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Correlation tensor imaging (CTI) is a new diffusion MRI framework thatutilises double diffusion encoding (DDE) to resolve isotropic, anisotropic andmicroscopic kurtosis sources. Microscopic kurtosis in CTI is provided by thecontrast between SDE and parallel DDE signals at the same b-value.Multi-Gaussian exchange (MGE) is a diffusion MRI framework that utilises DDE tomeasure exchange. The highest exchange sensitivity in MGE is obtained bycontrasting SDE and DDE signals at the same b-value. CTI and MGE can thus beapplied to analyse the same data but provide conflicting interpretations ofthat data. We perform Monte Carlo simulations in different geometries withvarying levels of exchange to determine which approach is more compatible withthe data. Simulations reveal that in all microstructures considered, CTImicroscopic kurtosis drastically increases when exchange is introduced.Furthermore, in microstructures that are well-described by the multi-Gaussianassumption, CTI-estimated microscopic kurtosis increases with both the exchangerate and the mixing time, despite fulfilment of the long-mixing-time conditionof CTI. Increasing the exchange rate by a factor of 2 positively biases CTImicroscopic kurtosis by approximately the same factor. At a modest exchangerate of 10 /s, varying the mixing time from 12 to 100 ms increases CTImicroscopic kurtosis by at least a factor of 3. To address this problem, wepropose a heuristic approach to combine CTI and MGE to estimateintra-compartmental kurtosis unconfounded by exchange and demonstrate itsfeasibility using numerical simulations.

Diffusional exchange versus microscopic kurtosis from CTI: two conflicting interpretations of the same data