Mathematical models are required to estimate kinetic parameters of [1-(13)C] pyruvate-lactate interconversion from magnetic resonance spectroscopy data. One- or two-way exchange models utilizing a hypothetical approximation to the true arterial input function (AIF), (e.g. an ideal 'box-car' function) have been used previously. We present a method for direct measurement of the AIF in the rat. The hyperpolarized [1-(13)C] pyruvate signal was measured in arterial blood as it was continuously withdrawn through a small chamber. The measured signal was corrected for T1 relaxation of pyruvate, RF pulses and dispersion of blood in the chamber to allow for the estimation of the direct AIF. Using direct AIF, rather than the commonly used box-car AIF, provided realistic estimates of the rate constant of conversion of pyruvate to lactate, kpl, the rate constant of conversion of lactate to pyruvate klp, the clearance rate constant of pyruvate from blood to tissue, Kip, and the relaxation rate of lactate T1la. Since no lactate signal was present in blood, it was possible to use a simple precursor-product relationship, with the tumor tissue pyruvate time-course as the input for the lactate time-course. This provided a robust estimate of kpl, similar to that obtained using a directly measured AIF.

A major assumption in arterial spin labeling (ASL) MRI perfusion quantification is the time course of the signal on arrival in the capillary network. The normally assumed square label profile is not preserved during transit of the label through the vasculature. This change in profile can be attributed to a number of effects collectively denoted as dispersion. A number of models for this effect have been proposed, but they have been difficult to validate. In this study ASL data acquired whilst the label was still within larger arteries was used to compare models of label dispersion. Models were fit using a probabilistic algorithm and evaluated according to their ability to fit the data. Data from an elderly population were considered including both healthy controls and patients with a variety of vascular disease. The authors conclude that modeling ASL dispersion using a convolution of the ideal ASL label profile with a dispersion kernel is most appropriate, where the kernel itself takes the form of a gamma distribution. This model provided a best fit to the data considered, was consistent with the measured flow profile in arteries and was sufficiently mathematically simple to make it practical for ASL tissue perfusion quantification.

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 (< 0.1 Hz) 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.