Our data indicate that the developed quantitative DCE-MRI method, combined with the Tofts PK modeling, provides a robust and non-invasive approach to screen tumors for their responsiveness to nanotherapeutics. These results imply that the developed qMRI method can be beneficial for personalized cancer treatments by ensuring that nanotherapeutics are administered only to patients with tumors showing sufficient EPR levels.

Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements.

DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.

Exploring the potential of Physics-Informed Neural Networks to extract vascularization data from DCE-MRI in the presence of diffusion.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used to assess tissue vascularization, particularly in oncological applications. However, the most widely used pharmacokinetic (PK) models do not account for contrast agent (CA) diffusion between neighboring voxels, which can limit the accuracy of the results, especially in cases of heterogeneous tumors. To address this issue, previous works have proposed algorithms that incorporate diffusion phenomena into the formulation. However, these algorithms often face convergence problems due to the ill-posed nature of the problem. In this work, we present a new approach to fitting DCE-MRI data that incorporates CA diffusion by using Physics-Informed Neural Networks (PINNs). PINNs can be trained to fit measured data obtained from DCE-MRI while ensuring the mass conservation equation from the PK model. We compare the performance of PINNs to previous algorithms on different 1D cases inspired by previous works from literature. Results show that PINNs retrieve vascularization parameters more accurately from diffusion-corrected tracer-kinetic models. Furthermore, we demonstrate the robustness of PINNs compared to other traditional algorithms when faced with noisy or incomplete data. Overall, our results suggest that PINNs can be a valuable tool for improving the accuracy of DCE-MRI data analysis, particularly in cases where CA diffusion plays a significant role.

Pharmacokinetics, Safety, and Tolerability of the Novel Tetrameric, High-Relaxivity, Macrocyclic Gadolinium-Based Contrast Agent Gadoquatrane in Healthy Adults.

OBJECTIVES: Gadolinium (Gd)-based contrast agents are well established in clinical routine and have been proven safe and effective. However, there is a need for "next-generation" Gd-based contrast agents that would allow lowering the Gd dose used for routine contrast-enhanced magnetic resonance imaging procedures. The objective of this first-in-human study was to investigate the pharmacokinetic profile, safety, and tolerability of gadoquatrane, a novel high-relaxivity Gd-based contrast agent. MATERIALS AND METHODS: This study was conducted in 2018/2019 as a prospective, randomized, single-blind, single-dose, placebo-controlled, escalating-dose study. Healthy volunteers were randomly assigned (6:2) to intravenous administration of gadoquatrane (0.025 to 0.2 mmol Gd/kg body weight) or placebo. Study procedures included collection of blood samples and excreta for pharmacokinetic analyses and safety assessments. RESULTS: Forty-nine healthy study participants (mean age ± SD, 35 ± 6.3 years; 24 female) were evaluated. The effective half-life of gadoquatrane in plasma was short and similar in all dose groups (1.4-1.7 hours). Plasma concentrations around the lower quantitation limit (0.0318 µmol Gd/L) were reached 15-72 hours after administration. The volume of distribution at steady state was ~0.2 L/kg in all dose groups. The clearance (total and renal) was ~0.1 L/h per kilogram in all groups. Across dose groups, the exposure of gadoquatrane increased dose-proportionally. Metabolite profiling revealed no hint of degradation in vivo or release of free Gd. Seven of 36 participants (19.4%) receiving gadoquatrane and 4 of 13 participants (30.8%) receiving placebo experienced mild or moderate treatment-emergent adverse events. No serious adverse events occurred. The analysis of the Gd concentration-QTc interval relationship indicated no risk of QT/QTc prolongation (>10 milliseconds) with gadoquatrane at clinical dose levels. CONCLUSIONS: Gadoquatrane with its high-relaxivity, pharmacokinetic similarity to established Gd-based contrast agents and high tolerability is a promising "next-generation" contrast agent for magnetic resonance imaging.

Safety, Tolerability, and Pharmacokinetics of a Novel Macrocyclic Gadolinium-Based Contrast Agent, HNP-2006, in Healthy Subjects.

