It has been recently proposed that computer-simulated phantom images can be used to evaluate methods for fMRI preprocessing. It is widely recognized that Gradient-Echo Echo Planar Imaging (EPI), the most often used technique for fMRI, is strongly affected by field inhomogeneities. Accurate and realistic phantom images for use by the fMRI community for software evaluation and training must incorporate these distortions and account for the effects of head motion and respiration on the distortions. A method to generate realistic distortions caused by field inhomogeneity for the generation of an fMRI phantom is presented in this paper. Changes in field inhomogeneity due to motion are studied by means of adding motions to the brain model and calculating the induced field map numerically rather than measuring it experimentally. A fast analytic version of an MR simulation is used to generate distorted EPI images based on the calculated field maps. The new generated fMRI phantoms can be used to evaluate processing algorithms for fMRI study more accurately. We can appreciate the importance of distortions for fMRI phantom generation by simulating a distortion-free image and adding distortions afterwards. Validations are performed by comparing the calculated field maps with measured ones. In addition, we show the similarities between a simulated fMRI phantom and real EPI image from our MR scanner.
We explore the use of scalar and multivariate autoregressive models to parameterize motion artifacts in fMRI time series. To do so, we acquire real fMRI data sets, measure rigid body motion in these data sets, and classify the type of observed motion in several categories such as random motion or motion correlated with activation. The measured motion sequences are then modeled and used to generate realistic image phantoms that can be used to validate fMRI data analysis packages. We compare phantoms generated with the original motion sequences and phantoms generated with simulated sequences. We show that both scalar and multivariate autoregressive models can be used to generate realistic motion sequences. An important difference between the two is the fact that multivariate models can capture correlations between motion parameters, which cannot be done with scalar models.
Geometric distortion is a well-recognized problem in echo planar (EP) images. One strategy for the correction of these distortions is to register an EP image to a reference image, such as a high resolution anatomical MR image in which geometric distortion is minimal. Non-rigid registration methods, which warp images locally, have been used for this purpose. While a physics-based distortion model for spin-echo (SE) EP image has been developed and used as a constraint in nonrigid registration algorithms, such a model for gradient-echo (GE) EP image has not been investigated. Here, we propose to use a physics-based model for GE EP image that incorporates a term that takes dephasing into consideration. To evaluate this technique, we generate a distortion-free EP image using an MR simulator we have developed. We then distort the image and modify its intensity values using a real field map and an analytical expression that includes dephasing. The geometric distortion computed from the field map is used as the ground truth to which the deformation fields obtained with our method is compared. We show that including the dephasing term improves the results.
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