To reconstruct a High-Resolution Susceptibility Weighted Image (SWI) from a subset of low-resolution functional MRI (fMRI) data using Super-Resolution (SR) reconstruction.Subject head motion is inevitable during fMRI scans. Even when the subject makes an effort to remain still, sub-voxel involuntary motions are still impossible to control. However, such motions are useful for reconstructing a high-resolution image using SR reconstruction from the multiple low resolution fMRI images. There are existing studies that use SR for DTI white matter fiber track visualization¹ and to enhance imaging resolution in general². This abstract presents preliminary results of a high-resolution SWI reconstructed from a subset of fMRI data. The high-resolution image can then be used as a template over which the activation from the low resolution scans can be overlaid.Conceptually, a high-resolution image can be reconstructed form a series of low-resolution images provided these images have sub-pixel shifts with respect to each other. The spatial characteristics of the voxel can be modelled and used to reconstruct an image with a higher resolution. The observation model of the i-th low-resolution fMRI image y_i, is the result of applying a series of linear operators (D_i, F_i, H_i) and the addition of a noise term n_i: $$y_{i}=D_{i}F_{i}H_{i}z+n_{i}$$, where D_i, F_i, and H_i represent the down-sampling, geometrical transformation, and blurring, respectively. The reconstruction of the high-resolution image z is a classical linear inverse problem, which can be solved using methods such as non-uniform interpolation, the frequency domain approach, projection-onto-convex sets, or regularized methods³. A resting state fMRI scan was performed on a 3T Siemens TimTrio scanner using a 32-channel head coil. The sequence was a Simultaneous Multi-Slice EPI (TE/TR=30/1000ms, FOV=220mm, 64 2mm slices, FA=55o). Motion parameters (translation and rotation) were estimate using FSL’s FLIRT program⁴. Sixteen volumes were selected from the fMRI data and the SR reconstruction was permed using a MATLAB SR program⁵. A structure-adaptive normalized convolution approach was used for the SR reconstruction at 2mm resolution⁶. The resting state Independent Component Analysis (ICA) analysis was performed using FSL’s MELODIC program⁷. Figure 1 shows image translation (top) and rotation during the fMRI scan. The black dashed box annotates the time points of the 16 volumes selected for the SR reconstruction. Note that the translation (less than 2mm pixel size) and rotation were small enough to be useful for SR. Figure 2 shows a comparison of a LR image (a), an HR image (b) interpolated from the LR image using a bilinear interpolation method, and an SR image (c), respectively. A resting state activation map from the default mode was overlaid on top of the bilinear interpolated image and the SR reconstructed image and shown in Figures 3a and 3b, respectively. There are several ways to improve the SR reconstruction presented here. The SR images were reconstructed slice-by-slice from the LR fMRI data for computational ease. Better results may be obtained if the SR reconstruction is performed on the whole 3D volume using 3D translation and rotation parameters. Second, the alignment from motion was estimated using FLIRT. Because the SR reconstruction highly depends on the accuracy of the motion parameters, the reconstruction can be improved using parameters measured accurately using a MRI-compatible motion-tracking system⁸. Third, it is unclear if the variation of the voxels from BOLD hemodynamics contributes to the quality of the SR reconstruction. A potential solution is to use a remove hemodynamic trend before the SR reconstruction. The preliminary results presented here demonstrate the feasibility of deriving a high-resolution image using a subset of fMRI images. Future work will investigate options to improve the image quality of SR reconstruction.