Super‐resolution reconstruction of diffusion parameters from diffusion‐weighted images with different slice orientations - Gwendolyn Van Steenkiste; Ben Jeurissen; Jelle Veraart; Arnold J. den Dekker; Paul M. Parizel; Dirk H. J. Poot; Jan Sijbers (2015)
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. Magn Reson Med 75:181–195, 2016. © 2015 Wiley Periodicals, Inc.