Short-TR BOLD fMRI studies have increased in popularity due to the advancement of simultaneous multi-slice (SMS) techniques and the many advantages afforded by rapid sampling. However, in rapid imaging of a single slice, disruptions to the steady-state from physiological sources can result in large temporal variability in long-T2 tissues, primarily around cerebrospinal fluid. We investigated the sources of this variability and how they translate from single-slice to whole-brain SMS-based fMRI.
The popularity of BOLD fMRI studies using short repetition time (TR <1 s) has steadily risen over the past several years as high sampling rates can increase statistical power,1 improve the estimation and removal of physiological noise,2,3 and enable the detection of high-frequency neural activity.4-6 As TR is decreased and becomes comparable to tissue T2, inadvertent steady-state free precession (SSFP) can be induced and the measured signal becomes a combination of the fresh FID and echoes of the transverse magnetization arising from earlier RF pulses. It has been shown that disruptions of the SSFP during rapid BOLD imaging induce significant fluctuations in tissues with long T2s, such as cerebrospinal fluid (CSF), and that these fluctuations can be reduced by applying a constant gradient spoiler after the readout;7 however, to achieve a sufficiently short TR, the acquisition was limited to a single slice. Respiratory-induced B0 fluctuations were proposed as the source of the SSFP disruption7 and the spoiler attenuated the echo formation through diffusion-weighting.8 As CSF bathes the cortex, gradient spoiling could, therefore, be an attractive method to prospectively decrease physiological noise around gray matter in fast fMRI. However, the signal variability reported in the above study may have been due to inflow effects, which were not considered, and these findings may not be as pronounced in contiguous multi-slice imaging due to i) the reduction of inflowing spins’ longitudinal magnetization from neighbouring slices’ excitations, and ii) increased “intrinsic spoiling” contributed by all other slices’ imaging gradients. Here, we investigate the role of gradient spoiling, intrinsic spoiling, and inflow on the temporal stability of rapid BOLD fMRI using single-slice, multiple-slice, and SMS imaging.
Four healthy volunteers (3 female, age=25±2-years) were scanned on a Siemens MAGNETOM Prisma 3 T system (Siemens Heathcare, Erlangen, Germany) using the manufacturer’s 32-channel head coil. All scans were based on a modification of Siemens’s product BOLD SMS-EPI sequence whereby spoiling was incorporated by adding a 24 mT/m gradient to induce dephasing by an amount Φ along the slice-direction after each readout. All scans used an isotropic resolution of 2.4 mm, 39° flip angle, TE=31 ms, and 700 repetitions.
Experiment 1: To reproduce the earlier observations,7 scans with a single excitation per repetition (“single-RF-per-repetition”) were performed with and without gradient spoiling. This included single-slice imaging and SMS=5, 5-slice imaging with a 500% slice gap. Using single-RF-per-repetition eliminated intrinsic spoiling from neighbouring slices’ excitations/readouts, and the slice gap ensured that inflow was unaffected. For these experiments, TR=450 ms, and Φ=0 or 60π (12-ms duration).
Experiment 2: To determine the impact of spoiling on the temporal stability of a typical SMS acquisition, subjects were scanned with an SMS factor of 5 or 6, acquiring 25 or 30 slices, respectively, no slice gap, TR = 400 or 450 ms, and spoiling Φ=0, 30π or 60π.
Experiment 3: To isolate the “intrinsic spoiling” imparted by the imaging gradients of other slices, the number of slices was increased from 1 through 5 using a 500% slice gap without SMS.
Experiment 4: To isolate the impact of inflow, the slice gap between 3-slice, non-SMS imaging was varied from 0% to 500%.
Data Analysis: For all experiments, the initial 10 repetitions were discarded, volumes were motion corrected (except the single-slice measurements), and each voxel’s temporal drift was removed by second-order polynomial regression. The temporal stability was assessed using the temporal signal-to-noise ratio (tSNR) and power spectral analysis.
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