Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer

State of the art of CT myocardial perfusion

Coronary computed tomography angiography (CCTA) is a powerful tool to rule out coronary artery disease (CAD). In the last decade, myocardial perfusion CT (CTP) technique has been developed for the evaluation of myocardial ischemia, thereby increasing positive predictive value for diagnosis of obstructive CAD. A diagnostic strategy combining CCTA and perfusion acquisitions provides both anatomical coronary evaluation and functional evaluation of the stenosis, increasing the specificity and the positive predictive value of cardiac CT. This could improve risk stratification and guide revascularization procedures, reducing unnecessary diagnostic procedures in invasive coronary angiography. Two different acquisitions protocol have been developed for CTP. Static CTP allows a qualitative or semiquantitative evaluation of myocardial perfusion using a single scan during the first pass of iodinated contrast material in the myocardium. Dynamic CTP is capable of a quantitative evaluation of perfusion through multiple acquisitions, providing direct measure of the myocardial blood flow. For both, CTP acquisition hyperemia is reached using stressor agents such as adenosine or regadenoson. CTP in addition to CCTA acquisition shows good diagnostic accuracy compared to invasive fractional flow reserve (FFR). Furthermore, the evaluation of late iodine enhancement (LIE) could be performed allowing the detection of myocardial infarction.

A method to measure renal inner medullary perfusion using MR renography

OBJECTIVE

In the kidney, the medulla is most susceptible to damage in case of hampered perfusion or oxygenation. Due to separate regulation of cortical and medullary perfusion, measurement of both is crucial to improve the understanding of renal pathophysiology. We aim to develop and evaluate a physiologically accurate model to measure renal inner medullary (Fmed) and cortical perfusion (Fcor) separately.

MATERIALS AND METHODS

We developed a 7-compartment model of renal perfusion and used an iterated approach to fit 10 free parameters. Model stability and accuracy were tested on both patient data and simulations. Cortical perfusion and FT (tubular flow or glomerular filtration rate per unit of tissue volume) were compared to a conventional 2-compartment filtration model.

RESULTS

Average (standard deviation) Fmed was 37(23)mL/100 mL/min. Fitting stability as expressed by the median (interquartile range) coefficient of variation between fits was 0.0(0.0-5.8)%, with outliers up to 81%. In simulations, Fmed was underestimated by around 8%. Intra-class correlation coefficients for Fcor and FT as measured with the 2- and 7- compartment model were 0.87 and 0.63, respectively.

DISCUSSION

We developed a pharmacokinetic model closely following renal physiology. Although the results were vulnerable for overfitting, relatively stable results could be obtained even for Fmed.

Remote Neuroinflammation in Newly Diagnosed Glioblastoma Correlates with Unfavorable Clinical Outcome

PURPOSE

Current therapy strategies still provide only limited success in the treatment of glioblastoma, the most frequent primary brain tumor in adults. In addition to the characterization of the tumor microenvironment, global changes in the brain of patients with glioblastoma have been described. However, the impact and molecular signature of neuroinflammation distant of the primary tumor site have not yet been thoroughly elucidated.

EXPERIMENTAL DESIGN

We performed translocator protein (TSPO)-PET in patients with newly diagnosed glioblastoma (n = 41), astrocytoma WHO grade 2 (n = 7), and healthy controls (n = 20) and compared TSPO-PET signals of the non-lesion (i.e., contralateral) hemisphere. Back-translation into syngeneic SB28 glioblastoma mice was used to characterize Pet alterations on a cellular level. Ultimately, multiplex gene expression analyses served to profile immune cells in remote brain.

RESULTS

Our study revealed elevated TSPO-PET signals in contralateral hemispheres of patients with newly diagnosed glioblastoma compared to healthy controls. Contralateral TSPO was associated with persisting epileptic seizures and shorter overall survival independent of the tumor phenotype. Back-translation into syngeneic glioblastoma mice pinpointed myeloid cells as the predominant source of contralateral TSPO-PET signal increases and identified a complex immune signature characterized by myeloid cell activation and immunosuppression in distant brain regions.

CONCLUSIONS

Neuroinflammation within the contralateral hemisphere can be detected with TSPO-PET imaging and associates with poor outcome in patients with newly diagnosed glioblastoma. The molecular signature of remote neuroinflammation promotes the evaluation of immunomodulatory strategies in patients with detrimental whole brain inflammation as reflected by high TSPO expression.

Noninvasive ROS imaging and drug delivery monitoring in the tumor microenvironment

Reactive oxygen species (ROS) that are overproduced in certain tumors can be considered an indicator of oxidative stress levels in the tissue. Here, we report a magnetic resonance imaging (MRI)-based probe capable of detecting ROS levels in the tumor microenvironment (TME) using ROS-responsive manganese ion (Mn2+)-chelated, biotinylated bilirubin nanoparticles (Mn@bt-BRNPs). These nanoparticles are disrupted in the presence of ROS, resulting in the release of free Mn2+, which induces T1-weighted MRI signal enhancement. Mn@BRNPs show more rapid and greater MRI signal enhancement in high ROS-producing A549 lung carcinoma cells compared with low ROS-producing DU145 prostate cancer cells. A pseudo three-compartment model devised for the ROS-reactive MRI probe enables mapping of the distribution and concentration of ROS within the tumor. Furthermore, doxorubicin-loaded, cancer-targeting ligand biotin-conjugated Dox/Mn@bt-BRNPs show considerable accumulation in A549 tumors and also effectively inhibit tumor growth without causing body weight loss, suggesting their usefulness as a new theranostic agent. Collectively, these findings suggest that Mn@bt-BRNPs could be used as an imaging probe capable of detecting ROS levels and monitoring drug delivery in the TME with potential applicability to other inflammatory diseases.

Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy

Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.

TIST-Net: style transfer in dynamic contrast enhanced MRI using spatial and temporal information

Objective.Training deep learning models for image registration or segmentation of dynamic contrast enhanced (DCE) MRI data is challenging. This is mainly due to the wide variations in contrast enhancement within and between patients. To train a model effectively, a large dataset is needed, but acquiring it is expensive and time consuming. Instead, style transfer can be used to generate new images from existing images. In this study, our objective is to develop a style transfer method that incorporates spatio-temporal information to either add or remove contrast enhancement from an existing image.Approach.We propose a temporal image-to-image style transfer network (TIST-Net), consisting of an auto-encoder combined with convolutional long short-term memory networks. This enables disentanglement of the content and style latent spaces of the time series data, using spatio-temporal information to learn and predict key structures. To generate new images, we use deformable and adaptive convolutions which allow fine grained control over the combination of the content and style latent spaces. We evaluate our method, using popular metrics and a previously proposed contrast weighted structural similarity index measure. We also perform a clinical evaluation, where experts are asked to rank images generated by multiple methods.Main Results.Our model achieves state-of-the-art performance on three datasets (kidney, prostate and uterus) achieving an SSIM of 0.91 ± 0.03, 0.73 ± 0.04, 0.88 ± 0.04 respectively when performing style transfer between a non-enhanced image and a contrast-enhanced image. Similarly, SSIM results for style transfer from a contrast-enhanced image to a non-enhanced image were 0.89 ± 0.03, 0.82 ± 0.03, 0.87 ± 0.03. In the clinical evaluation, our method was ranked consistently higher than other approaches.Significance.TIST-Net can be used to generate new DCE-MRI data from existing images. In future, this may improve models for tasks such as image registration or segmentation by allowing small training datasets to be expanded.

