Johnson PM, Drangova M (2019) Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Magn Reson Med 82(3):901–910
Haskell MW et al (2019) Network Accelerated Motion Estimation and Reduction (NAMER): convolutional neural network guided retrospective motion correction using a separable motion model. Magn Reson Med 82(4):1452–1461
Article PubMed PubMed Central Google Scholar
Duffy BA et al (2021) Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions. Neuroimage 230:117756
Pawar K, Chen Z, Shah NJ, Egan GF (2022) Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation. NMR Biomed 35(4):e4225
Levac B, Kumar S, Kardonik S, Tamir JI (2022) FSE compensated motion correction for MRI using data driven methods. Medical image computing and computer assisted intervention MICCAI. Springer Nature Switzerland, Cham
Zhao B, Zhou Y, Zong X (2024) Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation. Magn Reson Med 92(3):1079–1094
Zahneisen B, Keating B, Singh A, Herbst M, Ernst T (2016) Reverse retrospective motion correction. Magn Reson Med 75(6):2341–2349
Olsson H et al (2024) Simulating rigid head motion artifacts on brain magnitude MRI data–Outcome on image quality and segmentation of the cerebral cortex. PLoS ONE 19(4):e0301132
Article CAS PubMed PubMed Central Google Scholar
Preboske GM, Gunter JL, Ward CP, Jack CR (2006) Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI. Neuroimage 30(4):1196–1202
Blumenthal JD, Zijdenbos A, Molloy E, Giedd JN (2002) Motion artifact in magnetic resonance imaging: implications for automated analysis. Neuroimage 16(1):89–92
Reuter M, Tisdall MD, Qureshi A, Buckner RL, van der Kouwe AJW, Fischl B (2015) Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 107:107–115
Alexander-Bloch A et al (2016) Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI. Hum Brain Mapp 37(7):2385–2397
Article PubMed PubMed Central Google Scholar
Igata N et al (2017) Utility of real-time prospective motion correction (PROMO) for segmentation of cerebral cortex on 3D T1-weighted imaging: Voxel-based morphometry analysis for uncooperative patients. Eur Radiol 27(8):3554–3562
Savalia NK, Agres PF, Chan MY, Feczko EJ, Kennedy KM, Wig GS (2017) Motion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motion. Hum Brain Mapp 38(1):472–492
Pardoe HR, Kucharsky Hiess R, Kuzniecky R (2016) Motion and morphometry in clinical and nonclinical populations. Neuroimage 135:177–185
Ducharme S et al (2016) Trajectories of cortical thickness maturation in normal brain development: the importance of quality control procedures. Neuroimage 125:267–279
Liu S, Thung K-H, Qu L, Lin W, Shen D, Yap P-T (2021) Learning MRI artefact removal with unpaired data. Nature Mach Intell 3(1):60–67
Robert F, Dylan Tisdall M, Malte H, Bruce F, David HS, Kouwe AJWVD (2020) Scan-specific assessment of vNav motion artifact mitigation in the HCP Aging study using reverse motion correction, in In Proceedings of the 28th Annual Meeting of ISMRM. 2020: Virtual Meeting, p 0467
Liao S et al (2023) Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction. Cell Rep Med 4(7):101119
Article PubMed PubMed Central Google Scholar
Gallichan D, Marques JP, Gruetter R (2016) Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T. Magn Reson Med 75(3):1030–1039
Article CAS PubMed Google Scholar
Griswold MA et al (2002) Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 47(6):1202–1210
Fessler JA, Sutton BP (2003) Nonuniform fast fourier transforms using min-max interpolation. IEEE Trans Signal Process 51(2):560–574
Lustig M, Pauly JM (2010) SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn Reson Med 64(2):457–471
Article PubMed PubMed Central Google Scholar
Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res Int J 29:162–173
Zong X, Nanavati S, Hung SC, Li T, Lin W (2021) Effects of motion and retrospective motion correction on the visualization and quantification of perivascular spaces in ultrahigh resolution T2-weighted images at 7T. Magn Reson Med 86(4):1944–1955
Article CAS PubMed Google Scholar
Tisdall MD, Hess AT, Reuter M, Meintjes EM, Fischl B, van der Kouwe AJ (2012) Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med 68(2):389–399
Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851
Collignon MF, Delaere D, Vandermeulen D, Suetens P, Marchal G (1995) Automated multi-modality image registration based on information theory. Imaging 3(1):263–274
Bazin PL et al (2020) Sharpness in motion corrected quantitative imaging at 7T. Neuroimage 222:117227
Lee S, Jung S, Jung K-J, Kim D-H (2020) Deep learning in MR motion correction: a brief review and a new motion simulation tool (view2Dmotion). Investig Magn Reson Imaging 24(4):196
Bammer R, Aksoy M, Liu C (2007) Augmented generalized SENSE reconstruction to correct for rigid body motion. Magn Reson Med 57(1):90–102
Article PubMed PubMed Central Google Scholar
Yarach U, Stucht D, Godenschweger F, Speck O (2015) The correction of motion-induced coil sensitivity miscalibration in parallel imaging with prospective motion correction. In: International Society for Magnetic Resonance in Medicine (ISMRM)
Yarach U, Luengviriya C, Stucht D, Godenschweger F, Schulze P, Speck O (2016) Correction of B 0-induced geometric distortion variations in prospective motion correction for 7T MRI. Magn Reson Mater Phys Biol Med 29(3):319–332
Sulikowska A, Wharton S, Glover PM, Gowland PA (2014) Will field shifts due to head rotation compromise motion correction. In: International Society for Magnetic Resonance in Medicine (ISMRM)
Bammer R, Zhang B, Deng W, Wiggins GC, Stenger AV, Sodickson DK (2010) Impact of motion on parallel transmission. In: International Society for Magnetic Resonance in Medicine. (ISMRM)
Yarach U et al (2015) Correction of gradient nonlinearity artifacts in prospective motion correction for 7T MRI. Magn Reson Med 73(4):1562–1569
Comments (0)