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Parallel imaging has been demonstrated to reduce the encoding time of MR spectroscopic imaging (MRSI). Here we investigate up to 5-fold acceleration of 2D proton echo planar spectroscopic imaging (PEPSI) at 3T using generalized autocalibrating partial parallel acquisition (GRAPPA) with a 32-channel coil array, 1.5 cm 3 voxel size, TR/TE of 15/2000 ms, and 2.1 Hz spectral resolution. Compared to an 8-channel array, the smaller RF coil elements in this 32-channel array provided a 3.1-fold and 2.8-fold increase in signal-to-noise ratio (SNR) in the peripheral region and the central region, respectively, and more spatial modulated information. Comparison of sensitivityencoding (SENSE) and GRAPPA reconstruction using an 8-channel array showed that both methods yielded similar quantitative metabolite measures (P > 0.1). Concentration values of N-acetyl-aspartate (NAA), total creatine (tCr), choline (Cho), myo-inositol (mI), and the sum of glutamate and glutamine (Glx) for both methods were consistent with previous studies. Using the 32-channel array coil the mean Cramer-Rao lower bounds (CRLB) were less than 8% for NAA, tCr, and Cho and less than 15% for mI and Glx at 2-fold acceleration. At 4-fold acceleration the mean CRLB for NAA, tCr, and Cho was less than 11%. In conclusion, the use of a 32-channel coil array and GRAPPA reconstruction can significantly reduce the measurement time for mapping brain metabolites. Key words: proton echo planar spectroscopic imaging; PEPSI; MR spectroscopic imaging; parallel MRI; 32-channel phase array; GRAPPA MR spectroscopic imaging (MRSI) plays important roles in both clinical diagnosis and biomedical research. One of the main challenges of the conventional MRSI techniques is the lengthy data acquisition time, a result of the many phase-encoding steps required for complete spatial encoding. Several methods have been proposed to reduce scanning time using reduced or weighted k-space acquisition (1). Other methods acquire multiple (typically two to four) individually phase-encoded spin echoes within a single RF excitation to reduce encoding time (2). However, because the acquisition of multiple echoes requires shortened echo spacing, such a method is characterized by a limited spectral resolution. Alternatively, it is possible to acquire all the spatial information in a single shot using fast imaging readout modules, and to encode the spectral information by incrementing the spectral evolution time in separate RF excitations (3-6). In this way, spatial resolution is independent of scanning time, thus high spatial resolution can be achieved. However, the disadvantage of such approaches is the time-consuming spectral encoding process required to achieve high spectral resolution and bandwidth. Proton echo planar spectroscopic imaging (PEPSI) (7-9) uses an oscillating readout gradient to simultaneously acquire spatial and spectral information in a single RF excitation. PEPSI yields spectral resolution that approximates that of conventional MRSI and enables a reduction in the encoding tim...

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.