Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations

Abstract

Purpose Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images. Method An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation. Results Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error. Conclusion The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time.