Simultaneous Multi-slice Inverse imaging for high temporal resolution fMRI
Ying-Hua Chu1, Yi-Cheng Hsu1, and Fa-Hsuan Lin1

1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan


We proposed the simultaneous multi-slice (SMS) inverse imaging (InI) method to achieve 10 Hz sampling rate and significantly improved spatial resolution (30-fold higher than typical inverse imaging; quantified by point-spread function). SMS-InI was demonstrated in a visual fMRI experiment showing maps of brain activity similar to EPI and hemodynamic response with 0.1 s precision.


Functional MRI (fMRI) can measure high spatial resolution (<1 cm) brain activities noninvasively. Typically, the BOLD contrast fMRI can image the whole brain with about 3 mm isotropic spatial resolution at 0.5 Hz. Considering hemodynamic response is temporally smooth, this temporal resolution is sufficient for most functional brain mapping experiments. However, such a sampling rate may not high enough to study the dynamics between brain regions or fine features in the hemodynamic responses to different tasks and stimuli. Several highly paralleled imaging methods have been proposed to achieve 10 Hz sampling rate, such as MREG1, InI2, and EVI3. Using these methods, high frequency resting state networks have been identified using MREG4, and causal relations between visual and motor areas was found at frequency up to 3 Hz using InI5. Blipped CAIPI for simultaneous multi-slice (SMS) EPI6 has been successfully demonstrated in fMRI and diffusion MRI to achieve fast imaging with reduced noise amplification. Blipped CAIPI can be integrated with the simultaneous refocusing method (SIR)7 to further improve sampling efficiency. Here, we develop the SMS inverse imaging (SMS-InI) method to acquire images covering the whole brain in 0.1 s. Specifically, two slice sets covering the whole brain were simultaneously excited and acquired with blipped CAIPI SIR-EPI method. Aliased slices within each slice set were resolved by coil sensitivity information with small regularization. The spatial resolution of SMS-InI was significantly better than InI based on point-spread function maps. Experiment results show similar visual cortex BOLD signal using SMS-InI and EPI acquisitions.


The SMS-InI sequence excited two slice sets (10 slices each) to cover 10 cm FOV. Blipped CAIPI SIR-EPI (flip angle = 30°, slice thickness = 4 mm, slice gap = 1 mm, CAIPIRINHA FOV shift = 1/3 FOV, resolution = 5 mm x 5 mm x 5 mm, TR =100 ms, and TE =30 ms) was used to acquire signal. SENSE method was used to reconstruct images with Tikhonov regularization. Point-spread function map (PSF) was quantified by calculating the average deviation of the source signal location8. A volunteer participated the event-related fMRI experiment. Visual stimuli were checkerboard flickering in large and small visual fields (Figure 3 Left column). Each visual stimulus flickering was 8 Hz with 500 ms duration. Thirty large and thirty small visual field stimuli were randomly presented in each 4 min session. Six sessions of data were measured, including 2 EPI (TR = 2 s, TE = 30 ms, flip angle = 90°, 3.5 mm isotropic resolution), 2 InI (TR = 100 ms, TE = 30 ms, flip angle = 30°, and pixel size = 4 mm × 4 mm × 4 mm) and 2 SMS-InI. All data were acquired on a 3T system (Skyra, Siemens, Erlangen, Germany). After imaging reconstruction, physiological noise was first suppressed by DRIFTER9. Then spatial normalization and smoothing were applied to images using SPM8.


Figure 2 shows the PSF maps of InI and SMS-InI. The average and standard deviation of PSF for InI and SMS-InI were 17.7 mm +/- 1.1 mm and 0.6+/- 0.5 mm, respectively. Figure 3 shows spatial distributions of significant BOLD signal (t- statistic) for EPI, InI, and SMS-InI measurements. InI shows significant spatial blurring along the anterior-posterior direction. Both EPI and SMS-InI shows BOLD signal at gray matter of the occipital lobe.


Our results shows that SMS-InI has significantly higher spatial resolution than InI (~30-fold smaller PSF) while the sampling rate can be kept at 0.1 s per volume. This advantage was derived from the controlled aliasing between neighboring slices using blips. Regularization was chosen by trading-off between PSF (favoring small regularization) and SNR (favoring large regularization). Since SMS-InI used a longer readout than InI, the distortion became more serious. However, this can be corrected by field maps. In current experiment, the image reconstruction of SMS-InI involved multiple sets of linear equations with less unknown than InI (SMS-InI: 10; InI: 64). Importantly, SMS-InI is a multi-slice method while InI is a 3D approach. This difference suggested that gaps between slices and motion sensitivity can be concerns for SMS-InI.


This study was supported by Ministry of Science and Technology, Taiwan (MOST 104-2314-B-002-238, MOST 103-2628-B-002-002-MY3), National Health Research Institute, Taiwan (NHRI-EX104-10247EI), and Ministry of Economic Affairs, Taiwan (100-EC-17-A-19-S1-175).


1. Zahneisen B, Grotz T, Lee KJ, Ohlendorf S, Reisert M, Zaitsev M, Hennig J. Three-dimensional MR-encephalography: Fast volumetric brain imaging using rosette trajectories. Magnetic Resonance in Medicine 2011;65(5):1260-1268.

2. Lin FH, Wald LL, Ahlfors SP, Hämäläinen MS, Kwong KK, Belliveau JW. Dynamic magnetic resonance inverse imaging of human brain function. Magnetic resonance in medicine 2006;56(4):787-802.

3. Witzel T, Polimeni J, Lin F, Numenmaa A, Wald L. Single-shot whole brain echo volume imaging for temporally resolved physiological signals in fMRI. 2011.

4. Lee H-L, Zahneisen B, Hugger T, LeVan P, Hennig J. Tracking dynamic resting-state networks at higher frequencies using MR-encephalography. Neuroimage 2013;65:216-222.

5. Lin F-H, Chu Y-H, Hsu Y-C, Lin J-FL, Tsai KW-K, Tsai S-Y, Kuo W-J. Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals. NeuroImage 2015;121:69-77.

6. Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magnetic Resonance in Medicine 2012;67(5):1210-1224.

7. Feinberg DA, Setsompop K. Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. Journal of magnetic resonance 2013;229:90-100.

8. Chang WT, Setsompop K, Ahveninen J, Belliveau JW, Witzel T, Lin FH. Improving the spatial resolution of magnetic resonance inverse imaging via the blipped-CAIPI acquisition scheme. Neuroimage 2014;91:401-411.

9. Sarkka S, Solin A, Nummenmaa A, Vehtari A, Auranen T, Vanni S, Lin FH. Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER. Neuroimage 2012;60(2):1517-1527.


Figure 1. The pulse sequence of the simultaneous multi-slice inverse imaging (SMS-InI) method

Figure 2. Point-spread function map (PSF) of InI and SMS-InI.

Figure 3. Spatial distributions of significant BOLD signal (t- statistic) for EPI, InI, and SMS-InI measurements in visual stimuli experiment.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)