Tumors display a greater reliance on glycolysis for energy production than normal tissues. We have developed a non-invasive method for imaging glucose uptake in vivo, which is based on magnetic resonance imaging, and allows the uptake of non-labeled glucose to be measured via the chemical exchange of protons between hydroxyl groups and water. This method differs from existing molecular imaging methods, as it permits detection of the delivery and uptake of a metabolically active compound at 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 pathophysiology. 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.

Increased hyperphosphorylated tau and the formation of intracellular neurofibrillary tangles are associated with the loss of neurons and cognitive decline in Alzheimer's disease, and related neurodegenerative conditions. We applied two diffusion models, diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), to in vivo diffusion magnetic resonance images (dMRI) of a mouse model of human tauopathy (rTg4510) at 8.5 months of age. In grey matter regions with the highest degree of tau burden, microstructural indices provided by both NODDI and DTI discriminated the rTg4510 (TG) animals from wild type (WT) controls; however only the neurite density index (NDI) (the volume fraction that comprises axons or dendrites) from the NODDI model correlated with the histological measurements of the levels of hyperphosphorylated tau protein. Reductions in diffusion directionality were observed when implementing both models in the white matter region of the corpus callosum, with lower fractional anisotropy (DTI) and higher orientation dispersion (NODDI) observed in the TG animals. In comparison to DTI, histological measures of tau pathology were more closely correlated with NODDI parameters in this region. This in vivo dMRI study demonstrates that NODDI identifies potential tissue sources contributing to DTI indices and NODDI may provide greater specificity to pathology in Alzheimer's disease.

As the number of people diagnosed with Alzheimer's disease (AD) reaches epidemic proportions, there is an urgent need to develop effective treatment strategies to tackle the social and economic costs of this fatal condition. Dozens of candidate therapeutics are currently being tested in clinical trials, and compounds targeting the aberrant accumulation of tau proteins into neurofibrillary tangles (NFTs) are the focus of substantial current interest. Reliable, translatable biomarkers sensitive to both tau pathology and its modulation by treatment along with animal models that faithfully reflect aspects of the human disease are urgently required. Magnetic resonance imaging (MRI) is well established as a valuable tool for monitoring the structural brain changes that accompany AD progression. However the descent into dementia is not defined by macroscopic brain matter loss alone: non-invasive imaging measurements sensitive to protein accumulation, white matter integrity and cerebral haemodynamics probe distinct aspects of AD pathophysiology and may serve as superior biomarkers for assessing drug efficacy. Here we employ a multi-parametric array of five translatable MRI techniques to characterise the in vivo pathophysiological phenotype of the rTg4510 mouse model of tauopathy (structural imaging, diffusion tensor imaging (DTI), arterial spin labelling (ASL), chemical exchange saturation transfer (CEST) and glucose CEST). Tau-induced pathological changes included grey matter atrophy, increased radial diffusivity in the white matter, decreased amide proton transfer and hyperperfusion. We demonstrate that the above markers unambiguously discriminate between the transgenic group and age-matched controls and provide a comprehensive profile of the multifaceted neuropathological processes underlying the rTg4510 model. Furthermore, we show that ASL and DTI techniques offer heightened sensitivity to processes believed to precede detectable structural changes and, as such, provides a platform for the study of disease mechanisms and therapeutic intervention.

No abstract

High-field, pre-clinical MRI systems are widely used to characterise tissue structure and volume in small animals, using high resolution imaging. Both applications rely heavily on the consistent, accurate calibration of imaging gradients, yet such calibrations are typically only performed during maintenance sessions by equipment manufacturers, and potentially with acceptance limits that are inadequate for phenotyping. To overcome this difficulty, we present a protocol for gradient calibration quality assurance testing, based on a 3D-printed, open source, structural phantom that can be customised to the dimensions of individual scanners and RF coils. In trials on a 9.4 T system, the gradient scaling errors were reduced by an order of magnitude, and displacements of greater than 100 µm, caused by gradient non-linearity, were corrected using a post-processing technique. The step-by-step protocol can be integrated into routine pre-clinical MRI quality assurance to measure and correct for these errors. We suggest that this type of quality assurance is essential for robust pre-clinical MRI experiments that rely on accurate imaging gradients, including small animal phenotyping and diffusion MR.