Prospective acquisition correction for head motion with image‐based tracking for real‐time fMRI

By S. Thesen, O. Heid, E. Mueller, Lothar Rudi Schad. Magnetic Resonance in Medicine, 2000

612 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b0484f2ddd69a76f0dfdbd6803527d55cb8895a0

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion (2011, neutral): “On-line scanner corrections for head motion also did not ameliorate this effect (Thesen et al., 2000) (data not shown).”
Volumetric Navigators (vNavs) for Prospective Motion Correction and Selective Reacquisition in Neuroanatomical MRI (2011, methodology): “In its traditional functional MRI application, PACE has a lag of two TRs because while the data from TR i is being registered, the subsequent TR (i + 1) is being measured, so the correction from TR i is only available at the beginning of TR (i + 2) (5).”
Head motion during MRI acquisition reduces gray matter volume and thickness estimates (2015, neutral): “This limit is enforced by Siemens’ PACE motion-tracking system [28], which the vNavs system is based upon”
Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) (2004, background): “Moreover, prospective motion correction techniques can further reduce artifactual feedback signals by continuously tracking the brain with the measured volume [18].”
Functional MRI of Language in Aphasia: A Review of the Literature and the Methodological Challenges (2007, methodology): “Although techniques have been developed for “real-time” prospective motion correction during image acquisition (e.g., Thesen et al., 2000, Ward et al., 2000), as well as “real-time auto-shimming” for mitigation of regional magnetic field inhomogeneities (Ward et al., 2002), all these techniques…”
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration (2018, positive): “Real-time pose estimation and registration has broader potential applications such as guided and automated ultrasonography [28], automated fetal MRI [26], [30], and motion-robust MRI [31]–[35].”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “…during fMRI acquisitions, although most modern scanners include a method for semi-prospective head motion correction for fMRI that applies volumetric registration shortly after the acquisition of the volumes and corrects the current axes of the acquisition coordinate system (Thesen et al., 2000).”
Real-time fMRI and its application to neurofeedback (2012, methodology): “The retrospective correction of motion was complemented by different prospective correction methods (Thesen et al., 2000; Zaitsev et al., 2006).”
The Influence of Head Motion on Intrinsic Functional Connectivity MRI (2011, methodology): “, 2010), (2) prospectively correct for motion during data acquisition (Thesen et al., 2000; Ward et al., 2000), (3) post-hoc eliminate images or epochs where motion is evident (Power et al.”
Prospective motion correction in brain imaging: A review (2013, neutral): “for correction of motion during fMRI (17).”