Kinetic modelling of dissolution dynamic nuclear polarisation 13 C magnetic resonance spectroscopy data for analysis of pyruvate delivery and fate in tumours.

Dissolution dynamic nuclear polarisation (dDNP) of 13 C-labelled pyruvate in magnetic resonance spectroscopy/imaging (MRS/MRSI) has the potential for monitoring tumour progression and treatment response. Pyruvate delivery, its metabolism to lactate and efflux were investigated in rat P22 sarcomas following simultaneous intravenous administration of hyperpolarised 13 C-labelled pyruvate (13 C1 -pyruvate) and urea (13 C-urea), a nonmetabolised marker. A general mathematical model of pyruvate-lactate exchange, incorporating an arterial input function (AIF), enabled the losses of pyruvate and lactate from tumour to be estimated, in addition to the clearance rate of pyruvate signal from blood into tumour, Kip , and the forward and reverse fractional rate constants for pyruvate-lactate signal exchange, kpl and klp . An analogous model was developed for urea, enabling estimation of urea tumour losses and the blood clearance parameter, Kiu . A spectral fitting procedure to blood time-course data proved superior to assuming a gamma-variate form for the AIFs. Mean arterial blood pressure marginally correlated with clearance rates. Kiu equalled Kip , indicating equivalent permeability of the tumour vasculature to urea and pyruvate. Fractional loss rate constants due to effluxes of pyruvate, lactate and urea from tumour tissue into blood (kpo , klo and kuo , respectively) indicated that T1 s and the average flip angle, θ, obtained from arterial blood were poor surrogates for these parameters in tumour tissue. A precursor-product model, using the tumour pyruvate signal time-course as the input for the corresponding lactate signal time-course, was modified to account for the observed delay between them. The corresponding fractional rate constant, kavail , most likely reflected heterogeneous tumour microcirculation. Loss parameters, estimated from this model with different TRs, provided a lower limit on the estimates of tumour T1 for lactate and urea. The results do not support use of hyperpolarised urea for providing information on the tumour microcirculation over and above what can be obtained from pyruvate alone. The results also highlight the need for rigorous processes controlling signal quantitation, if absolute estimations of biological parameters are required.

Optimizing Data for Modeling Neuronal Responses.

In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol-and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.

Physiological basis of vascular autocalibration (VasA): Comparison to hypercapnia calibration methods.

PURPOSE: The statistical power of functional MRI (fMRI) group studies is significantly hampered by high intersubject spatial and magnitude variance. We recently presented a vascular autocalibration method (VasA) to account for vascularization differences between subjects and hence improve the sensitivity in group studies. Here, we validate the novel calibration method by means of direct comparisons of VasA with more established measures of baseline venous blood volume (and indirectly vascular reactivity), the M-value. METHODS: Seven healthy volunteers participated in two 7 T (T) fMRI experiments to compare M-values with VasA estimates: (i) a hypercapnia experiment to estimate voxelwise M-value maps, and (ii) an fMRI experiment using visual stimulation to estimate voxelwise VasA maps. RESULTS: We show that VasA and M-value calibration maps show the same spatial profile, providing strong evidence that VasA is driven by local variations in vascular reactivity as reflected in the M-value. CONCLUSION: The agreement of vascular reactivity maps obtained with VasA when compared with M-value maps confirms empirically the hypothesis that the VasA method is an adequate tool to account for variations in fMRI response amplitudes caused by vascular reactivity differences in healthy volunteers. VasA can therefore directly account for them and increase the statistical power of group studies. The VasA toolbox is available as a statistical parametric mapping (SPM) toolbox, facilitating its general application. Magn Reson Med, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: A pilot study.

The blood oxygenation level-dependent (BOLD) signal is widely used for functional magnetic resonance imaging (fMRI) of brain function in health and disease. The statistical power of fMRI group studies is significantly hampered by high inter-subject variance due to differences in baseline vascular physiology. Several methods have been proposed to account for physiological vascularization differences between subjects and hence improve the sensitivity in group studies. However, these methods require the acquisition of additional reference scans (such as a full resting-state fMRI session or ASL-based calibrated BOLD). We present a vascular autorescaling (VasA) method, which does not require any additional reference scans. VasA is based on the observation that slow oscillations (<0.1Hz) in arterial blood CO2 levels occur naturally due to changes in respiration patterns. These oscillations yield fMRI signal changes whose amplitudes reflect the blood oxygenation levels and underlying local vascularization and vascular responsivity. VasA estimates proxies of the amplitude of these CO2-driven oscillations directly from the residuals of task-related fMRI data without the need for reference scans. The estimates are used to scale the amplitude of task-related fMRI responses, to account for vascular differences. The VasA maps compared well to cerebrovascular reactivity (CVR) maps and cerebral blood volume maps based on vascular space occupancy (VASO) measurements in four volunteers, speaking to the physiological vascular basis of VasA. VasA was validated in a wide variety of tasks in 138 volunteers. VasA increased t-scores by up to 30% in specific brain areas such as the visual cortex. The number of activated voxels was increased by up to 200% in brain areas such as the orbital frontal cortex while still controlling the nominal false-positive rate. VasA fMRI outperformed previously proposed rescaling approaches based on resting-state fMRI data and can be readily applied to any task-related fMRI data set, even retrospectively.

