Quantifying gadolinium-based nanoparticle uptake distributions in brain metastases via magnetic resonance imaging.

AGuIX, a novel gadolinium-based nanoparticle, has been deployed in a pioneering double-blinded Phase II clinical trial aiming to assess its efficacy in enhancing radiotherapy for tumor treatment. This paper moves towards this goal by analyzing AGuIX uptake patterns in 23 patients. A phantom was designed to establish the relationship between AGuIX concentration and longitudinal ( T 1 ) relaxation. A 3T MRI and MP2RAGE sequence were used to generate patient T 1 maps. AGuIX uptake in tumors was determined based on longitudinal relaxivity. AGuIX (or placebo) was administered to 23 patients intravenously at 100 mg/kg 1-5 hours pre-imaging. Each of 129 brain metastases across 23 patients were captured in T 1 maps and examined for AGuIX uptake and distribution. Inferred AGuIX recipients had average tumor uptakes between 0.012 and 0.17 mg/ml, with a mean of 0.055 mg/ml. Suspected placebo recipients appeared to have no appreciable uptake. Tumors presented with varying spatial AGuIX uptake distributions, suspected to be related to differences in accumulation time and patient-specific bioaccumulation factors. This research demonstrates AGuIX's ability to accumulate in brain metastases, with quantifiable uptake via T 1 mapping. Future analyses will extend these methods to complete clinical trial data (~ 134 patients) to evaluate the potential relationship between nanoparticle uptake and possible tumor response following radiotherapy.Clinical Trial Registration Number: NCT04899908.Clinical Trial Registration Date: 25/05/2021.

Structural and practical identifiability of contrast transport models for DCE-MRI.

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

Preparation of Hematoporphyrin-Poly(Lactic Acid) Nanoparticles Encapsulated Perfluoropentane/Salicylic Acid for Enhanced US/CEST MR Bimodal Imaging.

Background: Medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, and fluorescence imaging, have gained widespread acceptance in clinical practice for tumor diagnosis. Each imaging modality has its own unique principles, advantages, and limitations, thus necessitating a multimodal approach for a comprehensive disease understanding of the disease process. To enhance diagnostic precision, physicians frequently integrate data from multiple imaging modalities, driving research advancements in multimodal imaging technology research. Methods: In this study, hematoporphyrin-poly (lactic acid) (HP-PLLA) polymer was prepared via ring-opening polymerization and thoroughly characterized using FT-IR, 1H-NMR, XRD, and TGA. HP-PLLA based nanoparticles encapsulating perfluoropentane (PFP) and salicylic acid were prepared via emulsion-solvent evaporation. Zeta potential and mean diameter were assessed using DLS and TEM. Biocompatibility was evaluated via cell migration, hemolysis, and cytotoxicity assays. Ultrasonic imaging was performed with a dedicated apparatus, while CEST MRI was conducted using a 7.0 T animal scanner. Results: We designed and prepared a novel dual-mode nanoimaging probe SA/PFP@HP-PLLA NPs. PFP enhanced US imaging, while salicylic acid bolstered CEST imaging. With an average size of 74.43 ± 1.12 nm, a polydispersity index of 0.175 ± 0.015, and a surface zeta potential of -64.1 ± 2.11 mV. These NPs exhibit excellent biocompatibility and stability. Both in vitro and in vivo experiments confirmed the SA/PFP@HP-PLLA NP's ability to improve tumor characterization and diagnostic precision. Conclusion: The SA/PFP@HP-PLLA NPs demonstrate promising dual-modality imaging capabilities, indicating their potential for preclinical and clinical use as a contrast agent.

In vivo micro-computed tomography evaluation of radiopaque, polymeric device degradation in normal and inflammatory environments.

