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.

Keywords: BOLD; Functional MRI; Functional connectivity; Motion correction; Spin history.

Fig. 1

Schematic of slicewise motion detection algorithm, a) schematic of volumetric motion synchronized to the volume, motion part-way through volume acquisition in a sequential acquisition and motion during an interleaved acquisition, b) SLOMOCO algorithm graphic and c) SLOMOCO flowchart diagram.

Fig. 2

Injected motion timeseries, metric based on slicewise known motion, and selected portions showing performance of SLOMOCO at detecting slicewise motion. A) TRU-AVG, TRU (separated by DOF) and TD-TRU-0D. B) First selected time shows injection of x-, y-, and z-translation spikes of 1 mm on 10 independent, nonadjacent (temporal or spatial) slices. B) Second selected time shows x-, y-, and z-translation spikes on volumes. C) Injection including last z-translation and x- and y-rotations, representing all 3 critical out-of-plane DOF. Major deviation from injected motion exists almost exclusively on the 1 most inferior and the 2 most superior slices. Colors: blue = x-translation, green = y-translation, red = z-translation, cyan = xrotation, purple = y-rotation.

Fig. 3

Motion metrics for realistic (top row, motion injected on 10 nonadjacent slices) slice-wise injected motion and unrealistic (bottom row, motion injected for all slices within a volume) volume injected motion. Far left (a,e) show injected motion parameters. Top row consists of first 50 volumes of SimPACE scan shown in Fig 2 left panel and bottom row consists of last 100 volumes of same scan. A) slicewise and E) volumetric injected motion, B,F) GS, PGS, VARS and DVARS BOLD signal-based metrics. C,G) No-derivative (TD-0D, FD-0D, VTD-0D) volumetric motion parameter based motion metrics and slicewise injected motion metric (TDTRU-0D). D,H) First-derivative (TD-1D, FD-1D, VTD-1D) motion metrics and slicewise motion (0^th derivative). Dotted and colored thresholds based on literature are also shown for each metric (0.5 for BOLD signal-based metrics, 0.5 for TD, FD and 0.1 for VTD), demonstrating the failure of volumetric motion parameter-based or BOLD signal-based motion detection methods to appropriately flag slice-wise motion. Segments of volumetric injection with rotational motion impulses injected are indicated with R at bottom of G) and H). Metrics are displayed separated by individual maxima and minima. For zoomed detail and overlay, see Fig 4. Vectors were normalized and plotted spaced for visualization only.

Fig. 4

Selected segment of Fig 3, zoomed to show detail. Metrics are displayed overlain. Note in zoomed view of slicewise injection in A) individual slice timepoints can be observed, with 1mm z-translation injection occurring over 10 nonadjacent slices within volume 32, followed by xrotation injection on 10 slices in volume 36, and so on (every 4^th volume). Similarly, the zoomed view of volumetric injection in E) shows injection of 0.5 degrees z-rotation motion on all slices of volume 72, followed by -1mm z-translation on all slices of volume 76, and so on. Vectors were normalized and plotted spaced for visualization only.

Fig. 5

Detail of VTD-1D and VTD-0D, demonstrating the derivative-based metrics suffer from nearly equal levels of assigned motion on two adjacent volumes, despite the motion occurring only on one volume. Vectors were normalized and plotted spaced for visualization only.

Fig. 6

A) Pearson linear correlation versus temporal slice number (interleaved ascending acquisition: slicetime 1 = slice 1, slicetime 16 = slice 31, slicetime 31 = slice 30). B) MSE versus temporal slice number. C) Breakout of (B) by each DOF, with translations on top and rotations on bottom. The most inferior slice and the 2 most superior slices exhibit poor correlation in the presence of very strong correlation for all other slices.

Fig. 7

Performance of motion correction methods on rs-fMRI analyses in cadaver subject SimPACE data. Figure shows cadaver data corrected with VOL, VOL+ S^VOL, VOL+ S^VOX, S^VOX-SLC with SLOMOCO and S^VOX-SLC with TRU. Seed placed in posterior cingulate, each map shows InstaCorr connectivity at 0.4 correlation threshold. Timeseries plot in lower right shows selected voxel timeseries after each correction, showing the reduction in motion artifact with various motion models. Lower right corner shows same voxel location timeseries after each correction. Note the reduction in signal spikes at arbitrary motion injection timepoints every fourth volume during bulk of scan, as shown in Figure 2a.

Fig. 8

Performance of motion correction methods on rs-fMRI analyses in live subject SimPACE data. Figure shows subject data corrected for physiologic noise, then in parallel with VOL, VOL+ S^VOL TRU, VOL+ S^VOX TRU, S^VOX-SLC with SLOMOCO and S^VOX-SLC with TRU. Seed placed in posterior cingulate, each map shows InstaCorr connectivity at 0.4 correlation threshold. Timeseries plot in lower right shows selected voxel timeseries after each correction, showing the reduction in motion artifact with various motion models. Note the visual improvement of SLOMOCO retrospective correction over the best correction using the truth motion parameters (S^VOX-SLC TRU), possibly due to real subject motion that differs from the injected motion. Lower right corner shows same voxel location timeseries after each correction. Note the reduction in signal spikes at arbitrary motion injection timepoints.