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.

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.

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.

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.

Holographically Activatable Nanoprobe via Glutathione/Albumin-Mediated Exponential Signal Amplification for High-Contrast Tumor Imaging.

Glutathione (GSH)-activatable probes hold great promise for in vivo cancer imaging, but are restricted by their dependence on non-selective intracellular GSH enrichment and uncontrollable background noise. Here, a holographically activatable nanoprobe caging manganese tetraoxide is shown for tumor-selective contrast enhancement in magnetic resonance imaging (MRI) through cooperative GSH/albumin-mediated cascade signal amplification in tumors and rapid elimination in normal tissues. Once targeting tumors, the endocytosed nanoprobe effectively senses the lysosomal microenvironment to undergo instantaneous decomposition into Mn2+ with threshold GSH concentration of ≈ 0.12 mm for brightening MRI signals, thus achieving high contrast tumor imaging and flexible monitoring of GSH-relevant cisplatin resistance during chemotherapy. Upon efficient up-regulation of extracellular GSH in tumor via exogenous injection, the relaxivity-silent interstitial nanoprobe remarkably evolves into Mn2+ that are further captured/retained and re-activated into ultrahigh-relaxivity-capable complex by stromal albumin in the tumor, and simultaneously allows the renal clearance of off-targeted nanoprobe in the form of Mn2+ via lymphatic vessels for suppressing background noise to distinguish tiny liver metastasis. These findings demonstrate the concept of holographic tumor activation via both tumor GSH/albumin-mediated cascade signal amplification and simultaneous background suppression for precise tumor malignancy detection, surveillance, and surgical guidance.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast.

PURPOSE: A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. THEORY: Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters. METHODS: The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points. RESULTS: The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve . CONCLUSION: It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

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.

The unique second wave phenomenon in contrast enhanced ultrasound imaging with nanobubbles.

Investigation of nanobubble (NB) pharmacokinetics in contrast-enhanced ultrasound (CEUS) at the pixel level shows a unique phenomenon where the first pass of the contrast agent bolus is accompanied by a second wave. This effect has not been previously observed in CEUS with microbubbles. The objective of this study was to investigate this second-wave phenomenon and its potential clinical applications. Seven mice with a total of fourteen subcutaneously-implanted tumors were included in the experiments. After injecting a bolus of NBs, the NB-CEUS images were acquired to record the time-intensity curves (TICs) at each pixel. These TICs are fitted to a pharmacokinetic model which we designed to describe the observed second-wave phenomenon. The estimated model parameters are presented as parametric maps to visualize the characteristics of tumor lesions. Histological analysis was also conducted in one mouse to compare the molecular features of tumor tissue with the obtained parametric maps. The second-wave phenomenon is evidently shown in a series of pixel-based TICs extracted from either tumor or tissues. The value of two model parameters, the ratio of the peak intensities of the second over the first wave, and the decay rate of the wash-out process present large differences between malignant tumor and normal tissue (0.04 < Jessen-Shannon divergence < 0.08). The occurrence of a second wave is a unique phenomenon that we have observed in NB-CEUS imaging of both mouse tumor and tissue. As the characteristics of the second wave are different between tumor and tissue, this phenomenon has the potential to support the diagnosis of cancerous lesions.

Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate-Can We Make Better Use of It?

We sought to investigate whether quantitative parameters from a dynamic contrast-enhanced study can be used to differentiate cancer from normal tissue and to determine a cut-off value of specific parameters that can predict malignancy more accurately, compared to the obturator internus muscle as a reference tissue. This retrospective study included 56 patients with biopsy proven prostate cancer (PCa) after multiparametric magnetic resonance imaging (mpMRI), with a total of 70 lesions; 39 were located in the peripheral zone, and 31 in the transition zone. The quantitative parameters for all patients were calculated in the detected lesion, morphologically normal prostate tissue and the obturator internus muscle. Increase in the Ktrans value was determined in lesion-to-muscle ratio by 3.974368, which is a cut-off value to differentiate between prostate cancer and normal prostate tissue, with specificity of 72.86% and sensitivity of 91.43%. We introduced a model to detect prostate cancer that combines Ktrans lesion-to-muscle ratio value and iAUC lesion-to-muscle ratio value, which is of higher accuracy compared to individual variables. Based on this model, we identified the optimal cut-off value with 100% sensitivity and 64.28% specificity. The use of quantitative DCE pharmacokinetic parameters compared to the obturator internus muscle as reference tissue leads to higher diagnostic accuracy for prostate cancer detection.