Identifiability of spatiotemporal tissue perfusion models

Objective.Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model.Approach.For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth.Main results.Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters.Significance.These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.

Comparison of Early Contrast Enhancement Models in Ultrafast Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Prostate Cancer

Tofts models have failed to produce reliable quantitative markers for prostate cancer. We examined the differences between prostate zones and lesion PI-RADS categories and grade group (GG) using regions of interest drawn in tumor and normal-appearing tissue for a two-compartment uptake (2CU) model (including plasma volume (vp), plasma flow (Fp), permeability surface area product (PS), plasma mean transit time (MTTp), capillary transit time (Tc), extraction fraction (E), and transfer constant (Ktrans)) and exponential (amplitude (A), arrival time (t0), and enhancement rate (α)), sigmoidal (amplitude (A0), center time relative to arrival time (A1 - T0), and slope (A2)), and empirical mathematical models, and time to peak (TTP) parameters fitted to high temporal resolution (1.695 s) DCE-MRI data. In 25 patients with 35 PI-RADS category 3 or higher tumors, we found Fp and α differed between peripheral and transition zones. Parameters Fp, MTTp, Tc, E, α, A1 - T0, and A2 and TTP all showed associations with PI-RADS categories and with GG in the PZ when normal-appearing regions were included in the non-cancer GG. PS and Ktrans were not associated with any PI-RADS category or GG. This pilot study suggests early enhancement parameters derived from ultrafast DCE-MRI may become markers of prostate cancer.

Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) can be used as a non-invasive method for the assessment of myocardial perfusion. The acquired images can be utilised to analyse the spatial extent and severity of myocardial ischaemia (regions with impaired microvascular blood flow). In the present paper, we propose a novel generalisable spatio-temporal hierarchical Bayesian model (GST-HBM) to automate the detection of ischaemic lesions and improve the in silico prediction accuracy by systematically integrating spatio-temporal context information. We present a computational inference procedure with an adequate trade-off between accuracy and computational efficiency, whereby model parameters are sampled from the posterior distribution with Gibbs sampling, while lower-level hyperparameters are selected using model selection strategies based on the Watanabe Akaike information criterion (WAIC). We have assessed our method on both synthetic (in silico) data with known gold-standard and 12 sets of clinical first-pass myocardial perfusion DCE-MRI datasets. We have also carried out a comparative performance evaluation with four established alternative methods: Gaussian mixture model (GMM), opening and closing operations based on Gaussian mixture model (GMMC&Omax), Markov random field constrained Gaussian mixture model (GMM-MRF) and model-based hierarchical Bayesian model (M-HBM). Our results show that the proposed GST-HBM method achieves much higher in silico prediction accuracy than the established alternative methods. Furthermore, this method appears to provide a more robust delineation of ischaemic lesions in datasets affected by spatially variant noise.

Perfusion MR Imaging of Liver: Principles and Clinical Applications

Perfusion imaging techniques provide quantitative characterization of tissue microvasculature. Perfusion MR of liver is particularly challenging because of dual afferent flow, need for large organ high-resolution coverage, and significant movement with respiration. The most common MR technique used for quantifying liver perfusion is dynamic contrast-enhanced MR imaging. Here, the authors describe the various perfusion MR models of the liver, the basic concepts behind implementing a perfusion acquisition, and clinical results that have been obtained using these models.

Perfusion Change of Hepatocellular Carcinoma During Atezolizumab plus Bevacizumab Treatment: A Pilot Study

PURPOSE

To investigate whether the early perfusion change in hepatocellular carcinoma (HCC) predicts the long-term therapeutic response to atezolizumab plus bevacizumab.

METHODS

We retrospectively selected 19 subjects (median age: 62 years, 4 females, and 15 males) having advanced HCC and treated with atezolizumab alone (n = 3) or in combination with bevacizumab (n = 16). The 4-phased CT or MRI imaging was performed for each subject before and at 9 ± 2 and 21 ± 5 weeks after therapy initiation. The tumor-to-liver signal ratio in the arterial phase was used to estimate the tumor perfusion. The change in tumor perfusion from the baseline to the 1st follow-up exam was correlated with the tumor response evaluated using mRECIST at the 2nd follow-up exam. The difference between favorably responding and non-responding groups was statistically analyzed using one-way ANOVA.

RESULTS

The mean tumor long axis in the baseline image was 59 ± 47 mm. The HCC perfusion changes were -26 ± 18% for complete (or partial) response (CR/PR, n = 8), -24 ± 12% for stable disease (SD, n = 8), and 9 ± 13% for progressive disease (PD, n = 3). The HCC perfusion change of the CR/PR groups was significantly lower than that of the PD group (p = 0.0040). The HCC perfusion changes between the SD and PD groups were also significantly different (p = 0.0135). The sensitivity and specificity of the early perfusion change to predict the long-term progression of the disease were 100 and 94%, respectively.

CONCLUSION

The early change in HCC perfusion may predict the long-term therapeutic response to atezolizumab plus bevacizumab, promoting personalized treatment for HCC patients.

Open source code for the generation of digital reference objects for dynamic contrast-enhanced MRI analysis software validation

OBJECTIVES

Dynamic contrast-enhanced MR images can be analyzed through the application of a wide range of kinetic models. This process is prone to variability and a lack of standardization that can affect the measured metrics. There is a need for customized digital reference objects (DROs) for the validation of DCE-MRI software packages that undertake kinetic model analysis. DROs are currently available only for a small subset of the kinetic models commonly applied to DCE-MRI data. This work aimed to address this gap.

METHODS

Code was written in the MATLAB programming environment to generate customizable DROs. This modular code allows the insertion of a plug-in to describe the kinetic model to be tested. We input our generated DROs into three commercial and open-source analysis packages and assessed the agreement of kinetic model parameter values output with the 'ground-truth' values used in the DRO generation.

