Dataset Information

SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction.

ABSTRACT: Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume that motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression.

SUBMITTER: Beall EB

PROVIDER: S-EPMC4165749 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction.

Beall Erik B EB Lowe Mark J MJ

NeuroImage 20140624

Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume that motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with ...[more]

PMID: 24969568

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Project description:BACKGROUND:Subject motion is known to produce spurious covariance among time-series in functional connectivity that has been reported to induce distance-dependent spurious correlations. PURPOSE:To present a feasibility study for applying the extended Kalman filter (EKF) framework for high temporal resolution motion correction of resting state functional MRI (rs-fMRI) series using each simultaneous multi-slice (SMS) echo planar imaging (EPI) shot as its own navigator. STUDY TYPE:Prospective feasibility study. POPULATION/SUBJECTS:Three human volunteers. FIELD STRENGTH/SEQUENCE:3T GE DISCOVERY MR750 scanner using a 32-channel head coil. Simultaneous multi-slice rs-fMRI sequence with repetition time (TR)/echo time (TE) = 800/30 ms, and SMS factor 6. ASSESSMENT:Motion estimates were computed using two techniques: a conventional rigid-body volume-wise registration; and a high-temporal resolution motion estimation rigid-body approach. The reference image was resampled using the estimates obtained from both approaches and the difference between these predicted volumes and the original moving series was summarized using the normalized mean squared error (NMSE). STATISTICAL TESTS:Direct comparison of NMSE values. RESULTS:High-temporal motion estimation was always superior to volume-wise motion estimation for the sample presented. For staged continuous rotations, the NMSE using high-temporal resolution motion estimates ranged between [0.130, 0.150] for the first volunteer (in-plane rotations), between [0.060, 0.068] for the second volunteer (in-plane rotations), and between [0.063, 0.080] for the third volunteer (through-plane rotations). These values went up to [0.384, 0.464]; [0.136, 0.179]; and [0.080, 0.096], respectively, when using volume-wise motion estimates. DATA CONCLUSION:Accurate high-temporal rigid-body motion estimates can be obtained for rs-fMRI taking advantage of simultaneous multi-slice EPI sub-TR shots. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Project description:OBJECTIVES:In this study, we present a fully automated and robust self-navigated approach to obtain 4-dimensional (4-D) motion-resolved images during free breathing. MATERIALS AND METHODS:The proposed method, Consistently Acquired Projections for Tuned and Robust Estimation (CAPTURE), is a variant of the stack-of-stars gradient-echo sequence. A 1-D navigator was consistently acquired at a fixed azimuthal angle for all stacks of spokes to reduce nonphysiological signal contamination due to system imperfections. The resulting projections were then "tuned" using complex phase rotation to adapt to scan-to-scan variations, followed by the detection of the respiratory curve. Four-dimensional motion-corrected and uncorrected images were then reconstructed via respiratory and temporal binning, respectively.This Health Insurance Portability and Accountability Act-compliant study was performed with Institutional Review Board approval. A phantom experiment was performed using a custom-made deformable motion phantom with an adjustable frequency and amplitude. For in vivo experiments, 10 healthy participants and 12 liver tumor patients provided informed consent and were imaged with the CAPTURE sequence.Two radiologists, blinded to which images were motion-corrected and which were not, independently reviewed the images and scored the image quality using a 5-point Likert scale. RESULTS:In the respiratory motion phantom experiment, CAPTURE reversed the effects of motion blurring and restored edge sharpness from 36% to 78% of that observed in the images from the static scan.Despite large intra- and intersubject variability in respiration patterns, CAPTURE successfully detected the respiratory motion signal in all participants and significantly improved the image quality according to the subjective radiological assessments of 2 raters (P < 0.05 for both raters) with a 1 to 2-point improvement in the median Likert scores across the whole set of participants. Small lesions (<1 cm in size) which might otherwise be missed on uncorrected images because of motion blurring were more clearly depicted on the CAPTURE images. CONCLUSIONS:CAPTURE provides a robust and fully automated solution for obtaining 4-D motion-resolved images in a free-breathing setting. With its unique tuning feature, CAPTURE can adapt to large intersubject and interscan variations. CAPTURE also enables better lesion delineation because of improved image sharpness, thereby increasing the visibility of small lesions.

Project description:Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject's head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity. Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals. Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data.

Dataset Information

SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction.

Publications

SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction.

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