Preclinical Profile of Gadoquatrane: A Novel Tetrameric, Macrocyclic High Relaxivity Gadolinium-Based Contrast Agent.

OBJECTIVES: The aim of this report was to characterize the key physicochemical, pharmacokinetic (PK), and magnetic resonance imaging (MRI) properties of gadoquatrane (BAY 1747846), a newly designed tetrameric, macrocyclic, extracellular gadolinium-based contrast agent (GBCA) with high relaxivity and stability. MATERIALS AND METHODS: The r1-relaxivities of the tetrameric gadoquatrane at 1.41 and 3.0 T were determined in human plasma and the nuclear magnetic relaxation dispersion profiles in water and plasma. The complex stability was analyzed in human serum over 21 days at pH 7.4 at 37°C and was compared with the linear GBCA gadodiamide and the macrocyclic GBCA (mGBCA) gadobutrol. In addition, zinc transmetallation assay was performed to investigate the kinetic inertness. Protein binding and the blood-to-plasma ratio were determined in vitro using rat and human plasma. The PK profile was evaluated in rats (up to 7 days postinjection). Magnetic resonance imaging properties were investigated using a glioblastoma (GS9L) rat model. RESULTS: The new chemical entity gadoquatrane is a macrocyclic tetrameric Gd complex with one inner sphere water molecule per Gd ( q = 1). Gadoquatrane showed high solubility in buffer (1.43 mol Gd/L, 10 mM Tris-HCl, pH 7.4), high hydrophilicity (logP -4.32 in 1-butanol/water), and negligible protein binding. The r1-relaxivity of gadoquatrane in human plasma per Gd of 11.8 mM -1 ·s -1 (corresponding to 47.2 mM -1 ·s -1 per molecule at 1.41 T at 37°C, pH 7.4) was more than 2-fold (8-fold per molecule) higher compared with established mGBCAs. Nuclear magnetic relaxation dispersion profiles confirmed the more than 2-fold higher r1-relaxivity in human plasma for the clinically relevant magnetic field strengths from 0.47 to 3.0 T. The complex stability of gadoquatrane at physiological conditions was very high. The observed Gd release after 21 days at 37°C in human serum was below the lower limit of quantification. Gadoquatrane showed no Gd 3+ release in the presence of zinc in the transmetallation assay. The PK profile (plasma elimination, biodistribution, recovery) was comparable to that of gadobutrol. In MRI, the quantitative evaluation of the tumor-to-brain contrast in the rat glioblastoma model showed significantly improved contrast enhancement using gadoquatrane compared with gadobutrol at the same Gd dose administered (0.1 mmol Gd/kg body weight). In comparison to gadoterate meglumine, similar contrast enhancement was reached with gadoquatrane with 75% less Gd dose. In terms of the molecule dose, this was reduced by 90% when compared with gadoterate meglumine. Because of its tetrameric structure and hence lower number of molecules per volume, all prepared formulations of gadoquatrane were iso-osmolar to blood. CONCLUSIONS: The tetrameric gadoquatrane is a novel, highly effective mGBCA for use in MRI. Gadoquatrane provides favorable physicochemical properties (high relaxivity and stability, negligible protein binding) while showing essentially the same PK profile (fast extracellular distribution, fast elimination via the kidneys in an unchanged form) to established mGBCAs on the market. Overall, gadoquatrane is an excellent candidate for further clinical development.

Evaluation of Physicochemical Properties, Pharmacokinetics, Biodistribution, Toxicity, and Contrast-Enhanced Cancer MRI of a Cancer-Targeting Contrast Agent, MT218.