RESULTS

For the five kinetic models tested, the concordance correlation coefficient values were >98%, indicating excellent agreement of the results with 'ground-truth'.

CONCLUSIONS

Testing our DROs on three independent software packages produced concordant results, strongly suggesting our DRO generation code is correct. This implies that our DROs can be used to validate other third party software for the kinetic model analysis of DCE-MRI data.

ADVANCES IN KNOWLEDGE

This work extends published work of others to allow customized generation of test objects for any applied kinetic model and allows the incorporation of B1 mapping into the DRO for application at higher field strengths.

Blood-brain barrier damage and new onset refractory status epilepticus: An exploratory study using dynamic contrast-enhanced magnetic resonance imaging

OBJECTIVE

This study was undertaken to characterize the blood-brain barrier (BBB) dysfunction in patients with new onset refractory status epilepticus (NORSE) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS

This study included three groups of adult participants: patients with NORSE, encephalitis patients without status epilepticus (SE), and healthy subjects. These participants were retrospectively included from a prospective DCE-MRI database of neurocritically ill patients and healthy subjects. The BBB permeability (Ktrans) in the hippocampus, basal ganglia, thalamus, claustrum, periventricular white matter, and cerebellum were measured and compared between these three groups.

RESULTS

A total of seven patients with NORSE, 14 encephalitis patients without SE, and nine healthy subjects were included in this study. Among seven patients with NORSE, only one had a definite etiology (autoimmune encephalitis), and the rest were cryptogenic. Etiology of encephalitis patients without SE included viral (n = 2), bacterial (n = 8), tuberculous (n = 1), cryptococcal (n = 1), and cryptic (n = 2) encephalitis. Of these 14 encephalitis patients without SE, three patients had seizures. Compared to healthy controls, NORSE patients had significantly increased Ktrans values in the hippocampus (.73 vs. .02 × 10^-3 /min, p = .001) and basal ganglia (.61 vs. .003 × 10^-3 /min, p = .007) and a trend in the thalamus (.24 vs. .08 × 10^-3 /min, p = .017). Compared to encephalitis patients without SE, NORSE patients had significantly increased Ktrans values in the thalamus (.24 vs. .01 × 10^-3 /min, p = .002) and basal ganglia (.61 vs. .004 × 10^-3 /min, p = .013).

SIGNIFICANCE

This exploratory study demonstrates that BBBs of NORSE patients were impaired diffusely, and BBB dysfunction in the basal ganglia and thalamus plays an important role in the pathophysiology of NORSE.

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.

Absolute iodine concentration for dynamic perfusion imaging of the myocardium: improved detection of poststenotic ischaemic in a 3D-printed dynamic heart phantom

Background

To investigate the detection capabilities of myocardial perfusion defects of dual-energy computed tomography (CT) technology using time-resolved iodine-based maps for functional assessment of coronary stenosis in a dynamic heart phantom.

Methods

An anatomical heart model was designed using a three-dimensional (3D) printing technique. The lumen of the right coronary artery was reduced to 25% of the original areal cross-section. Scans were acquired with a 64-slice dual-layer CT equipment using a perfusion protocol with 36 time points. For distinguishing haemodynamically affected from unaffected myocardial regions, conventional and spectral mean transit time (MTT) parameter maps were compared. A dose reduction technique was simulated by using a subset of time points of the time attenuation curves (TACs).

Results

The tracer kinetic modeling showed decreased errors on fit parameters from conventional to spectral TACs (42% reduction for A and 40% for λ). Three characteristic regions (highly, moderately, and not affected by the simulated stenosis) can be distinguished in all spectral perfusion maps. The best distinction was observed on MTT maps. An area under the curve (AUC) value of 1.00 for the voxel-wise differentiation of haemodynamically affected tissue was achieved versus a 0.89 AUC for conventional MTT maps. By temporal under-sampling, a dose reduction of approximately 78% from 19 to 4.3 mSv was achieved with a 0.96 AUC.

Conclusion

Dual-energy CT can provide time-resolved iodine density data, which enables the calculation of absolute quantitative perfusion maps with decreased fitting errors, improving the accuracy for poststenotic myocardial ischaemic detection in a 3D-printed heart phantom.

End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT

(1) Background: CT perfusion (CTP) is used to quantify cerebral hypoperfusion in acute ischemic stroke. Conventional attenuation curve analysis is not standardized and might require input from expert users, hampering clinical application. This study aims to bypass conventional tracer-kinetic analysis with an end-to-end deep learning model to directly categorize patients by stroke core volume from raw, slice-reduced CTP data. (2) Methods: In this retrospective analysis, we included patients with acute ischemic stroke due to proximal occlusion of the anterior circulation who underwent CTP imaging. A novel convolutional neural network was implemented to extract spatial and temporal features from time-resolved imaging data. In a classification task, the network categorized patients into small or large core. In ten-fold cross-validation, the network was repeatedly trained, evaluated, and tested, using the area under the receiver operating characteristic curve (ROC-AUC). A final model was created in an ensemble approach and independently validated on an external dataset. (3) Results: 217 patients were included in the training cohort and 23 patients in the independent test cohort. Median core volume was 32.4 mL and was used as threshold value for the binary classification task. Model performance yielded a mean (SD) ROC-AUC of 0.72 (0.10) for the test folds. External independent validation resulted in an ensembled mean ROC-AUC of 0.61. (4) Conclusions: In this proof-of-concept study, the proposed end-to-end deep learning approach bypasses conventional perfusion analysis and allows to predict dichotomized infarction core volume solely from slice-reduced CTP images without underlying tracer kinetic assumptions. Further studies can easily extend to additional clinically relevant endpoints.

Physics-informed neural networks for myocardial perfusion MRI quantification

Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters. These neural networks can be trained to fit the observed perfusion MR data while respecting the underlying physical conservation laws described by a multi-compartment exchange model. Here, we provide a framework for the implementation of PINNs in myocardial perfusion MR. The approach is validated both in silico and in vivo. In the in silico study, an overall decrease in mean-squared error with the ground-truth parameters was observed compared to a standard non-linear least squares fitting approach. The in vivo study demonstrates that the method produces parameter values comparable to those previously found in literature, as well as providing parameter maps which match the clinical diagnosis of patients.