OBJECTIVES: Gadolinium-based contrast agents (GBCAs) are indispensable in contrast-enhanced magnetic resonance imaging. A higher risk of gadolinium deposition in linear GBCAs required the introduction of macrocyclic GBCAs with a stable molecular structure. We conducted the first-in-human study to evaluate the safety, tolerability, and pharmacokinetics (PKs) of HNP-2006, a novel macrocyclic GBCA, in healthy male subjects. MATERIALS AND METHODS: A randomized, placebo-controlled, double-blind, single-ascending dose study was conducted. Subjects received either a single intravenous bolus injection of HNP-2006 or its matching placebo with a treatment-to-placebo ratio of 6:2 at the dose level of 0.02, 0.05, 0.1, 0.2, and 0.3 mmol/kg. Safety was assessed through routine clinical assessments. Blood sampling and urine collection were performed up to 72 hours postdose for PK assessments. Noncompartmental methods were used to calculate PK parameters, and a population PK model was constructed. RESULTS: Overall, 40 subjects completed the study. Fourteen subjects reported 22 treatment-emergent adverse events (TEAEs). The severity of all TEAEs was mild, and the HNP-2006 dose was associated with the incidence of TEAEs. The most common TEAEs included nausea and dizziness, which occurred within an hour of administration. HNP-2006 was rapidly eliminated by urinary excretion with a half-life of 1.8-2.0 hours and showed a dose-proportional PK. A 2-compartment model had the best fit with the population PK analysis. CONCLUSIONS: A single intravenous dose of HNP-2006 was well-tolerated and safe up to 0.30 mmol/kg. HNP-2006 was rapidly excreted in urine and exhibited dose-independent PK profiles.

A Comprehensive Overview of the Efficacy and Safety of Gadopiclenol: A New Contrast Agent for MRI of the CNS and Body.

ABSTRACT: This review describes the pharmacokinetics, efficacy, and safety of gadopiclenol, a new macrocyclic gadolinium-based contrast agent (GBCA) recently approved by the Food and Drug Administration at the dose of 0.05 mmol/kg. Gadopiclenol is a high relaxivity contrast agent that shares similar pharmacokinetic characteristics with other macrocyclic GBCAs, including a predominant renal excretion. In pediatric patients aged 2-17 years, the pharmacokinetic parameters (assessed through a population pharmacokinetics model) were comparable to those observed in adults, indicating no need for age-based dose adjustment. For contrast-enhanced magnetic resonance imaging (MRI) of the central nervous system (CNS) and body indications, gadopiclenol at 0.05 mmol/kg was shown to be noninferior to gadobutrol at 0.1 mmol/kg in terms of 3 lesion visualization parameters (ie, lesion border delineation, internal morphology, and contrast enhancement). Moreover, for contrast-enhanced MRI of the CNS, compared with gadobenate dimeglumine at 0.1 mmol/kg, gadopiclenol exhibited superior contrast-to-noise ratio at 0.1 mmol/kg and comparable contrast-to-noise ratio at 0.05 mmol/kg. A pooled safety analysis of 1047 participants showed a favorable safety profile for gadopiclenol. Comparative studies showed that the incidence and nature of adverse drug reactions with gadopiclenol were comparable to those observed with other GBCAs. Importantly, no significant safety concerns were identified in pediatric and elderly patients, as well as in patients with renal impairment. Overall, these findings support the clinical utility and safety of gadopiclenol for MRI in adult and pediatric patients aged 2 years and older in CNS and body indications.

Influence of scan duration on dynamic contrast -enhanced magnetic resonance imaging pharmacokinetic parameters for brain lesions.