OBJECTIVES: Preclinical assessments were performed according to the US Food and Drug Administration guidelines to determine the physicochemical properties, pharmacokinetics, clearance, safety, and tumor-specific magnetic resonance (MR) imaging of MT218, a peptidic gadolinium-based MR imaging agent targeting to extradomain B fibronectin for MR molecular imaging of aggressive tumors. MATERIALS AND METHODS: Relaxivity, chelation stability, binding affinity, safety-related target profiling, and effects on CYP450 enzymes and transporters were evaluated in vitro. Magnetic resonance imaging was performed with rats bearing prostate cancer xenografts, immunocompetent mice bearing murine pancreatic cancer allografts, and mice bearing lung cancer xenografts at different doses of MT218. Pharmacological effects on cardiovascular, respiratory, and central nervous systems were determined in rats and conscious beagle dogs. Pharmacokinetics were tested in rats and dogs. Biodistribution and excretion were studied in rats. Single and repeated dosing toxicity was evaluated in rats and dogs. In vitro and in vivo genotoxicity, in vitro hemolysis, and anaphylactic reactivity were also performed. RESULTS: At 1.4 T, the r1 and r2 relaxivities of MT218 were 5.43 and 7.40 mM -1 s -1 in pure water, 6.58 and 8.87 mM -1 s -1 in phosphate-buffered saline, and 6.54 and 8.70 mM -1 s -1 in aqueous solution of human serum albumin, respectively. The binding affinity of MT218 to extradomain B fragment is 3.45 µM. MT218 exhibited no dissociation of the Gd(III) chelates under physiological conditions. The peptide degradation half-life ( t1/2 ) of MT218 was 1.63, 5.85, and 2.63 hours in rat, dog, and human plasma, respectively. It had little effect on CYP450 enzymes and transporters. MT218 produced up to 7-fold increase of contrast-to-noise ratios in the extradomain B fibronectin-rich tumors with a dose of 0.04 mmol/kg for at least 30 minutes. MT218 had little pharmacological effect on central nervous, cardiovascular, or respiratory systems. MT218 had a mean plasma elimination half-life ( t1/2 ) of 0.31 and 0.89 hours in rats and dogs at 0.1 mmol/kg, respectively. No detectable Gd deposition was observed in the brain at 6 hours postinjection of MT218 at 0.1 mmol/kg in rats. MT218 was not mutagenic and had no mortality or morbidity in the rats or dogs up to 1.39 and 0.70 mmol/kg/d, respectively. The no observed adverse effect level of MT218 in Sprague-Dawley rats was 1.39 mmol/kg for single dosing and 0.46 mmol/kg/d for repeated dosing. The no observed adverse effect level in dogs was 0.07 mmol/kg/d. MT218 exhibited no genotoxicity, hemolysis, and anaphylactic reactivity. CONCLUSION: The preclinical assessments showed that the targeted contrast agent MT218 has high r1 and r2 relaxivities, satisfactory physicochemical properties, pharmacokinetic, and safety profiles and produces effective tumor enhancement in multiple cancer types in rats and mice at reduced doses.

Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation.

This work proposes to develop and evaluate a deep learning framework that jointly optimizes k-t sampling patterns and reconstruction for head and neck dynamic contrast-enhanced (DCE) MRI aiming to reduce bias and uncertainty of pharmacokinetic (PK) parameter estimation. 2D Cartesian phase encoding k-space subsampling patterns for a 3D spoiled gradient recalled echo (SPGR) sequence along a time course of DCE MRI were jointly optimized in a deep learning-based dynamic MRI reconstruction network by a loss function concerning both reconstruction image quality and PK parameter estimation accuracy. During training, temporal k-space data sharing scheme was optimized as well. The proposed method was trained and tested by multi-coil complex digital reference objects of DCE images (mcDROs). The PK parameters estimated by the proposed method were compared with two published iterative DCE MRI reconstruction schemes using normalized root mean squared errors (NRMSEs) and Bland-Altman analysis at temporal resolutions of [Formula: see text] = 2s, 3s, 4s, and 5s, which correspond to undersampling rates of R = 50, 34, 25, and 20. The proposed method achieved low PK parameter NRMSEs at all four temporal resolutions compared with the benchmark methods on testing mcDROs. The Bland-Altman plots demonstrated that the proposed method reduced PK parameter estimation bias and uncertainty in tumor regions at temporal resolution of 2s. The proposed method also showed robustness to contrast arrival timing variations across patients. This work provides a potential way to increase PK parameter estimation accuracy and precision, and thus facilitate the clinical translation of DCE MRI.

