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Purpose: The expanded encoding model incorporates spatially- and time-varyingfield perturbations for correction during reconstruction. So far, thesereconstructions have used the conjugate gradient method with early stoppingused as implicit regularization. However, this approach is likely suboptimalfor low-SNR cases like diffusion or high-resolution MRI. Here, we investigatethe extent that l1-wavelet regularization, or equivalently compressed sensing(CS), combined with expanded encoding improves trade-offs between spatialresolution, readout time and SNR for single-shot spiral diffusion-weightedimaging at 7T. The reconstructions were performed using our open-sourceGPU-enabled reconstruction toolbox, MatMRI, that allows inclusion of thedifferent components of the expanded encoding model, with or without CS.Methods: In vivo accelerated single-shot spirals were acquired with fiveacceleration factors (2-6) and three in-plane spatial resolutions (1.5, 1.3,and 1.1 mm). From the in vivo reconstructions, we estimated diffusion tensorsand computed fractional anisotropy maps. Then, simulations were used toquantitatively investigate and validate the impact of CS-based regularizationon image quality when compared to a known ground truth. Results: In vivoreconstructions revealed improved image quality with retainment of smallfeatures when CS was used. Simulations showed that the joint use of theexpanded encoding model and CS improves accuracy of image reconstructions(reduced mean-squared error) over the range of acceleration factorsinvestigated. Conclusion: The expanded encoding model and CS regularization arecomplementary tools for single-shot spiral diffusion MRI, which enables bothhigher spatial resolutions and higher acceleration factors.

High-resolution single-shot spiral diffusion-weighted imaging at 7T using expanded encoding with compressed sensing