Differential Spatial Distribution of TSPO or Amino Acid PET Signal and MRI Contrast Enhancement in Gliomas

Simple Summary

Radiotracers targeting the translocator protein (TSPO) have recently gained substantial interest, since TSPO is overexpressed in malignant gliomas, where it correlates inversely with patient’s survival. The high-affinity TSPO PET ligand [¹⁸F]GE180 was found to depict tumor areas with a remarkably high contrast and has been shown to provide non-invasive information on histological tumor grades. Yet, its significance was questioned with the argument, that the high contrast may solely arise from nonspecific accumulation in tissue supplied by leaky vessels. This study aimed to address this question by providing a detailed evaluation of spatial associations between TSPO and amino acid PET with relative contrast enhancement in T1-weighted MRI. The results show that [¹⁸F]GE180 contrast does not reflect a disrupted blood–brain barrier (BBB) only and that multi-modal imaging generates complementary information, which may better depict spatial heterogeneity of tumor biology and may be used to individualize the therapy for each patient.

Abstract

In this study, dual PET and contrast enhanced MRI were combined to investigate their correlation per voxel in patients at initial diagnosis with suspected glioblastoma. Correlation with contrast enhancement (CE) as an indicator of BBB leakage was further used to evaluate whether PET signal is likely caused by BBB disruption alone, or rather attributable to specific binding after BBB passage. PET images with [¹⁸F]GE180 and the amino acid [¹⁸F]FET were acquired and normalized to healthy background (tumor-to-background ratio, TBR). Contrast enhanced images were normalized voxel by voxel with the pre-contrast T1-weighted MRI to generate relative CE values (rCE). Voxel-wise analysis revealed a high PET signal even within the sub-volumes without detectable CE. No to moderate correlation of rCE with TBR voxel-values and a small overlap as well as a larger distance of the hotspots delineated in rCE and TBR-PET images were detected. In contrast, voxel-wise correlation between both PET modalities was strong for most patients and hotspots showed a moderate overlap and distance. The high PET signal in tumor sub-volumes without CE observed in voxel-wise analysis as well as the discordant hotspots emphasize the specificity of the PET signals and the relevance of combined differential information from dual PET and MRI images.

Comparative analysis of two mathematical algorithms for the calculation of computed tomography perfusion parameters in the healthy and diseased pancreas

BACKGROUND

The maximum slope (MS) and deconvolution (DC) algorithms are commonly used to post-process computed tomography perfusion (CTP) data. This study aims to analyze the differences between MS and DC algorithms for the calculation of pancreatic CTP parameters.

METHODS

The pancreatic CTP data of 57 patients were analyzed using MS and DC algorithms. Two blinded radiologists calculated pancreatic blood volume (BV) and blood flow (BF). Interobserver correlation coefficients were used to evaluate the consistency between two radiologists. Paired t-tests, Pearson linear correlation analysis, and Bland-Altman analysis were performed to evaluate the correlation and consistency of the CTP parameters between the two algorithms.

RESULTS

Among the 30 subjects with normal pancreas, the BV values in the three pancreatic regions were higher in the case of the MS algorithm than in the case of the DC algorithm (t = 39.35, p < 0.001), and the BF values in the three pancreatic regions were slightly higher for the MS algorithm than for the DC algorithm (t = 2.19, p = 0.031). Similarly, among the 27 patients with acute pancreatitis, the BV values obtained using the MS methods were higher than those obtained using the DC methods (t = 54.14, p < 0.001). Furthermore, the BF values were higher with the MS methods than the DC methods (t = 8.45, p < 0.001). Besides, Pearson linear correlation and Bland-Altman analysis showed that the BF and BV values showed a good correlation and a bad consistency between the two algorithms.

CONCLUSIONS

The BF and BV values measured using MS and DC algorithms had a good correlation but were not consistent.

Markers of human endometrial hypoxia can be detected in vivo and ex vivo during physiological menstruation

STUDY QUESTION

Can markers of human endometrial hypoxia be detected at menstruation in vivo?

SUMMARY ANSWER

Our in vivo data support the presence of hypoxia in menstrual endometrium of women during physiological menstruation.

WHAT IS KNOWN ALREADY

Current evidence from animal models and human in vitro studies suggests endometrial hypoxia is present at menstruation and drives endometrial repair post menses. However, detection of human endometrial hypoxia in vivo remains elusive.

STUDY DESIGN, SIZE, DURATION

We performed a prospective case study of 16 women with normal menstrual bleeding.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Reproductively aged female participants with a regular menstrual cycle underwent objective measurement of their menstrual blood loss using the alkaline haematin method to confirm a loss of <80 ml per cycle. Exclusion criteria were exogenous hormone use, an intrauterine device, endometriosis or fibroids >3 cm. Participants attended for two MRI scans; during days 1-3 of menstruation and the early/mid-secretory phase of their cycle. The MRI protocol included dynamic contrast-enhanced MRI and T2* quantification. At each visit, an endometrial sample was also collected and hypoxia-regulated repair factor mRNA levels (ADM, VEGFA, CXCR4) were quantified by RT-qPCR.

MAIN RESULTS AND THE ROLE OF CHANCE

Women had reduced T2* during menstrual scans versus non-menstrual scans (P = 0.005), consistent with menstrual hypoxia. Plasma flow (Fp) was increased at menstruation compared to the non-menstrual phase (P = 0.0005). Laboratory findings revealed increased ADM, VEGF-A and CXCR4 at menstruation on examination of paired endometrial biopsies from the menstrual and non-menstrual phase (P = 0.008; P = 0.03; P = 0.009). There was a significant correlation between T2* and these ex vivo hypoxic markers (P < 0.05).

LIMITATIONS, REASONS FOR CAUTION

This study examined the in vivo detection of endometrial hypoxic markers at specific timepoints in the menstrual cycle in women with a menstrual blood loss <80 ml/cycle and without significant uterine structural abnormalities. Further research is required to determine the presence of endometrial hypoxia in those experiencing abnormal uterine bleeding with and without fibroids/adenomyosis.

WIDER IMPLICATIONS OF THE FINDINGS

Heavy menstrual bleeding (HMB) is a common, debilitating condition. Understanding menstrual physiology may improve therapeutics. To our knowledge, this is the first in vivo data supporting the presence of menstrual hypoxia in the endometrium of women with normal menstrual bleeding. If aberrant in those with HMB, these non-invasive tests may aid diagnosis and facilitate personalized treatments for HMB.

STUDY FUNDING/COMPETING INTEREST(S)

This work was funded by Wellbeing of Women grant RG1820, Wellcome Trust Fellowship 209589/Z/17/Z and undertaken in the MRC Centre for Reproductive Health, funded by grants G1002033 and MR/N022556/1. H.O.D.C. has clinical research support for laboratory consumables and staff from Bayer AG and provides consultancy advice (but with no personal remuneration) for Bayer AG, PregLem SA, Gedeon Richter, Vifor Pharma UK Ltd, AbbVie Inc; Myovant Sciences GmbH. H.O.D.C. receives royalties from UpToDate for articles on abnormal uterine bleeding.

TRIAL REGISTRATION NUMBER

N/A.

