Comparison of prospective and retrospective motion correction in 3D‐encoded neuroanatomical MRI

To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients. Theory and Methods: Gradient switching during simultaneous EEG-fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high-impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators. Results: Head-pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]-mm translation, [0.30 0.27 0.22]-degree rotation).Retrospective correction of 3D gradient-echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with Cap-Track pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees). Conclusion:Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Tracking of rigid head motion duringMRIusing an EEGsystem

Laustsen

Andersen

Xue

et al. 2022

“…This experiment was conducted on the first subject on both a 3 T and 7 T scanner. Additional sequence parameters were at 3 T, 1.50 × 1.741.74 mm 3 For each scan session on both scanners, coil array sensitivity profiles used in the reconstructions were estimated from a separate low-resolution (6 × 6 × 6 mm 3 ) reference scan using a custom implementation of the ESPIRiT algorithm. 33…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Prospective techniques have shown to be advantageous in terms of spin history and k-space sampling density, but the need for extra hardware limits these methods in clinical workflow. 3 Retrospective methods, on the other hand, can be applied to a variety of sequences and do not require any type of additional hardware. The aligned sensitivity encoding (SENSE) framework 4 achieves intrascan motion estimation and correction for volumetric anatomical imaging by temporally subdividing k-space samples and accounting for different motion states in each segment.…”

Section: Introductionmentioning

confidence: 99%

“…Prospective and retrospective motion‐correcting schemes have been proposed that enable motion‐tolerant imaging. Prospective techniques have shown to be advantageous in terms of spin history and k‐space sampling density, but the need for extra hardware limits these methods in clinical workflow 3 . Retrospective methods, on the other hand, can be applied to a variety of sequences and do not require any type of additional hardware.…”

Section: Introductionmentioning

confidence: 99%

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Data‐driven motion‐corrected brain MRI incorporating pose‐dependent B₀ fields

Brackenier

Cordero‐Grande

Tomi‐Tricot

et al. 2022

To develop a fully data-driven retrospective intrascan motion-correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose-dependent changes in polarizing magnetic (B 0 ) fields. Theory and Methods: Tissue susceptibility induces spatially varying B 0 distributions in the head, which change with pose. A physics-inspired B 0 model has been deployed to model the B 0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B 0 variations from appropriately acquired data without requiring navigators. The effect on data-driven motion correction is validated in simulations and in vivo. Results:The applicability of the physics-inspired B 0 model was confirmed in vivo. Simulations show the need to include the pose-dependent B 0 fields in the reconstruction to improve motion-correction performance and the feasibility of estimating B 0 evolution from the acquired data. The proposed motion and B 0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. Conclusion:We have developed a motion-correction framework that accounts for and estimates pose-dependent B 0 fields. The method improves current state-of-the-art data-driven motion-correction techniques when B 0 dependencies cannot be neglected. The use of a compact physics-inspired B 0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data-driven approach.

Navigator‐based slice tracking for kidney pCASL using spin‐echo EPI acquisition

Zhang

Triphan

Ziener

et al. 2023

Purpose To apply a navigator‐based slice‐tracking method to prospectively compensate respiratory motion for kidney pseudo‐continuous arterial spin labeling (pCASL), using spin‐echo (SE) EPI acquisition. Methods A single gradient‐echo slice selection and projection readout at the location of the diaphragm along the inferior–superior direction was applied as a navigator. Navigator acquisition and fat suppression were inserted before each transverse imaging slice of the readouts of a 2D‐SE‐EPI‐based pCASL sequence. Motion information was calculated after exclusion of the signal saturation in the navigator signal caused by EPI excitations. The motion information was then used to directly adjust the slice positioning in real time. Results The respiratory motion from the navigator signal was calculated, and slice positioning was changed in real time based on the motion information. We could show that motion compensation reduces kidney movement, and that the coefficients of variation across renal perfusion values were significantly reduced when motion correction was applied. The average reduction of coefficients of variation was approximately 20%, resulting in a more accurate and detailed structure of the respective perfusion maps. Conclusions This study demonstrates the feasibility of a navigator‐based slice‐tracking technique in kidney imaging with a SE‐EPI readout pCASL sequence to reduce kidney motion.