OBJECTIVE: Gadolinium-based contrast agent needs time to leak into the extravascular-extracellular space, leak back into the vascular space, and reach an equilibrium state. For this reason, acquisition times of <10 min may cause inaccurate estimation of pharmacokinetic parameters. Since no studies have been conducted on the influence of long scan times on DCE-MRI parameters in brain tumors, the aim of this study is to investigate the variation of DCE-MRI-derived kinetic parameters as a function of acquisition time, from 5 to 10 min in brain tumors. MATERIALS AND METHODS: Fifty-two patients with histologically confirmed brain tumors were enrolled in this retrospective study, and examination at 3 T, DCE-MRI, with scan duration of 10 min, was used for retrospective generation of 6 sets of quantitative DCE-MRI maps (Ktrans, Ve and Kep) from 5 to 10 min. Features were extracted from the DCE-MRI maps in contrast enhancement (CE) volumes. Kruskal-Wallis with post-hoc correction and coefficient of variation (CoV) were used as statistical test to compare DCE-MRI maps obtained from 6 data sets. SIGNIFICANCE: p < 0.05. RESULTS: No differences in Ktrans features in CE volumes between different scan durations. Ve, Kep features in CE volumes were influenced by different data length. The highest number of significantly different Ve and Kep features in CE volumes were between 5 min and 10 min (p < 0.013), 5 min and 9 min (p < 0.044), 6 min and 10 min (p < 0.040). CoV of Kep was reduced from 5 min to 10 min, going from highly variable (CoV = 0.70) to mildly variable (CoV = 0.42). CONCLUSION: Kep and Ve were time-dependent in brain tumors, so a longer scan time is needed to obtain reliable parameter values. Ktrans was found to be time-independent, as it remains the same in all 6 acquisition times and is the only reliable parameter with short acquisition times.

Efficient estimation of pharmacokinetic parameters from breast dynamic contrast-enhanced MRI based on a convolutional neural network for predicting molecular subtypes.

Objective. Tracer kinetic models allow for estimating pharmacokinetic (PK) parameters, which are related to pathological characteristics, from breast dynamic contrast-enhanced magnetic resonance imaging. However, existing tracer kinetic models subject to inaccuracy are time-consuming for PK parameters estimation. This study aimed to accurately and efficiently estimate PK parameters for predicting molecular subtypes based on convolutional neural network (CNN).Approach. A CNN integrating global and local features (GL-CNN) was trained using synthetic data where known PK parameters map was used as the ground truth, and subsequently used to directly estimate PK parameters (volume transfer constantKtransand flux rate constantKep) map. The accuracy assessed by the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and concordance correlation coefficient (CCC) was compared between the GL-CNN and Tofts-based PK parameters in synthetic data. Radiomic features were calculated from the PK parameters map in 208 breast tumors. A random forest classifier was constructed to predict molecular subtypes using a discovery cohort (n= 144). The diagnostic performance evaluated on a validation cohort (n= 64) using the area under the receiver operating characteristic curve (AUC) was compared between the GL-CNN and Tofts-based PK parameters.Main results. The average PSNR (48.8884), SSIM (0.9995), and CCC (0.9995) between the GL-CNN-basedKtransmap and ground truth were significantly higher than those between the Tofts-basedKtransmap and ground truth. The GL-CNN-basedKtransobtained significantly better diagnostic performance (AUCs = 0.7658 and 0.8528) than the Tofts-basedKtransfor luminal B and HER2 tumors. The GL-CNN method accelerated the computation by speed approximately 79 times compared to the Tofts method for the whole breast of all patients.Significance. Our results indicate that the GL-CNN method can be used to accurately and efficiently estimate PK parameters for predicting molecular subtypes.

Preclinical Evaluation of a Protein-Based Nanoscale Contrast Agent for MR Angiography at an Ultralow Dose.

Purpose: BSA-biomineralized Gd nanoparticles (Gd@BSA NPs) have been recognized as promising nanoscale MR contrast agents. The aim of this study was to carry out a preclinical evaluation of these NPs in a middle-sized animal model (rabbits). Methods: New Zealand white rabbits were treated intravenously with Gd@BSA NPs (0.02 mmol Gd/kg) via a clinically-used high-pressure injector, with commercial Gd-diethylene triamine pentaacetate (Gd-DTPA)-injected group as control. Then MR angiography was performed according to the standard clinical protocol with a 3.0-T MR scanner. The SNR and CNR of the main arteries and branches were monitored. Pharmacokinetics and bioclearance were continuously evaluated in blood, urine, and feces. Gd deposition in vital organs was measured by ICP‒MS. Weight monitoring, HE staining, and blood biochemical analysis were also performed to comprehensively estimate systemic toxicity. Results: The ultrasmall Gd@BSA NPs (<6 nm) exhibited high stability and T1 relaxivity. Compared to Gd-DTPA, Gd@BSA NPs demonstrated superior vascular system imaging performance at ultralow doses, especially of the cardiac artery and other main branches, and exhibited a significantly higher SNR and CNR. Notably, the Gd@BSA NPs showed a shorter half-life in blood, less retention in organs, and improved biocompatibility. Conclusion: The preclinical evaluations here demonstrated that Gd@BSA NPs are promising and advantageous MR CA candidates that can be used at a low dose with excellent MR imaging performance, thus suggesting its further clinical trials and applications.

Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI.

The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with 'Under-sampling with Repeated Advancing Phase' (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.

Dynamic contrast-enhanced MRI to characterize angiogenesis in primary epithelial ovarian cancer: An exploratory study.

PURPOSE: Angiogenesis is essential for tumor growth. Currently, there are no established imaging biomarkers to show angiogenesis in tumor tissue. The aim of this prospective study was to evaluate whether semiquantitative and pharmacokinetic DCE-MRI perfusion parameters could be used to assess angiogenesis in epithelial ovarian cancer (EOC). METHOD: We enrolled 38 patients with primary EOC treated in 2011-2014. DCE-MRI was performed with a 3.0 T imaging system before the surgical treatment. Two different sizes of ROI were used to evaluate semiquantitative and pharmacokinetic DCE perfusion parameters: a large ROI (L-ROI) covering the whole primary lesion on one plane and a small ROI (S-ROI) covering a small solid, highly enhancing focus. Tissue samples from tumors were collected during the surgery. Immunohistochemistry was used to measure the expression of vascular endothelial growth factor (VEGF), its receptors (VEGFRs) and to analyse microvascular density (MVD) and the number of microvessels. RESULTS: VEGF expression correlated inversely with Ktrans (L-ROI, r = -0.395 (p = 0.009), S-ROI, r = -0.390, (p = 0.010)), Ve (L-ROI, r = -0.395 (p = 0.009), S-ROI, r = -0.412 (p = 0.006)) and Vp (L-ROI, r = -0.388 (p = 0.011), S-ROI, r = -0.339 (p = 0.028)) values in EOC. Higher VEGFR-2 correlated with lower DCE parameters Ktrans (L-ROI, r = -0.311 (p = 0.040), S-ROI, r = -0.337 (p = 0.025)) and Ve (L-ROI, r = -0.305 (p = 0.044), S-ROI, r = -0.355 (p = 0.018)). We also found that MVD and the number of microvessels correlated positively with AUC, Peak and WashIn values. CONCLUSIONS: We observed that several DCE-MRI parameters correlated with VEGF and VEGFR-2 expression and MVD. Thus, both semiquantitative and pharmacokinetic perfusion parameters of DCE-MRI represent promising tools for the assessment of angiogenesis in EOC.

Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer.

PURPOSE: We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that Ktrans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known. PROCEDURES: Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment. RESULTS: Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, kep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when kep is obtained from fitting the DCE-MRI data using the SSM, kep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of kep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when kep is derived from the TM, only the use of individual AIF leads to a significant increase of kep after PEGPH20 treatment, whereas the %change of kep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of Vp (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of Vp is consistent with a large increase of mean capillary lumen area estimated from immunostaining. CONCLUSION: Our results suggest that kep derived from SSM and Vp from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.

The arterial input function: Spatial dependence within the imaging volume and its influence on 3D quantitative dynamic contrast-enhanced MRI for head and neck cancer.

PURPOSE: To evaluate the dependence of the arterial input function (AIF) on the imaging z-axis and its effect on 3D DCE MRI pharmacokinetic parameters as mediated by the SPGR signal equation and Extended Tofts-Kermode model. THEORY: For SPGR-based 3D DCE MRI acquisition of the head and neck, inflow effects within vessels violate the assumptions underlying the SPGR signal model. Errors in the SPGR-based AIF estimate propagate through the Extended Tofts-Kermode model to affect the output pharmacokinetic parameters. MATERIALS AND METHODS: 3D DCE-MRI data were acquired for six newly diagnosed HNC patients in a prospective single arm cohort study. AIF were selected within the carotid arteries at each z-axis location. A region of interest (ROI) was placed in normal paravertebral muscle and the Extended Tofts-Kermode model solved for each pixel within the ROI for each AIF. Results were compared to those obtained with a published population average AIF. RESULTS: Due to inflow effect, the AIF showed extreme variation in their temporal shapes. Ktrans was most sensitive to the initial bolus concentration and showed more variation over the muscle ROI with AIF taken from the upstream portion of the carotid. kep was less sensitive to the peak bolus concentration and showed less variation for AIF taken from the upstream portion of the carotid. CONCLUSION: Inflow effects may introduce an unknown bias to SPGR-based 3D DCE pharmacokinetic parameters. Variation in the computed parameters depends on the selected AIF location. In the context of high flow, measurements may be limited to relative rather than absolute quantitative parameters.