A receptor-binding radiopharmaceutical for imaging of traumatic brain injury in a rodent model: [99mTc]Tc-tilmanocept

INTRODUCTION

Blood-brain barrier (BBB) disruption and subsequent neuro-inflammation occur following traumatic brain injury (TBI), resulting in a spectrum of human nervous system disorders. [99mTc]Tc-tilmanocept is a receptor-binding radiopharmaceutical FDA-approved for sentinel lymph node mapping. We hypothesize that after an intravenous (i.v.) injection, [99mTc]Tc-tilmanocept, will traverse a disrupted BBB and bind to CD206-bearing microglial cells.

METHODS

Age-matched mice were divided into three groups: 5-days post TBI (n = 4), and 5-days post sham (n = 4), and naïve controls (n = 4). IRDye800CW-labeled [99mTc]Tc-tilmanocept (0.15 nmol per gram body weight) and FITC-labeled bovine serum albumin (FITC-BSA) were injected (i.v.) into each mouse. Mice were imaged with a high-resolution gamma camera for 45 min. Immediately after imaging, the brains were perfused with fixative, excised, imaged with a fluorescence scanner, assayed for radioactivity, and prepared for histology.

RESULTS

In vivo nuclear imaging, ex vivo fluorescence imaging, ex vivo gamma well counting, and histo-microscopy demonstrated enhanced tilmanocept uptake in the TBI region. The normalized [99mTc]Tc-tilmanocept uptake value from nuclear imaging and the maximum pixel intensity from fluorescence imaging of the TBI group (1.12 ± 0.12 and 2288 ± 278 a.u., respectively) were significantly (P < 0.04) higher than the sham group (0.64 ± 0.28 and 1708 ± 101 a.u., respectively) and the naive group (0.76 ± 0.24 and 1643 ± 391 a.u., respectively). The mean [99mTc]Tc-tilmanocept scaled uptake in the TBI brains (0.058 ± 0.013%/g) was significantly (P < 0.010) higher than the scaled brain uptake of the sham group (0.031 ± 0.011%/g) and higher (P = 0.04) than the uptake of the naïve group (0.020 ± 0.002%/g). Fluorescence microscopy demonstrated increased uptake of the IRDye800CW-tilmanocept and FITC-BSA in the TBI brain regions.

CONCLUSION

[99mTc]Tc-tilmanocept traverses disrupted blood-brain barrier and localizes within the injured region. ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT CARE: [99mTc]Tc-tilmanocept could serve as an imaging biomarker for TBI-associated neuroinflammation and any disease process that involves a disruption of the blood-brain barrier.

Modeling dynamic radial contrast enhanced MRI with linear time invariant systems for motion correction in quantitative assessment of kidney function

Early identification of kidney function deterioration is essential to determine which newborn patients with congenital kidney disease should be considered for surgical intervention as opposed to observation. Kidney function can be measured by fitting a tracer kinetic (TK) model onto a series of Dynamic Contrast Enhanced (DCE) MR images and estimating the filtration rate parameter from the model. Unfortunately, breathing and large bulk motion events due to patient movement in the scanner create outliers and misalignments that introduce large errors in the TK model parameter estimates even when using a motion-robust dynamic radial VIBE sequence for DCE-MR imaging. The misalignments between the series of volumes are difficult to correct using standard registration due to 1) the large differences in geometry and contrast between volumes of the dynamic sequence and 2) the requirement of fast dynamic imaging to achieve high temporal resolution and motion deteriorates image quality. These difficulties reduce the accuracy and stability of registration over the dynamic sequence. An alternative registration approach is to generate noise and motion free templates of the original data from the TK model and use them to register each volume to its contrast-matched template. However, the TK models used to characterize DCE-MRI are tissue specific, non-linear and sensitive to the same motion and sampling artifacts that hinder registration in the first place. Hence, these can only be applied to register accurately pre-segmented regions of interest, such as kidneys, and might converge to local minima under the presence of large artifacts. Here we introduce a novel linear time invariant (LTI) model to characterize DCE-MR data for different tissue types within a volume. We approximate the LTI model as a sparse sum of first order LTI functions to introduce robustness to motion and sampling artifacts. Hence, this model is well suited for registration of the entire field of view of DCE-MR data with artifacts and outliers. We incorporate this LTI model into a registration framework and evaluate it on both synthetic data and data from 20 children. For each subject, we reconstructed the sequence of DCE-MR images, detected corrupted volumes acquired during motion, aligned the sequence of volumes and recovered the corrupted volumes using the LTI model. The results show that our approach correctly aligned the volumes, provided the most stable registration in time and improved the tracer kinetic model fit.

Associations between histogram analysis parameters derived from dynamic-contrast enhanced MRI and PD L1-expression in head and neck squamous cell carcinomas. A preliminary study

PURPOSE

Programmed death 1 ligand (PD-L 1) plays an essential role in oncology. It might be crucial to predict its expression non-invasively by imaging. Dynamic-contrast enhanced MRI (DCE MRI) is one of the important imaging modalities in head and neck squamous cell cancer (HNSCC). The aim of the present study was to analyze possible associations between histogram analysis parameters of DCE MRI and PD-L 1 expression in HNSCC METHODS: Overall, 26 patients with primary HNSCC of different localizations were involved in the study. DCE MRI was obtained on a 3 T MRI and analyzed with a whole lesion measurement using a histogram approach. PD-L 1 expression was estimated on bioptic samples before any form of treatment using 3 scores (Tumor positive score (TPS), Immune cell score (ICS) and Combined positive score (CPS)).

RESULTS

CPS correlated with mode derived from Ktrans (r = 0.40, p = .04). Also CPS correlated with P90 derived from Kep (r = 0.40, p = .04). ICS correlated with the maximum derived from Kep (r = 0.41, p = .03) and entropy derived from Kep (r = 0.43, p = .02). There were no associations between DCE MRI parameters and TPS.

CONCLUSION

Ktrans and Kep related histogram analysis parameters derived from DCE MRI correlated moderately with PD-L 1 expression of immune cells in HNSCC.

Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.

Computed tomography for myocardial characterization in ischemic heart disease: a state-of-the-art review

This review provides an overview of the currently available computed tomography (CT) techniques for myocardial tissue characterization in ischemic heart disease, including CT perfusion and late iodine enhancement. CT myocardial perfusion imaging can be performed with static and dynamic protocols for the detection of ischemia and infarction using either single- or dual-energy CT modes. Late iodine enhancement may be used for the analysis of myocardial infarction. The accuracy of these CT techniques is highly dependent on the imaging protocol, including acquisition timing and contrast administration. Additionally, the options for qualitative and quantitative analysis and the accuracy of each technique are discussed.

