Simon Walker-Samuel

In vivo imaging of glucose uptake and metabolism in tumors

Tumors have a greater reliance on anaerobic glycolysis for energy production than normal tissues. We developed a noninvasive method for imaging glucose uptake in vivo that is based on magnetic resonance imaging and allows the uptake of unlabeled glucose to be measured through the chemical exchange of protons between hydroxyl groups and water. This method differs from existing molecular imaging methods because it permits detection of the delivery and uptake of a metabolically active compound in physiological quantities. We show that our technique, named glucose chemical exchange saturation transfer (glucoCEST), is sensitive to tumor glucose accumulation in colorectal tumor models and can distinguish tumor types with differing metabolic characteristics and pathophysiologies. The results of this study suggest that glucoCEST has potential as a useful and cost-effective method for characterizing disease and assessing response to therapy in the clinic.

Evaluation of response to treatment using DCE-MRI: the relationship between initial area under the gadolinium curve (IAUGC) and quantitative pharmacokinetic analysis

The initial area under the gadolinium curve (IAUGC) is often used in addition to or as an alternative to parameters derived from pharmacokinetic modelling of T1-weighted dynamic contrast-enhanced (DCE) MRI data in the assessment of response to treatment of cancer. However, the physiological meaning of the IAUGC has not been rigorously defined with respect to model-based parameters. Here, simulations of DCE-MRI data were used to investigate the relationship between IAUGC and the parameters K(trans) (transfer constant), v(e) (fractional extravascular extracellular volume) and v(p) (fractional plasma volume), using two vascular input functions. It is shown that IAUGC is a mixed parameter that can display correlation with K(trans), v(e) and v(p) and ultimately has an intractable relationship with all three. Furthermore, it is demonstrated that the range over which IAUGC is taken and the nature of the vascular input function do not significantly affect this relationship.

Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI

A description of the vascular input function is needed to obtain tissue kinetic parameter estimates from dynamic contrast enhanced MRI (DCE-MRI) data. This paper describes a general modelling framework for defining compact functional forms to describe vascular input functions. By appropriately specifying the components of this model it is possible to generate models that are realistic, and that ensure that the tissue concentration curves can be analytically calculated. This means that the computations necessary to estimate parameters from measured data are relatively efficient, which is important if such methods are to become of use in clinical practice. Three models defined by four parameters, using exponential, gamma-variate and cosine descriptions of the bolus, are described and their properties investigated using simulations. The results indicate that if there is no plasma fraction, then the proposed models are indistinguishable. When a small plasma fraction is present the exponential model gives parameter estimates that are biassed by up to 50%, while the other two models give very little bias; up to 10% but less than 5% in most cases. With a larger plasma fraction the exponential model is again biassed, the gamma-variate model has a small bias, but the cosine model has a very little bias and is indistinguishable from the model used to generate the data. The computational speed of the analytic approaches is compared with a fast-Fourier-transform-based numerical convolution approach. The analytic methods are nearly 10 times faster than the numerical methods for the isolated computation of the convolution, and around 4-5 times faster when used in an optimization routine to obtain parameter estimates. These results were obtained from five example data sets, one of which was examined in more detail to compare the estimates obtained using the different models, and with literature values.

Noninvasive Quantification of Solid Tumor Microstructure Using VERDICT MRI

There is a need for biomarkers that are useful for noninvasive imaging of tumor pathophysiology and drug efficacy. Through its use of endogenous water, diffusion-weighted MRI (DW-MRI) can be used to probe local tissue architecture and structure. However, most DW-MRI studies of cancer tissues have relied on simplistic mathematical models, such as apparent diffusion coefficient (ADC) or intravoxel incoherent motion (IVIM) models, which produce equivocal results on the relation of the model parameter estimate with the underlying tissue microstructure. Here, we present a novel technique called VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) to quantify and map histologic features of tumors in vivo. VERDICT couples DW-MRI to a mathematical model of tumor tissue to access features such as cell size, vascular volume fraction, intra- and extracellular volume fractions, and pseudo-diffusivity associated with blood flow. To illustrate VERDICT, we used two tumor xenograft models of colorectal cancer with different cellular and vascular phenotypes. Our experiments visualized known differences in the tissue microstructure of each model and the significant decrease in cell volume resulting from administration of the cytotoxic drug gemcitabine, reflecting the apoptotic volume decrease. In contrast, the standard ADC and IVIM models failed to detect either of these differences. Our results illustrate the superior features of VERDICT for cancer imaging, establishing it as a noninvasive method to monitor and stratify treatment responses.

Gold–silica quantum rattles for multimodal imaging and therapy

Significance Therapeutic and diagnostic nanoparticles combine multiple functionalities to improve efficacy of treatment but often require assembling complex systems at the expense of overall performance. Here we present a simple strategy to synthesize a hybrid, rattle-like, gold–silica nanoparticle that very efficiently combines therapy and imaging in an animal model. The nanoparticle design is uniquely centered on enabling the use of gold quantum dots (<2 nm) in biological systems. The resulting nanoparticles are highly biocompatible and display emergent photonic and magnetic properties matching and in some instances outperforming state-of-the-art nanotechnology-based medical agents for each of the functionalities investigated, promising a tighter integration between imaging and therapy. Gold quantum dots exhibit distinctive optical and magnetic behaviors compared with larger gold nanoparticles. However, their unfavorable interaction with living systems and lack of stability in aqueous solvents has so far prevented their adoption in biology and medicine. Here, a simple synthetic pathway integrates gold quantum dots within a mesoporous silica shell, alongside larger gold nanoparticles within the shell’s central cavity. This “quantum rattle” structure is stable in aqueous solutions, does not elicit cell toxicity, preserves the attractive near-infrared photonics and paramagnetism of gold quantum dots, and enhances the drug-carrier performance of the silica shell. In vivo, the quantum rattles reduced tumor burden in a single course of photothermal therapy while coupling three complementary imaging modalities: near-infrared fluorescence, photoacoustic, and magnetic resonance imaging. The incorporation of gold within the quantum rattles significantly enhanced the drug-carrier performance of the silica shell. This innovative material design based on the mutually beneficial interaction of gold and silica introduces the use of gold quantum dots for imaging and therapeutic applications.

