Parallel Imaging in Functional MRI : Comparison of Spiral SENSE and GRAPPA and of Calibration Update Schemes

Y. Kim, D. C. Noll Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States Introduction Parallel imaging has been applied to many application areas of MRI to reduce scan time and improve image quality. It may also be useful for functional MRI, for which a large number time points are required, however, there are a number implementation details that need to be resolved. Several parallel imaging techniques involving temporal data such as TSENSE[1] and TGRAPPA[2] have been introduced. In these, estimating missing k-space lines uses data from other neighboring time points, however, little work has been done on approach to updating the calibration data, such as sensitivity map (SENSE) and GRAPPA weights (GRAPPA), for functional MRI. In this study, spiral SENSE and spiral GRAPPA are used and compared in terms of SNR in time course, image error when compared to fully sampled data, and number of activated pixels. Several update schemes for parallel imaging calibration data are also compared for cases of minimal head motion and large head motion. Method & Experiment In this study, a functional experiment was conducted in order to compare the performance between spiral SENSE and GRAPPA. Investigation of different update schemes for calibration data was also examined. A 3T GE scanner with a 8-channel array coil was used and the imaging parameters were TR=2 sec, TE= 25msec, flip angle=80°, FOV = 22cm, 64×64 matrix with 2-shot spiral trajectory. Five healthy volunteers (two male, three female; mean age 22±3 years) were participated in the functional study after informed consent. The task was a finger tapping task with reversing checkerboard visual pattern of (20 s ON)/(20 s OFF), repeated 4 times for a task period of 10 time points with 40 total time points. The acquired data were reconstructed using both spiral SENSE and GRAPPA methods. For reconstruction of spiral SENSE, iterative conjugate gradient (CG) method is [3] used. The sensitivity maps are derived by dividing a fully sampled coil image by the geometric mean of coil images [4]. The reconstruction algorithm was speeded up by using fast iterative algorithm [6]. For spiral GRAPPA, constant-linear velocity spiral trajectory data are resampled into constant-angular spiral trajectory so as to be considered as Cartesian GRAPPA technique [5] and conjugate phase approach was used to reconstruct the image. Magnetic field maps were used to correct of spiral blurring for both reconstruction methods. For each time point, the reference interleaf is alternated such as in TSENSE. The final reconstructed images were lowpass filtered in time (bandwidth that filters out the 10% of high frequencies) to suppress variations from alternating shots. In order to examine differences between SENSE and GRAPPA, reconstructed images are compared by SNR in time series, number of activated pixels, and errors relative to a fully sampled image. Three different scheme for updating the calibration data (the sensitivity map or GRAPPA coefficients) were considered: (1) using values acquired at 1 time point for all images, (2) updating the calibration data at every time point, and (3) updating at every time point using moving average of 5 neighboring time points. Moreover, in order to see the effect of motion, during additional separate scan, subjects were told to move their head according to the movement of checkerboard that was moving left to right and back to left at every 20 seconds during visual activation. Results Figure 1. (a,d) fully sampled image (b,e) reconstructed image and (c,f) error maps for (top) SENSE and (bottom) GRAPPA