Accelerated proton echo planar spectroscopic imaging (PEPSI) using GRAPPA with a 32-channel phased-array coil.

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 sensitivity-encoding (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.     

Mimicking the human expert: pattern recognition for an automated assessment of data quality in MR spectroscopic images.

Besides the diagnostic evaluation of a spectrum, the assessment of its quality and a check for plausibility of its information remains a highly interactive and thus time-consuming process in MR spectroscopic imaging (MRSI) data analysis. In the automation of this quality control, a score is proposed that is obtained by training a machine learning classifier on a representative set of spectra that have previously been classified by experts into evaluable data and nonevaluable data. In the first quantitative evaluation of different quality measures on a test set of 45,312 long echo time spectra in the diagnosis of brain tumor, the proposed pattern recognition (using the random forest classifier) separated high- and low-quality spectra comparable to the human operator (area-under-the-curve of the receiver-operator-characteristic, AUC>0.993), and performed better than decision rules based on the signal-to-noise-ratio (AUC<0.934) or the estimated Cramér-Rao-bound on the errors of a spectral fitting (AUC<0.952). This probabilistic assessment of the data quality provides comprehensible confidence images and allows filtering the input of any subsequent data processing, i.e., quantitation or pattern recognition, in an automated fashion. It thus can increase robustness and reliability of the final diagnostic evaluation and allows for the automation of a tedious part of MRSI data analysis.     

Four-dimensional flow-sensitive MRI of the thoracic aorta: 12- versus 32-channel coil arrays

Purpose:

To evaluate the performance of four‐dimensional (4D) flow‐sensitive MRI in the thoracic aorta using 12‐ and 32‐channel coils and parallel imaging.

Materials and Methods:

4D flow‐sensitive MRI was performed in the thoracic aorta of 11 healthy volunteers at 3 Tesla (T) using different coils and parallel imaging (GRAPPA) accelerations (R): (i) 12‐channel coil, R = 2; (ii) 12‐channel coil, R = 3; (iii) 32‐channel coil, R = 3. The quantitative analysis included SNR, residual velocity divergence and length and curvature of traces (streamlines and pathlines) as used for 3D flow visualization. In addition, semi‐quantitative image grading was performed to assess quality of phase‐contrast angiography and 3D flow visualization.

Results:

Parallel imaging with an acceleration factor R = 3 allowed to save 19.5 ± 5% measurement time compared with R = 2 (14.2 ± 2.4 min). Acquisition using 12 channels with R = 2 and 32 channels with R = 3 produced data with significantly (P < 0.05) higher quality compared with 12 channels and R = 3. There was no significant difference between 12 channels with R = 2 and 32 channels with R = 3 but for the depiction of supra‐aortic branches where the 32‐channel coil proved superior.

Conclusion:

Using 32‐channel coils is beneficial for 4D flow‐sensitive MRI of the thoracic aorta and can allow for a reduction of total scan time while maintaining overall image quality. J. Magn. Reson. Imaging 2012;35:190‐195. © 2011 Wiley Periodicals, Inc.     

Auto-calibrated parallel spiral imaging.

This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is defined along radial directions so that missing spiral interleaves can be estimated directly from neighboring interleaves. This kernel is invariant along the radial direction but varies azimuthally. Therefore, the k-space is divided into angular sectors and sector-specific kernels are used. It is demonstrated experimentally that relatively few sectors are sufficient for accurate reconstruction, allowing for efficient implementation. The interpolation kernels can be derived either from a separate calibration scan or self-calibration data available with a dual-density spiral acquisition. The reconstruction method is implemented with two sampling strategies and experimentally demonstrated to be robust.     

GRAPPA operator for wider radial bands (GROWL) with optimally regularized self‐calibration

A self‐calibrated parallel imaging reconstruction method is proposed for azimuthally undersampled radial dataset. A generalized auto‐calibrating partially parallel acquisition (GRAPPA) operator is used to widen each radial view into a band consisting of several parallel lines, followed by a standard regridding procedure. Self‐calibration is achieved by regridding the central k‐space region, where Nyquist criterion is satisfied, to a rotated Cartesian grid. During the calibration process, an optimal Tikhonov regularization factor is introduced to reduce the error caused by the small k‐space area of the self‐calibration region. The method was applied to phantom and in vivo datasets acquired with an eight‐element coil array, using 32‐64 radial views with 256 readout samples. When compared with previous radial parallel imaging techniques, GRAPPA operator for wider radial bands (GROWL) provides a significant speed advantage since calibration is carried out using the fully sampled k‐space center. A further advantage of GROWL is its applicability to arbitrary‐view angle ordering schemes. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.     

MRA of abdominal vessels: technical advances

Magnetic resonance angiography (MRA) in general and MRA of the abdominal vessels in particular have undergone substantial improvements in the past 5 years triggered by the introduction and application of parallel imaging (PI), new sequence techniques such as centric k-space trajectories and undersampling, dedicated contrast agents and clinical high-field scanners. All of these techniques have the potential to improve image quality and resolution or decrease the image acquisition time. However, each of them has its own specific advantages and drawbacks. This review describes the main technical innovations and focuses on the impact these developments may have on abdominal MRA. Special consideration is given to the interaction of these various technical advances. The clinical value of advanced MRA techniques is discussed and illustrated by characteristic cases.An erratum to this article can be found at     

SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space

A new approach to autocalibrating, coil‐by‐coil parallel imaging reconstruction, is presented. It is a generalized reconstruction framework based on self‐consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k‐space sampling patterns. The formulation can flexibly incorporate additional image priors such as off‐resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k‐space domain are presented. These are based on a projection over convex sets and conjugate gradient algorithms. Phantom and in vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off‐resonance correction and nonlinear ?₁‐wavelet regularization are also demonstrated. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.