Dynamic MRI for imaging tumor microvasculature: Comparison of susceptibility and relaxivity techniques in pelvic tumors

To assess the reproducibility of intrinsic relaxivity and both relaxivity‐ and susceptibility‐based dynamic contrast enhanced (DCE) MRI in pelvic tumors; to correlate kinetic parameters obtained and to assess whether acute antivascular effects are seen in response to cisplatin‐ or taxane‐based chemotherapy.

Robust estimation of the apparent diffusion coefficient (ADC) in heterogeneous solid tumors

The least‐squares algorithm is known to bias apparent diffusion coefficient (ADC) values estimated from magnitude MR data, although this effect is commonly assumed to be negligible. In this study the effect of this bias on tumor ADC estimates was evaluated in vivo and was shown to introduce a consistent and significant underestimation of ADC, relative to those given by a robust maximum likelihood approach (on average, a 23.4 ± 12% underestimation). Monte Carlo simulations revealed that the magnitude of the bias increased with ADC and decreasing signal‐to‐noise ratio (SNR). In vivo, this resulted in a much‐reduced ability to resolve necrotic regions from surrounding viable tumor tissue compared with a maximum likelihood approach. This has significant implications for the evaluation of diffusion MR data in vivo, in particular in heterogeneous tumor tissue, when evaluating bi‐ and multiexponential tumor diffusion models for the modeling of data acquired with larger b‐values (b > 1000 s/mm2) and for data with modest SNR. Use of a robust approach to modeling magnitude MR data from tumors is therefore recommended over the least‐squares approach when evaluating data from heterogeneous tumors. Magn Reson Med, 2009.

Reference tissue quantification of DCE-MRI data without a contrast agent calibration

The quantification of dynamic contrast-enhanced (DCE) MRI data conventionally requires a conversion from signal intensity to contrast agent concentration by measuring a change in the tissue longitudinal relaxation rate, R(1). In this paper, it is shown that the use of a spoiled gradient-echo acquisition sequence (optimized so that signal intensity scales linearly with contrast agent concentration) in conjunction with a reference tissue-derived vascular input function (VIF), avoids the need for the conversion to Gd-DTPA concentration. This study evaluates how to optimize such sequences and which dynamic time-series parameters are most suitable for this type of analysis. It is shown that signal difference and relative enhancement provide useful alternatives when full contrast agent quantification cannot be achieved, but that pharmacokinetic parameters derived from both contain sources of error (such as those caused by differences between reference tissue and region of interest proton density and native T(1) values). It is shown in a rectal cancer study that these sources of uncertainty are smaller when using signal difference, compared with relative enhancement (15 +/- 4% compared with 33 +/- 4%). Both of these uncertainties are of the order of those associated with the conversion to Gd-DTPA concentration, according to literature estimates.

Reproducibility of reference tissue quantification of dynamic contrast-enhanced data: comparison with a fixed vascular input function

Reference tissues are currently used to analyse dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. The assessment of tumour response to treatment with anti-cancer drugs is a particularly important application of this type of analysis and requires a measure of reproducibility to define a level above which a significant change due to therapy can be inferred. This study compares the reproducibility of such quantification strategies with that found using a published, group-averaged uptake curve. It is shown that reference tissue quantification gives poorer reproducibility for most parameters than that found using a group-averaged plasma curve (a change in K(trans) of greater than 41.8% and 16.4% would be considered significant in the two approaches, respectively), but successfully incorporates some of the variability observed in plasma kinetics between visits and provides vascular input functions that, across the group, are comparable with the group-averaged curve. This study therefore provides an indirect validation of the methodology.

Bayesian Estimation of Changes in Transverse Relaxation Rates

Although the biasing of R*2 estimates by assuming magnitude MR data to be normally distributed has been described, the effect on changes in R*2 (ΔR*2), such as induced by a paramagnetic contrast agent, has not been reported. In this study, two versions of a novel Bayesian maximum a posteriori approach for estimating ΔR*2 are described and evaluated: one that assumes normally distributed data and the other, Rice‐distributed data. The approach enables the robust, voxelwise determination of the uncertainty in ΔR*2 estimates and provides a useful statistical framework for quantifying the probability that a pixel has been significantly enhanced. This technique was evaluated in vivo, using ultrasmall superparamagnetic iron oxide particles in orthotopic murine prostate tumors. It is shown that assuming magnitude data to be normally distributed causes ΔR*2 to be underestimated when signal‐to‐noise ratio is modest. However, the biasing effect is less than is found in R*2 estimates, implying that the simplifying assumption of normally distributed noise is more justifiable when evaluating ΔR*2 compared with when evaluating precontrast R*2 values. Magn Reson Med, 2010.