Measurement of the acute metabolic response to hypoxia in rat tumours in vivo using magnetic resonance spectroscopy and hyperpolarised pyruvate.

PURPOSE: To estimate the rate constant for pyruvate to lactate conversion in tumours in response to a hypoxic challenge, using hyperpolarised (13)C1-pyruvate and magnetic resonance spectroscopy. METHODS AND MATERIALS: Hypoxic inspired gas was used to manipulate rat P22 fibrosarcoma oxygen tension (pO2), confirmed by luminescence decay of oxygen-sensitive probes. Hyperpolarised (13)C1-pyruvate was injected into the femoral vein of anaesthetised rats and slice-localised (13)C magnetic resonance (MR) spectra acquired. Spectral integral versus time curves for pyruvate and lactate were fitted to a precursor-product model to estimate the rate constant for tumour conversion of pyruvate to lactate (kpl). Mean arterial blood pressure (MABP) and oxygen tension (ArtpO2) were monitored. Pyruvate and lactate concentrations were measured in freeze-clamped tumours. RESULTS: MABP, ArtpO2 and tumour pO2 decreased significantly during hypoxia. kpl increased significantly (p<0.01) from 0.029±0.002s(-1) to 0.049±0.006s(-1) (mean±SEM) when animals breathing air were switched to hypoxic conditions, whereas pyruvate and lactate concentrations were minimally affected by hypoxia. Both ArtpO2 and MABP influenced the estimate of kpl, with a strong negative correlation between kpl and the product of ArtpO2 and MABP under hypoxia. CONCLUSION: The rate constant for pyruvate to lactate conversion, kpl, responds significantly to a rapid reduction in tumour oxygenation.

A system for accurate and automated injection of hyperpolarized substrate with minimal dead time and scalable volumes over a large range.

Over recent years hyperpolarization by dissolution dynamic nuclear polarization has become an established technique for studying metabolism in vivo in animal models. Temporal signal plots obtained from the injected metabolite and daughter products, e.g. pyruvate and lactate, can be fitted to compartmental models to estimate kinetic rate constants. Modeling and physiological parameter estimation can be made more robust by consistent and reproducible injections through automation. An injection system previously developed by us was limited in the injectable volume to between 0.6 and 2.4ml and injection was delayed due to a required syringe filling step. An improved MR-compatible injector system has been developed that measures the pH of injected substrate, uses flow control to reduce dead volume within the injection cannula and can be operated over a larger volume range. The delay time to injection has been minimized by removing the syringe filling step by use of a peristaltic pump. For 100µl to 10.000ml, the volume range typically used for mice to rabbits, the average delivered volume was 97.8% of the demand volume. The standard deviation of delivered volumes was 7µl for 100µl and 20µl for 10.000ml demand volumes (mean S.D. was 9 ul in this range). In three repeat injections through a fixed 0.96mm O.D. tube the coefficient of variation for the area under the curve was 2%. For in vivo injections of hyperpolarized pyruvate in tumor-bearing rats, signal was first detected in the input femoral vein cannula at 3-4s post-injection trigger signal and at 9-12s in tumor tissue. The pH of the injected pyruvate was 7.1±0.3 (mean±S.D., n=10). For small injection volumes, e.g. less than 100µl, the internal diameter of the tubing contained within the peristaltic pump could be reduced to improve accuracy. Larger injection volumes are limited only by the size of the receiving vessel connected to the pump.

Modeling the effects of flow dispersion in arterial spin labeling.

Recent experimental results have shown that effects such as dispersion and cardiac pulsation have a significant effect on the arterial spin labeling (ASL) signal. These have not been incorporated into the existing ASL models potentially leading to inaccuracies in flow calculation. In this study, we develop a new model, based on physical principles, to model the transit of the ASL signal from the tagging band to the imaging band using the mass transport equation. We relax the assumption of a uniform plug flow, and account for the dispersion caused by the viscous nature of blood. The model also provides a framework within which other physiological aspects can easily be examined. Here, we examine the effects of flow dispersion on the ASL signal, and hence the quantification of cerebral perfusion. Our results suggest that not accounting for flow dispersion may result in inaccurate values of cerebral perfusion.