Polymeric biomedical implants are an important clinical tool, but degradation remains difficult to determine post-implantation. Computed tomography (CT) could be a powerful tool for device monitoring, but polymers require incorporation of radiopaque contrast agents to be distinguishable from tissue. In addition, immune response to radiopaque devices must be characterized as it modulates device function. Radiopaque devices and films were produced by incorporating 0-20 wt% TaOx nanoparticles into polymers: polycaprolactone (PCL) and poly(lactide-co-glycolide) (PLGA). In vitro inflammatory responses of mouse bone marrow-derived macrophages to polymer matrix incorporating TaOx nanoparticles was determined by monitoring cytokine secretion. Nanoparticle addition stimulated a slight inflammatory reaction, increasing TNFα secretion, mediated by changes in polymer matrix properties. Subsequently, devices (PLGA 50:50 + 20 wt% TaOx) were implanted subcutaneously in a mouse model of chronic inflammation, that featured a sustained increase in inflammatory response local to the implant site over 12 weeks. No changes to device degradation rates or foreign body response were noted between a normal and chronically stimulated inflammatory environment. Serial CT device monitoring post-implantation provided a detailed timeline of device collapse, with no rapid, spontaneous release of nanoparticles that occluded matrix visualization. Importantly, repeat CT sessions did not ablate the immune system or alter degradation kinetics. Thus, polymer devices incorporating radiopaque nanoparticles can be used for in situ monitoring and be readily combined with other medical imaging techniques, for a dynamic view biomaterial and tissue interactions. STATEMENT OF SIGNIFICANCE: A growing number of implantable devices are in use in the clinic, exposing patients to inherent risks of implant movement, collapse, and infection. The ability to monitor implanted devices would enable faster diagnosis of failure and open the door for personalized rehabilitation therapies - both of which could vastly improve patient outcomes. Unfortunately, polymeric materials which make up most biomedical devices are not radiologically distinguishable from tissue post-implantation. The introduction of radiopaque nanoparticles into polymers allows for serial monitoring via computed tomography, without affecting device degradation. Here we demonstrate for the first time that nanoparticles do not undergo burst release from devices post-implantation and that inflammatory responses - a key determinant of device function in vivo - are also unaffected by nanoparticle addition.

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.

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.

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.

Acoustic Activation Imaging With Intravenous Perfluoropropane Nanodroplets Results in Selective Bioactivation of the Risk Area.

BACKGROUND: Acoustically activatable perfluoropropane droplets (PD) can be formulated from commercially available microbubble preparations. Diagnostic transthoracic ultrasound frequencies have resulted in acoustic activation (AA) predominately within myocardial infarct zones (IZ). OBJECTIVE: We hypothesized that the AA area following acute coronary ischemia/reperfusion (I/R) would selectively enhance the developing scar zone, and target bioeffects specifically to this region. METHODS: We administered intravenous PD in 36 rats and 20 pigs at various stages of myocardial scar formation (30 minutes, 1 day, and 7 days post I/R) to determine what effect infarct age had on the AA within the IZ. This was correlated with histology, myeloperoxidase activity, and tissue nitrite activity. RESULTS: The degree of AA within the IZ in rats was not associated with collagen content, neutrophil infiltration, or infarct age. AA within 24 hours of I/R was associated with increased nitric oxide utilization selectively within the IZ (P < .05 compared with remote zone). The spatial extent of AA in pigs correlated with infarct size only when performed before sacrifice at 7 days (r = .74, P < .01). CONCLUSIONS: Acoustic activation of intravenous PD enhances the developing scar zone following I/R, and results in selective tissue nitric oxide utilization.

Decorrelation Time Mapping as an Analysis Tool for Nanobubble-Based Contrast Enhanced Ultrasound Imaging.

Nanobubbles (NBs; ~100-500 nm diameter) are preclinical ultrasound (US) contrast agents that expand applications of contrast enhanced US (CEUS). Due to their sub-micron size, high particle density, and deformable shell, NBs in pathological states of heightened vascular permeability (e.g. in tumors) extravasate, enabling applications not possible with microbubbles (~1000-10,000 nm diameter). A method that can separate intravascular versus extravascular NB signal is needed as an imaging biomarker for improved tumor detection. We present a demonstration of decorrelation time (DT) mapping for enhanced tumor NB-CEUS imaging. In vitro models validated the sensitivity of DT to agent motion. Prostate cancer mouse models validated in vivo imaging potential and sensitivity to cancerous tissue. Our findings show that DT is inversely related to NB motion, offering enhanced detail of NB dynamics in tumors, and highlighting the heterogeneity of the tumor environment. Average DT was high in tumor regions (~9 s) compared to surrounding normal tissue (~1 s) with higher sensitivity to tumor tissue compared to other mapping techniques. Molecular NB targeting to tumors further extended DT (11 s) over non-targeted NBs (6 s), demonstrating sensitivity to NB adherence. From DT mapping of in vivo NB dynamics we demonstrate the heterogeneity of tumor tissue while quantifying extravascular NB kinetics and delineating intra-tumoral vasculature. This new NB-CEUS-based biomarker can be powerful in molecular US imaging, with improved sensitivity and specificity to diseased tissue and potential for use as an estimator of vascular permeability and the enhanced permeability and retention (EPR) effect in tumors.

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.

Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI).

PURPOSE: Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS: A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS: The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS: The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.

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.

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.

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.

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.