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.