Fractal Dimension Analysis of Pixel Dynamic Contrast Enhanced-Magnetic Resonance Imaging Pharmacokinetic Parameters for Discrimination of Benign and Malignant Breast Lesions.

PURPOSE: Breast cancer is the most frequent cancer in women worldwide. However, its diagnosis mostly depends on visual examination of radiologic images, leading to an overdiagnosis with substantial costs. Therefore, a quantitative approach such as dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) through pharmacokinetic (PK) modeling is required for reliable analysis. As PK parameters lack information on parameter heterogeneity, texture-based analysis is required to quantify PK parameter heterogeneity. Therefore, this study focused on determining the usefulness of fractal dimension (FD) as a potential imaging biomarker of tumor heterogeneity for discriminating benign and malignant breast lesions. METHODS: Parametric maps for PK parameters, extravasation rate of contrast agent from blood plasma to extravascular extracellular space (Ktrans) and volume fraction of extravascular extracellular space (ve), were generated for the regions of interest (ROIs) under the standard model using 18 lesions. Then, tumor ROI and pixel DCE-MRI time-course data were analyzed to extract pixel values of Ktrans and ve. For each ROI, FD values of Ktrans and ve were computed using the blanket method. RESULTS: The FD values of Ktrans for benign and malignant lesions varied from 2.96 to 3.49 and from 2.37 to 3.16, respectively, whereas FD values of ve for benign and malignant lesions varied from 3.01 to 5.15 and 2.42 to 3.44, respectively. There were significant differences in FD values derived from Ktrans parametric maps (P = .0053) and ve parametric maps (P = .0271) between benign and malignant lesions according to the statistical analysis. CONCLUSION: Incorporating texture heterogeneity changes in breast lesions captured by FD with quantitative DCE-MRI parameters generated under the standard model is a potential marker for prediction of malignant lesions.

Comparison of DCE-MRI parametric mapping using MP2RAGE and variable flip angle T1 mapping.

Quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) measures the rate of transfer of contrast agent from the vascular space to the tissue space by fitting signal-time data to pharmacokinetic models. However, these models are very sensitive to errors in T1 mapping. Accurate T1 mapping is necessary for high quality quantitative DCE-MRI studies. This study compares magnetization prepared rapid (two) gradient echo sequence (MP2RAGE) T1-mapping accuracy to the conventional variable flip angle (VFA) approach, and also determines the effect of the new T1-mapping method on the Ktrans parameter. VFA and MP2RAGE T1 values were compared to the gold standard inverse recovery (IR) method in phantom over manually drawn ROIs. In vivo, ROIs were manually drawn over prostate and prostatic lesions. Average T1 values over ROIs were compared and Ktrans maps for each method were calculated via the extended Tofts model. VFA-T1 maps overestimated T1 values by up to 50% compared to gold standard IR T1 values in phantom. MP2RAGE differed by up to 9%. MP2RAGE-T1 and Ktrans values were significantly different from VFA values over prostatic lesions (p < 0.05). Ktrans was consistently underestimated using VFA compared to MP2RAGE (p < 0.05). MP2RAGE T1 maps are shown to be more accurate, leading to more reliable pharmacokinetic modeling. This can potentially lead to better lesion characterization and improve clinical outcomes.

Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer.

BACKGROUND: Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP). PURPOSE: To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (Ktrans , ve , kep , and vp ), for HNC and muscle. STUDY TYPE: Prospective. POPULATION: A total of 34 HNC patients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins. ASSESSMENT: Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios. STATISTICAL TESTS: Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant. RESULTS: Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean Ktrans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8). DATA CONCLUSION: We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Reproducibility and Optimal Arterial Input Function Selection in Dynamic Contrast-Enhanced Perfusion MRI in the Healthy Brain.