Application of Intraoperative Contrast-Enhanced Ultrasound in the Resection of Brain Tumors.

OBJECTIVE: To investigate the value of routine intraoperative ultrasound (IU) and intraoperative contrast-enhanced ultrasound (ICEUS) in the surgical treatment of brain tumors, and to explore the utilization of ICEUS for the removal of the remnants surrounding the resection cavity. METHODS: In total, 51 patients who underwent operations from 2012 to 2018 due to different tumors in the brain were included in this study. The clinical data were evaluated retrospectively. IU was performed in all patients, among which 28 patients underwent ICEUS. The effects of IU and ICEUS on tumor resection and recurrence were evaluated. Semiquantitative analysis was performed to compare ICEUS parameters of the brain tumor with those of the surrounding tissue. RESULTS: In total, 36 male and 15 female patients were included in this study. The average age was 43 years (range: 14-68 years). The follow-up period was from 7 to 74 months (mean follow-up 32 months). IU was used in all patients, and no lesion was missed. Among them, 28 patients underwent ICEUS. The rate of total removal of the ICEUS group (23/28, 82%) was significantly higher than that of the IU group (11/23, 48%) (P<0.05). The recurrence rate of ICEUS and IU was 18% (5/23), and 22% (5/28), respectively, and the difference did not reach statistical significance (P>0.05). The semiquantitative analysis showed that the intensity and the transit time of microbubbles reaching the lesions were significantly different from the intensity and the transit time of microbubbles reaching the surrounding tissue (P<0.05) and reflected indirectly the volume and the speed of blood perfusion in the lesions was higher than those in the surrounding tissue. CONCLUSION: ICEUS is a useful tool in localizing and outlining brain lesions, especially for the resection of the hypervascular lesions in the brain. ICEUS could be more beneficial for identifying the remnants and improving the rate of total removal of these lesions than routine intraoperative ultrasound.

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 [Ktrans ], fractional volume of the extravascular extracellular space [ve ], and blood plasma volume fraction [vp ]) 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 Ktrans and ve , and less than 11% in the estimation of vp . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.

Pharmacokinetic/Pharmacodynamic Determinations of Iron-tannic Molecular Nanoparticles with its Implication in MR Imaging and Enhancement of Liver Clearance.

Assessment and enhancement of liver clearance are promising strategies for protection of liver from various liver diseases. Iron-tannic nanoparticles (FTs) were previously considered as imageable autophagic enhancers with biodegradation potential. Herein, we present a new approach for utilizing Iron-tannic nanoparticles (FTs) as a tool for imaging and increasing liver clearance. Pharmacokinetic profiling suggested that FTs were initially found in blood circulation and thereafter were distributed to the liver. By using MR imaging (T1 weighted), maximum MRI signal enhancement was found to occur after 30 minutes post-injection (i.v.) and gradually decreased afterward. Decreasing MRI signal may be due to FTs metabolism by the liver. By assessing imaging-derived pharmacokinetics, we can simply determine the rate constant of liver degradation of FTs. Potentially, we might use this parameter to monitor liver function, where its clearance is of concern. Once functional implication of FTs in liver clearance was investigated, FTs were found to induce hepatocyte autophagy along with activation of lysosomes. Consequently, the hepatocytes were capable of efficiently clearing cellular debris. From these results, it is clear that FTs should be considered as a molecular tool for quantitative MRI-derived liver function assessment, and for enhancing clearance function in liver parenchyma. Hopefully, our findings will pave the way to develop new strategies for non-invasive assessment and enhancement of liver clearance.