Q-space trajectory imaging (QTI) facilitates tensor-valued diffusion encoding with variable shapes and provides more specific parameters than those available from conventional stick-shaped encoding. However, it generally requires longer echo times than conventional encoding, impacting the spatial resolution, scan time or signal-to-noise ratio. In this work, we propose a super-resolution acquisition and reconstruction approach for QTI that allows high-resolution parameter maps to be estimated from multiple low-resolution images. Using simulations and real data, we show that this does not only improve QTI’s precision, it also significantly improves its accuracy, as it avoids deleterious signal bias caused by the noise floor.
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