BACKGROUND: Dynamic contrast-enhanced MRI (DCE-MRI) has seen increasing use for quantification of low level of blood-brain barrier (BBB) leakage in various pathological disease states and correlations with clinical outcomes. However, currently there exists limited studies on reproducibility in healthy controls, which is important for the establishment of a normality threshold for future research. PURPOSE: To investigate the reproducibility of DCE-MRI and to evaluate the effect of arterial input function (AIF) selection and manual region of interests (ROI) delineation vs. automated global segmentation. STUDY TYPE: Prospective. POPULATION: A total of 16 healthy controls; 11 females; mean age 28.7 years (SD 10.1). FIELD STRENGTH/SEQUENCE: A 3T; GE DCE; 3D TFE T1WI. 2D TSE T2. ASSESSMENT: The influx constant Ki , a measure of BBB permeability, and Vp , the blood plasma volume, was calculated using the Patlak model. Cerebral blood flow (CBF) was calculated using Tikhonov model free deconvolution. Manual tissue ROIs, drawn by H.J.S. (30+ years of experience), were compared to automatic tissue segmentation. STATISTICAL TESTS: Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) was used to assess reproducibility. Bland-Altman plots were used to evaluate agreement between measurements day 1 vs. day 2, and manual vs. segmentation method. RESULTS: Ki showed excellent reproducibility in both white and gray matter with an ICC between 0.79 and 0.82 and excellent agreement between manual ROI and automatic segmentation, with an ICC of 0.89 for Ki in WM. Furthermore, Ki values in gray and white matter conforms with histological tissue characteristics, where gray matter generally has a 2-fold higher vessel density. The highest reproducibility measures of Ki (ICC = 0.83), CBF (ICC = 0.77) and Vd (ICC = 0.83) was obtained with the AIF sampled in the internal carotid artery (ICA). DATA CONCLUSION: DCE-MRI shows excellent reproducibility of pharmacokinetic variables derived from healthy controls. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Optimal Temporal Resolution to Achieve Good Image Quality and Perform Pharmacokinetic Analysis in Free-breathing Dynamic Contrast-enhanced MR Imaging of the Pancreas.

PURPOSE: The optimal temporal resolution for free-breathing dynamic contrast-enhanced MRI (FBDCE-MRI) of the pancreas has not been determined. This study aimed to evaluate the appropriate temporal resolution to achieve good image quality and to perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using golden-angle radial sparse parallel (GRASP). METHODS: Sixteen participants (53 ± 15 years, eight females) undergoing FBDCE-MRI were included in this prospective study. Images were retrospectively reconstructed at four temporal resolutions (1.8, 3.0, 4.8, and 7.8s). Two radiologists (5 years of experience) evaluated the image quality of each reconstructed image by assessing the visualization of the celiac artery (CEA), the common hepatic artery, the splenic artery, each area of the pancreas, and artifacts using a 5-point scale. Using Tissue-4D, pharmacokinetic parameters were calculated for each area in the reconstructed images at each temporal resolution for 16 examinations, excluding two with errors in the pharmacokinetic modeling analysis. Friedman and Bonferroni tests were used for analysis. A P value < 0.05 was considered statistically significant. RESULTS: During vascular assessment, only scores for the CEA at 7.8s were significantly lower than the other temporal resolutions. Scores of all pancreatic regions and artifacts were significantly lower at 1.8s than at 4.8s and 7.8s. In the pharmacokinetic analysis, all volume transfer coefficients (Ktrans), rate constants (Kep), and the initial area under the concentration curve (iAUC) in the pancreatic head and tail were significantly lower at 4.8s and 7.8s than at 1.8s. iAUC in the pancreatic body and extracellular extravascular volume fraction (Ve) in the pancreatic head were significantly lower at 7.8s than at 1.8s. CONCLUSION: A temporal resolution of 3.0s is appropriate to achieve image quality and perform pharmacokinetic analysis in FBDCE-MRI of the pancreas using GRASP.