Jelle Veraart1,2, Els Fieremans1, Ileana O Jelescu1, Florian Knoll1, Dmitry S Novikov1. 1. Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA. 2. Department of Physics, iMinds-Vision Lab, University of Antwerp, Antwerp, Belgium.
Abstract
PURPOSE: To study and reduce the effect of Gibbs ringing artifact on computed diffusion parameters. METHODS: We reduce the ringing by extrapolating the k-space of each diffusion weighted image beyond the measured part by selecting an adequate regularization term. We evaluate several regularization terms and tune the regularization parameter to find the best compromise between anatomical accuracy of the reconstructed image and suppression of the Gibbs artifact. RESULTS: We demonstrate empirically and analytically that the Gibbs artifact, which is typically observed near sharp edges in magnetic resonance images, has a significant impact on the quantification of diffusion model parameters, even for infinitesimal diffusion weighting. We find the second order total generalized variation to be a good choice for the penalty term to regularize the extrapolation of the k-space, as it provides a parsimonious representation of images, a practically full suppression of Gibbs ringing, and the absence of staircasing artifacts typical for total variation methods. CONCLUSIONS: Regularized extrapolation of the k-space data significantly reduces truncation artifacts without compromising spatial resolution in comparison to the default option of window filtering. In particular, accuracy of estimating diffusion tensor imaging and diffusion kurtosis imaging parameters improves so much that unconstrained fits become possible. Magn Reson Med 76:301-314, 2016.
PURPOSE: To study and reduce the effect of Gibbs ringing artifact on computed diffusion parameters. METHODS: We reduce the ringing by extrapolating the k-space of each diffusion weighted image beyond the measured part by selecting an adequate regularization term. We evaluate several regularization terms and tune the regularization parameter to find the best compromise between anatomical accuracy of the reconstructed image and suppression of the Gibbs artifact. RESULTS: We demonstrate empirically and analytically that the Gibbs artifact, which is typically observed near sharp edges in magnetic resonance images, has a significant impact on the quantification of diffusion model parameters, even for infinitesimal diffusion weighting. We find the second order total generalized variation to be a good choice for the penalty term to regularize the extrapolation of the k-space, as it provides a parsimonious representation of images, a practically full suppression of Gibbs ringing, and the absence of staircasing artifacts typical for total variation methods. CONCLUSIONS: Regularized extrapolation of the k-space data significantly reduces truncation artifacts without compromising spatial resolution in comparison to the default option of window filtering. In particular, accuracy of estimating diffusion tensor imaging and diffusion kurtosis imaging parameters improves so much that unconstrained fits become possible. Magn Reson Med 76:301-314, 2016.
Authors: Ileana O Jelescu; Magdalena Zurek; Kerryanne V Winters; Jelle Veraart; Anjali Rajaratnam; Nathanael S Kim; James S Babb; Timothy M Shepherd; Dmitry S Novikov; Sungheon G Kim; Els Fieremans Journal: Neuroimage Date: 2016-02-11 Impact factor: 6.556
Authors: William M Spees; Tsen-Hsuan Lin; Peng Sun; Chunyu Song; Ajit George; Sam E Gary; Hsin-Chieh Yang; Sheng-Kwei Song Journal: Proc Natl Acad Sci U S A Date: 2018-10-08 Impact factor: 11.205
Authors: Matthew J Muckley; Benjamin Ades-Aron; Antonios Papaioannou; Gregory Lemberskiy; Eddy Solomon; Yvonne W Lui; Daniel K Sodickson; Els Fieremans; Dmitry S Novikov; Florian Knoll Journal: Magn Reson Med Date: 2020-07-14 Impact factor: 4.668
Authors: Meghann C Ryan; Peter Kochunov; Paul M Sherman; Laura M Rowland; S Andrea Wijtenburg; Ashley Acheson; L Elliot Hong; John Sladky; Stephen McGuire Journal: J Neurosci Methods Date: 2018-08-09 Impact factor: 2.390
Authors: Benjamin Ades-Aron; Jelle Veraart; Peter Kochunov; Stephen McGuire; Paul Sherman; Elias Kellner; Dmitry S Novikov; Els Fieremans Journal: Neuroimage Date: 2018-08-02 Impact factor: 6.556