Abstract
Functional MRI acquisition is sensitive to subjects' motion that cannot be fully constrained. Therefore, signal corrections have to be applied a posteriori in order to mitigate the complex interactions between changing tissue localization and magnetic fields, gradients and readouts. To circumvent current preprocessing strategies limitations, we developed an integrated method that correct motion and spatial low-frequency intensity fluctuations at the level of each slice in order to better fit the acquisition processes. The registration of single or multiple simultaneously acquired slices is achieved online by an Iterated Extended Kalman Filter, favoring the robust estimation of continuous motion, while an intensity bias field is non-parametrically fitted. The proposed extraction of gray-matter BOLD activity from the acquisition space to an anatomical group template space, taking into account distortions, better preserves fine-scale patterns of activity. Importantly, the proposed unified framework generalizes to high-resolution multi-slice techniques. When tested on simulated and real data the latter shows a reduction of motion explained variance and signal variability when compared to the conventional preprocessing approach. These improvements provide more stable patterns of activity, facilitating investigation of cerebral information representation in healthy and/or clinical populations where motion is known to impact fine-scale data.
Highlights
Functional MRI with echo-planar-imaging (EPI) is widely used in neuroscience research to indirectly measure brain activity through the brain-oxygen-level-dependent (BOLD) signal
To model continuously the subjects’ head changing position, we chose an Iterated Extended Kalman Filter (IEKF); a derivation of standard Kalman Filter that takes into account the nonlinearity of MR signal changes described above through local derivation and iterative optimization
We evaluated the root-mean-squared error (RMSE) between the estimated and simulated motion and intensity bias in brain mask (Figure 3) in all single-slice sequence simulations
Summary
Functional MRI (fMRI) with echo-planar-imaging (EPI) is widely used in neuroscience research to indirectly measure brain activity through the brain-oxygen-level-dependent (BOLD) signal. Integrated fMRI Slicewise Correction Framework air and subjects’ tissues or bones, which when moved in the B0 field modulate these artifacts The latter artifacts as well as physiological noise are even more evident at high fields (e.g., 3 or 7 Tesla) (Triantafyllou et al, 2005; Bollmann et al, 2017) mitigating the benefit in signal sensitivity. Related to these issues is the fact that numerous innovations in pulse acquisition sequences, and simultaneous multi-slice imaging (SMS) (Feinberg and Setsompop, 2013) in particular, have rapidly been adopted in leading research initiatives such as the Human Connectome Project (HCP) (Ugurbil et al, 2013; Van Essen et al, 2013). The increase of in-plane resolution extends the readout echo train and concerns have been raised about a heightened sensitivity to motion and respiration induced B0 in-homogeneity fluctuations under those conditions
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