Quantification of heterogeneity to classify benign parotid tumors: a feasibility study on most frequent histotypes

Aim: To differentiate Warthin tumors (WTs) and pleomorphic adenomas (PAs) measuring heterogeneity of intravoxel incoherent motion (IVIM) and dynamic-contrast enhanced-magnetic resonance imaging biomarkers. Methods: Volumes of interest were traced on 18 WT and 18 PA in 25 patients. For each IVIM and dynamic-contrast enhanced biomarker, histogram parameters were calculated and then compared using the Wilcoxon-signed-rank test. Receiver operating characteristic curves and multivariate analysis were employed to identify the parameters and their pairs with the best accuracy. Results: Most of the biomarkers exhibited significant difference (p < 0.05) between PA and WT for histogram parameters. Time to peak median and skewness, and D* median and entropy showed the highest area under the curve. No meaningful improvement of accuracy was obtained using two features. Conclusion: IVIM and dynamic-contrast enhanced histogram descriptors may help in the classification of WT and PA.

Feasibility of using dynamic contrast-enhanced MRI for differentiating thymic carcinoma from thymic lymphoma based on semi-quantitative and quantitative models

AIM

To evaluate the value of using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived parameters to differentiate thymic carcinoma and thymic lymphoma based on semi-quantitative and quantitative models.

MATERIALS AND METHODS

Twenty-nine pathologically confirmed anterior mediastinum tumours in 29 patients were enrolled in this retrospective study, including 15 thymic carcinoma and 14 lymphoma patients. All the patients underwent pre-treatment mediastinum DCE-MRI. Both semi-quantitative and quantitative parameters were calculated and the volume transfer constant K^trans, the flux rate constant between extravascular extracellular space and plasma k_ep, the extravascular extracellular volume fraction v_e were obtained based on a modified Tofts model. DCE-MRI derived parameters were compared between thymic carcinoma and thymic lymphoma groups.

RESULTS

Thymic carcinoma had significantly lower k_ep (p=0.040) and higher v_e (p=0.018) than thymic lymphoma; however, there were no significant differences on K^trans and semi-quantitative parameters between the two groups. v_e had the highest area under the curve (cut-off value, 0.282; area under the curve, 0.748; sensitivity, 71.4%; specificity, 80%). The combination of k_ep and v_e could increase the diagnostic performance significantly (area under the curve, 0.752; sensitivity, 57.1%; specificity, 93.3%).

CONCLUSION

DCE-MRI derived parameters may have value in the differentiating thymic carcinoma and thymic lymphoma.

Rheumatoid cervical pannus: feasibility of volume and perfusion quantification using dynamic contrast enhanced time resolved MRI

Background

Dynamic contrast-enhanced magnetic resonance imaging has the potential to show disease activity of rheumatoid arthritis even in complex anatomic areas as the atlantodental region.

Purpose

To demonstrate the technical feasibility of measuring synovial volume and perfusion characteristics with dynamic contrast-enhanced magnetic resonance imaging of the atlantodental region in patients with rheumatoid arthritis.

Material and Methods

Ten patients with rheumatoid arthritis and cervical spine involvement underwent dynamic contrast-enhanced magnetic resonance imaging of the cervical spine at 1.5 T. For each patient, 80 3D datasets were acquired using the commercialized Time Resolved Imaging of Contrast KineticS (TRICKS) sequence. Volumes of synovia with active synovitis on anatomical and parametric images were segmented. Synovial tissue perfusion parameters, namely plasma flow (Fp), relative plasma volume (vp), and the permeability-surface area product (PS), were calculated using a two-compartment uptake model. Statistical analysis included calculation of intra- and inter-reader agreement and a correlation of perfusion parameters with Outcome Measures in Rheumatology Clinical Trials (OMERACT) criteria.

Results

Dynamic contrast-enhanced magnetic resonance imaging as well as quantification of volume and perfusion characteristics of synovia was successful in most patients (80%). Intra- and inter-reader agreement was excellent (0.89–0.99). There was a positive correlation between OMERACT score and the permeability-surface product.

Conclusion

Dynamic contrast-enhanced magnetic resonance imaging using a 4D angiography sequence for the atlantodental region in patients with rheumatoid arthritis for quantitative and qualitative assessment of synovial volume and perfusion characteristics is technically feasible.

Hierarchical Bayesian myocardial perfusion quantification

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Quantitative MRI for Assessment of Treatment Outcomes in a Rabbit VX2 Hepatic Tumor Model

Globally, primary and secondary liver cancer is one of the most common cancer types, accounting 8.2% of deaths worldwide in 2018. One of the key strategies to improve the patient's prognosis is the early diagnosis, when liver function is still preserved. In hepatocellular carcinoma (HCC), the typical wash-in/wash-out pattern in conventional magnetic resonance imaging (MRI) reaches a sensitivity of 60% and specificity of 96-100%. However, in recent years functional MRI sequences such as hepatocellular-specific gadolinium-based dynamic-contrast enhanced MRI, diffusion-weighted imaging (DWI), and magnetic resonance spectroscopy (MRS) have been demonstrated to improve the evaluation of treatment success and thus the therapeutic decision-making and the patient's outcome. In the preclinical research setting, the VX2 liver rabbit tumor, which once originated from a virus-induced anaplastic squamous cell carcinoma, has played a longstanding role in experimental interventional oncology. Especially the high tumor vascularity allows assessing the treatment response of locoregional interventions such as radiofrequency ablation (RFA) and transcatheter arterial embolization (TACE). Functional MRI has been used to monitor the tumor growth and viability following interventional treatment. Besides promising results, a comprehensive overview of functional MRI sequences used so far in different treatment setting is lacking, thus lowering the comparability of study results. This review offers a comprehensive overview of study protocols, results, and limitations of quantitative MRI sequences applied to evaluate the treatment outcome of VX2 hepatic tumor models, thus generating a unique basis for future MRI studies and potential translation into the clinical setting. Level of Evidence: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2019. J. Magn. Reson. Imaging 2020;52:668-685.

