Correlation tensor magnetic resonance imaging Abstract   Diffusional Kurtosis Magnetic Resonance Imaging (DKI) quantifies the extent of nonGaussian water diffusion, which has been shown to be a very sensitive biomarker for microstructure in health and disease. However, DKI is not specific to any microstructural property per se since kurtosis may emerge from several different sources. Qspace trajectory encoding schemes have been proposed to decouple kurtosis related with the variance of mean diffusivities across different compartments (isotropic kurtosis) from kurtosis related with microscopic anisotropy (anisotropic kurtosis), however, under explicit assumptions of vanishing intracompartmental kurtosis and diffusion time independence (i.e. multiple Gaussian compartments approximation). Here, we aim to develop a more general strategy to resolve different kurtosis sources without relying on the multiple Gaussian compartments approximation. We introduce Correlation Tensor MRI (CTI) xe2x80x93 an approach exploiting the versatility of double diffusion encoding (DDE) and its sensitivity to underlying correlation tensors capable of decoupling isotropic and anisotropic kurtosis components from timedependent and intracompartmental kurtosis effects. Additionally, by subtracting these kurtosis components from the total diffusional kurtosis, CTI provides an index that is potentially sensitive to intracompartmental kurtosis. The theoretical foundations of CTI are presented and the first proofofconcept CTI ex vivo experiments are performed in mouse brains at ultrahigh field of 16.4xe2x80xafT. We find that anisotropic and isotropic kurtosis estimates can be used to decouple microscopic anisotropy from substantial partial volume effects between tissue and free water. Our intracompartmental kurtosis index exhibited positive estimates in both white and grey matter. Simulations of different synthetic microenvironments shows, however, that with the current method the estimation of intracompartmental kurtosis value is limited by higher order effects that were not taken into account in this study. CTI measurements were then extended to in vivo settings and used to map heathy rat brains at 9.4xe2x80xafT. These in vivo CTI results were found to be consistent with our ex vivo findings. Although future studies are still required to access and minimize the high order effects on the intracompartmental kurtosis index, our results show that CTIu0027s more general estimates of anisotropic and isotropic kurtosis contributions are ready to be used in future in vivo studies, which can have significant impact our understanding of the mechanisms underlying diffusion metrics extracted in health and disease.

Quasidiffusion magnetic resonance imaging (QDI): A fast, high bvalue diffusion imaging technique Abstract   To enable application of nonGaussian diffusion magnetic resonance imaging (dMRI) techniques in largescale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multishell bvalue diffusion data, QuasiDiffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1xe2x80x934xe2x80xafmin). We also describe a coherent framework for multidirectional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasidiffusion tensor imaging (QDTI) maps.  QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of nonGaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 sxe2x88x921) and the shape of the power law tail (i.e. the fractional exponent, xcexb1). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum bvalue.  We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228xe2x80xafs. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena.