Conclusions Simulated fMRI phantoms with real motion and realistic susceptibility artifacts have been generated and tested using SPM2. Image distortion and signal loss in the series causes erroneous activation detection. The new phantoms will provide a better test bed for pre-processing algorithms that are designed to compensate for motion and susceptibility artifacts. Work is in progress to evaluate various susceptibility artifacts correction methods using these phantoms. Acknowledgement This work was supported by Vanderbilt intramural discovery grants program and the NIH grant R01- NS Phantoms available at [1] Y. Li, et al., ISMRM. 2005, P1571 [2] D. Pickens, et al., Magn.Reson. Imag. 2005, 23: [3] [4] D. Yoder, et al., Magn.Reson. Imag. 2004, 22: [5] L. R. Andersson, et al., NeuroImage. 2001, 13: [6] N. Xu, et al., Med Im 2006, V Introduction As an extension of work on computer-generated phantoms [1-2], improved fMRI phantoms are generated with realistic susceptibility artifacts caused by both motion and static-field inhomogeneity that is correlated with the motion. These phantoms provide a ground truth that can be used by the fMRI community to assess quantitatively pre-processing algorithms, such as motion correction, distortion correction, and signal loss compensation. An activation analysis is performed on these phantoms based on a block paradigm design using SPM2 [3], and the experimental results demonstrate that motion-correlated susceptibility artifacts affect motion correction and activation detection. Thus these artifacts represent a critical component of phantom generation. Methods An anatomical volume of the human head is segmented into air and tissue, and these components are assigned the susceptibilities of air and water, respectively. The map of field inhomogeneity induced by the susceptibility variations is calculated using a field map calculation method [4]. A sequence of head motion estimated from real fMRI data is applied, and the map of field inhomogeneity is recalculated for each orientation of the head to capture the complex dependence of field inhomogeneity on susceptibility and orientation [5]. A fast simulator for MR formation in the presence of static-field inhomogeneity has been implemented with Matlab® [6]. This simulator can perform both a single-shot EPI pulse sequence and a single-shot spiral EPI pulse sequence. It takes as input an “object image”, which is a high-resolution, segmented volume with spin density, T1, T2, and susceptibility specified for each voxel, and a set of pulse parameters; it generates as output a lower resolution volume with concomitant image distortion and signal loss. A single-shot EPI slice can be simulated in seconds. First, the simulator is employed to generate a T2-weighted, distortion-free, ground-truth EPI volume. This volume is replicated 99 times to generate a template for our phantoms. Simulated activations are added to these volumes by modifying image intensities accordingly. Second, the simulator is applied with motion included. Third, both motion and susceptibility are included. As a result, the effects of motion and of motion plus motion-correlated susceptibility can be compared to ground truth. A specified SNR is achieved by adding noise to the K-space data before image reconstruction. Experiments and Results. Single-shot EPI volumes with 64x64 matrix size, 240mm FOV, 28 slices, and 4.5mm slice thickness are simulated. To demonstrate the relative fidelity of the simulator, a comparison is shown in Fig. 1 between a simulated EPI volume in the presence of static-field inhomogeneity and a real volume. A set of fMRI time series with motion only and with motion and susceptibility artifacts were simulated. Activation analysis was performed after motion correction using SPM2[3]. Detected activation maps and percent signal change in ROIs are shown in Fig. 2 and Fig. 3. The estimated motion parameters from series with and without susceptibility artifacts are shown in Fig. 4. Realistic Computer Generated fMRI Phantoms with Motion-Correlated Susceptibility Artifacts Ning Xu 1, Yong Li 1, J. Michael Fitzpatrick 1, Benoit M. Dawant 1, Victoria L. Morgan 2, David. R. Pickens 2 1 Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States, 2 Vanderbilt University Institute for Imaging Science, Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States Fig. 4 Estimated motion parameters from time series with (dashed) and without (solid) susceptibility artifacts. Large changes in the motion parameters are evident when susceptibility is added. Thus, the presence of susceptibility has a large effect on the accuracy of the estimation of motion parameters. Fig. 1 A simulated EPI volume (top) and a real EPI volume (bottom). The volumes exhibit similar geometric distortion and signal loss. Fig. 3 Plot of percent signal change in ROI (white square in Fig. 2). The activation in this area is not readily detected when motion and susceptibility artifacts are present. (a) (b) (c) Fig. 2. Activation maps in red on corresponding slices. (a) anatomical, (b) simulated EP: motion only, (c) simulated EP: motion + susceptibility. In (b) detected activations after motion correction agree with the true activations in (a), but in (c) they are badly compromised by susceptibility artifacts. White squares show an ROI used in Fig. 3.