Evaluating differential nanoparticle accumulation and retention kinetics in a mouse model of traumatic brain injury via Ktrans mapping with MRI

Traumatic brain injury (TBI) is a leading cause of injury-related death worldwide, yet there are no approved neuroprotective therapies that improve neurological outcome post-injury. Transient opening of the blood-brain barrier following injury provides an opportunity for passive accumulation of intravenously administered nanoparticles through an enhanced permeation and retention-like effect. However, a thorough understanding of physicochemical properties that promote optimal uptake and retention kinetics in TBI is still needed. In this study, we present a robust method for magnetic resonance imaging of nanoparticle uptake and retention kinetics following intravenous injection in a controlled cortical impact mouse model of TBI. Three contrast-enhancing nanoparticles with different hydrodynamic sizes and relaxivity properties were compared. Accumulation and retention were monitored by modelling the permeability coefficient, K^trans, for each nanoparticle within the reproducible mouse model. Quantification of K^trans for different nanoparticles allowed for non-invasive, multi-time point assessment of both accumulation and retention kinetics in the injured tissue. Using this method, we found that 80 nm poly(lactic-co-glycolic acid) nanoparticles had maximal K^trans in a TBI when injected 3 hours post-injury, showing significantly higher accumulation kinetics than the small molecule, Gd-DTPA. This robust method will enable optimization of administration time and nanoparticle physicochemical properties to achieve maximum delivery.

Bayesian pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging: validation and application

Tracer-kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging data is commonly performed with the well-known Tofts model and nonlinear least squares (NLLS) regression. This approach yields point estimates of model parameters, uncertainty of these estimates can be assessed e.g. by an additional bootstrapping analysis. Here, we present a Bayesian probabilistic modeling approach for tracer-kinetic analysis with a Tofts model, which yields posterior probability distributions of perfusion parameters and therefore promises a robust and information-enriched alternative based on a framework of probability distributions. In this manuscript, we use the quantitative imaging biomarkers alliance (QIBA) Tofts phantom to evaluate the Bayesian tofts model (BTM) against a bootstrapped NLLS approach. Furthermore, we demonstrate how Bayesian posterior probability distributions can be employed to assess treatment response in a breast cancer DCE-MRI dataset using Cohen's d. Accuracy and precision of the BTM posterior distributions were validated and found to be in good agreement with the NLLS approaches, and assessment of therapy response with respect to uncertainty in parameter estimates was found to be excellent. In conclusion, the Bayesian modeling approach provides an elegant means to determine uncertainty via posterior distributions within a single step and provides honest information about changes in parameter estimates.

Extended quantitative dynamic contrast-enhanced cardiac perfusion imaging in mice using accelerated data acquisition and spatially distributed, two-compartment exchange modeling

The objective of the present work was to improve data acquisition and quantification of dynamic contrast-enhanced perfusion imaging in the in vivo murine heart. Four-fold undersampled data were acquired in 14 mice and reconstructed using k-t SPARSE. A two-compartment exchange model was employed to provide additional characterization of myocardial tissue based on compartment volumes and the permeability surface area product. The feasibility of the proposed method was tested using compartment-based analysis of contrast-enhanced perfusion data acquired with intravascular and extracellular contrast agents. A significantly different permeability surface area product was measured for the intravascular versus extracellular contrast agent (0.13-0.15 ml/g/min vs 0.86-0.88 ml/g/min). The reduced extravasation also resulted in significantly smaller interstitial volumes of the intravascular versus extracellular agent (9.8-11% vs 45-47%). No difference was found for myocardial blood flow (6.5-7.2 ml/g/min vs 6.0-7.0 ml/g/min). The results presented here show that two-compartment exchange modeling in the in vivo murine heart is feasible and gives access to tissue parameters beyond myocardial blood flow.

A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model

A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.

Indirect methods for improving parameter estimation of PET kinetic models

PURPOSE

Parametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel-wise image-driven time-activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images.

METHODS

Three different approaches related to noise reduction and advanced curve fitting algorithm are used in the proposed framework. First, dynamic PET images are denoised using a kernel-based denoising method and the highly constrained backprojection technique. Second, gradient-free curve fitting algorithms are exploited to improve the accuracy and precision of parameter estimates. Third, a kernel-based post-filtering method is applied to parametric images to further improve the quality of parametric images. Computer simulations were performed to evaluate the performance of the proposed framework.

RESULTS AND CONCLUSIONS

The simulation results showed that when compared to the Gaussian filtering, the proposed denoising method could provide better PET image quality, and consequentially improve the quality and quantitative accuracy of parametric images. In addition, gradient-free optimization algorithms (i.e., pattern search) can result in better parametric images than the gradient-based curve fitting algorithm (i.e., trust-region-reflective). Finally, our results showed that the proposed kernel-based post-filtering method could further improve the precision of parameter estimates while maintaining the accuracy of parameter estimates.

Feasibility of quantitative MR-perfusion imaging to monitor treatment response after uterine artery embolization (UAE) in symptomatic uterus fibroids

INTRODUCTION

In 25% of women, symptomatic uterus myomas are diagnosed with clinical and functional impairment ranging from abdominal and pelvic pain to dys- and hypermenorrhea, dyspareunia, pollakiuria and infertility. Women undergoing a treatment increasingly prefer nowadays minimal invasive, uterus preserving therapies like uterine artery embolization (UAE) over surgical hysterectomy, nowadays. To emphasize the efficacy of UAE as a uterus preserving treatment with targeted therapy of myomas only, analysis of tissue perfusion pre and post embolization is required. The purpose of this study was to assess treatment response in UAE in females with symptomatic uterus myomas by quantitative magnetic resonance perfusion imaging.

METHODS

Seven females scheduled for uterus myoma embolization underwent three MRI examinations (pre, post, follow-up) including morphological and dynamic contrast enhanced perfusion imaging at 3 T. To measure tumor volume, regions-of-interest covering the tumor and the uterus were drawn by two readers in consensus. Blood flow, blood volume, and mean transit time were calculated by a pixel-by-pixel deconvolution approach. Kruskal-Wallis/Friedman test was employed to test whether the group medians differ significantly with correction for multiple comparisons using Bonferroni method.

RESULTS

Change of volume could be observed in all patients after embolization but was significantly different only between pre/post and follow-up time point. Measured differences in all perfusion parameters were significant between pre-intervention and post-intervention/follow-up in the myomas, no significant differences could be detected for the uterus tissue.

CONCLUSIONS

Our results demonstrate devascularization of symptomatic myomas which correlates with cessation of hypermenorrhea in all treated patients without affecting healthy uterus tissue. Supplementing UAE with perfusion imaging to monitor early treatment response is feasible and might provide valuable information for the follow-up of patients and contribute to providing confidence for the patients in treatment success.

MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI

Background

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow.

Results

We present a framework for medical image fitting tasks that is included in the Medical Imaging Interaction Toolkit MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth.

Conclusions

Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.

Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences

Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed.

