Gwendolyn Van Steenkiste1, Ben Jeurissen1, Jelle Veraart1, Arnold J den Dekker1,2, Paul M Parizel3, Dirk H J Poot4,5, Jan Sijbers1. 1. iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium. 2. Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands. 3. Department of Radiology, University of Antwerp, Antwerp University Hospital, Belgium. 4. Imaging Science and Technology, Delft University of Technology, 2628 CJ Delft, The Netherlands. 5. BIGR, Department of Medical informatics and Radiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
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.
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.
Authors: Lorenz Epprecht; Elliott D Kozin; Marco Piccirelli; Vivek V Kanumuri; Osama Tarabichi; Aaron Remenschneider; Frederick G Barker; Michael J McKenna; Alexander M Huber; Marybeth E Cunnane; Katherine L Reinshagen; Daniel J Lee Journal: J Neurol Surg B Skull Base Date: 2019-03-01
Authors: Stuart D Washington; Julie Hamaide; Ben Jeurissen; Gwendolyn van Steenkiste; Toon Huysmans; Jan Sijbers; Steven Deleye; Jagmeet S Kanwal; Geert De Groof; Sayuan Liang; Johan Van Audekerke; Jeffrey J Wenstrup; Annemie Van der Linden; Susanne Radtke-Schuller; Marleen Verhoye Journal: Neuroimage Date: 2018-08-10 Impact factor: 6.556