Isolated Perfused Rat Livers to Quantify the Pharmacokinetics and Concentrations of Gd-BOPTA

With recent advances in liver imaging, the estimation of liver concentrations is now possible following the injection of hepatobiliary contrast agents and radiotracers. However, how these images are generated remains partially unknown. Most experiments that would be helpful to increase this understanding cannot be performed in vivo. For these reasons, we investigated the liver distribution of the magnetic resonance (MR) contrast agent gadobenate dimeglumine (Gd-BOPTA, MultiHance®, Bracco Imaging) in isolated perfused rat livers (IPRLs). In IPRL, we developed a new set up that quantifies simultaneously the Gd-BOPTA compartment concentrations and the transfer rates between these compartments. Concentrations were measured either by MR signal intensity or by count rates when the contrast agent was labelled by [¹⁵³Gd]. With this experimental model, we show how the Gd-BOPTA hepatocyte concentrations are modified by temperature and liver flow rates. We define new pharmacokinetic parameters to quantify the canalicular transport of Gd-BOPTA. Finally, we present how transfer rates generate Gd-BOPTA concentrations in rat liver compartments. These findings better explain how liver imaging with hepatobiliary radiotracers and contrast agents is generated and improve the image interpretation by clinicians.

Imaging techniques to study drug transporter function in vivo

Transporter systems involved in the permeation of drugs and solutes across biological membranes are recognized as key determinants of pharmacokinetics. Typically, the action of membrane transporters on drug exposure to tissues in living organisms is inferred from invasive procedures, which cannot be applied in humans. In recent years, imaging methods have greatly progressed in terms of instruments, synthesis of novel imaging probes as well as tools for data analysis. Imaging allows pharmacokinetic parameters in different tissues and organs to be obtained in a non-invasive or minimally invasive way. The aim of this overview is to summarize the current status in the field of molecular imaging of drug transporters. The overview is focused on human studies, both for the characterization of transport systems for imaging agents as well as for the determination of drug pharmacokinetics, and makes reference to animal studies where necessary. We conclude that despite certain methodological limitations, imaging has a great potential to study transporters at work in humans and that imaging will become an important tool, not only in drug development but also in medicine. Imaging allows the mechanistic aspects of transport proteins to be studied, as well as elucidating the influence of genetic background, pathophysiological states and drug-drug interactions on the function of transporters involved in the disposition of drugs.

VEGF pathway targeting agents, vessel normalization and tumor drug uptake: from bench to bedside

Vascular endothelial growth factor (VEGF) pathway targeting agents have been combined with other anticancer drugs, leading to improved efficacy in carcinoma of the cervix, stomach, lung, colon and rectum, ovary, and breast. Vessel normalization induced by VEGF pathway targeting agents influences tumor drug uptake. Following bevacizumab treatment, preclinical and clinical studies have shown a decrease in tumor delivery of radiolabeled antibodies and two chemotherapeutic drugs. The decrease in vessel pore size during vessel normalization might explain the decrease in tumor drug uptake. Moreover, the addition of bevacizumab to cetuximab, or panitumumab in colorectal cancer patients or to trastuzumab in breast cancer patients, did not improve efficacy. However, combining bevacizumab with chemotherapy did increase efficacy in some cancer types. Novel biomarkers to select patients who may benefit from combination therapies, such as the effect of an angiogenesis inhibitor on tumor perfusion, requires innovative trial designs and large clinical trials. Small imaging studies with radiolabeled drugs could be used in the interphase to gain further insight into the interplay between VEGF targeted therapy, vessel normalization and tumor drug delivery.

MRI Study on the Changes of Bone Marrow Microvascular Permeability and Fat Content after Total-Body X-Ray Irradiation

In this study, we investigated microvascular perfusion status, changes to fat content and fatty acid composition in the bone marrow of rat femurs after total-body irradiation by quantitative permeability parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ex vivo high-resolution magic angle spinning (HRMAS) ¹H nuclear magnetic resonance spectroscopy (NMRS). Thirty-six Sprague-Dawley rats were randomly assigned to either an irradiated or nonirradiated control group. Permeability imaging using DCE-MRI and HRMAS ¹H NMRS was performed before irradiation, as well as at days 4 and 7 postirradiation. The volume transfer constant (K^trans) values increased to 2.219 ± 0.418/min ( P < 0.01) at day 4 and to 2.760 ± 0.217/min at day 7 ( P < 0.01) postirradiation. The plasma fraction (v_p) values gradually decreased. The proportion of (n-6) polyunsaturated fatty acids (PUFA) gradually reached a peak at day 7, the proportion of (n-3) PUFA gradually decreased and the proportion of saturated fatty acids gradually increased. After irradiation, K^trans at different times showed significant negative correlation with (n-3) PUFA ( r = -0.6393, P < 0.01) and significant positive correlation with (n-6) PUFA ( r = 0.6841, P < 0.05). These findings indicate that bone marrow microcirculation perfusion and vascular permeability correlated with fat content at an early time point after irradiation. A pathophysiological mechanism may exist based on fat-vascular permeability in the case of injury to bone marrow microcirculation.

Chapter 8 On the Horizon: Advanced Imaging Techniques to Improve Noninvasive Assessment of Cervical Lymph Nodes

Conventional imaging modalities are limited in the evaluation of lymph nodes as they predominantly rely on size and morphology, which have suboptimal sensitivity and specificity for malignancy. In this review we will explore the role of "on the horizon" advanced imaging modalities that can look beyond the size and morphologic features of a cervical lymph node and explore its molecular nature and can aid in personalizing therapy rather than use the "one-size-fits-all" approach.

Comparison of perfusion models for quantitative T1 weighted DCE-MRI of rectal cancer

In this work, the two compartment exchange model and two compartment uptake model were applied to obtain quantitative perfusion parameters in rectum carcinoma and the results were compared to those obtained by the deconvolution algorithm. Eighteen patients with newly diagnosed rectal carcinoma underwent 3 T MRI of the pelvis including a T₁ weighted dynamic contrastenhanced (DCE) protocol before treatment. Mean values for Plasma Flow (PF), Plasma Volume (PV) and Mean Transit Time (MTT) were obtained for all three approaches and visualized in parameter cards. For the two compartment models, Akaike Information Criterion (AIC) and [Formula: see text] were calculated. Perfusion parameters determined with the compartment models show results in accordance with previous studies focusing on rectal cancer DCE-CT (PF_2CX = 68 ± 44 ml/100 ml/min, PF_2CU = 55 ± 36 ml/100 ml/min) with similar fit quality (AIC:169 ± 81/179 ± 77, [Formula: see text]:10 ± 12/9 ± 10). Values for PF are overestimated whereas PV and MTT are underestimated compared to results of the deconvolution algorithm. Significant differences were found among all models for perfusion parameters as well as between the AIC and [Formula: see text] values. Quantitative perfusion parameters are dependent on the chosen tracer kinetic model. According to the obtained parameters, all approaches seem capable of providing quantitative perfusion values in DCE-MRI of rectal cancer.