Parallel imaging is a robust method for accelerating the acquisition of magnetic resonance imaging (MRI) data, and has made possible many new applications of MR imaging. Parallel imaging works by acquiring a reduced amount of k‐space data with an array of receiver coils. These undersampled data can be acquired more quickly, but the undersampling leads to aliased images. One of several parallel imaging algorithms can then be used to reconstruct artifact‐free images from either the aliased images (SENSE‐type reconstruction) or from the undersampled data (GRAPPA‐type reconstruction). The advantages of parallel imaging in a clinical setting include faster image acquisition, which can be used, for instance, to shorten breath‐hold times resulting in fewer motion‐corrupted examinations. In this article the basic concepts behind parallel imaging are introduced. The relationship between undersampling and aliasing is discussed and two commonly used parallel imaging methods, SENSE and GRAPPA, are explained in detail. Examples of artifacts arising from parallel imaging are shown and ways to detect and mitigate these artifacts are described. Finally, several current applications of parallel imaging are presented and recent advancements and promising research in parallel imaging are briefly reviewed. J. Magn. Reson. Imaging 2012;36:55–72. © 2012 Wiley Periodicals, Inc.

Purpose For clinical implementation, a chemical exchange saturation transfer (CEST) imaging sequence must be fast, with high signal‐to‐noise ratio (SNR), 3D coverage, and produce robust contrast. However, spectrally selective CEST contrast requires dense sampling of the Z‐spectrum, which increases scan duration. This article proposes a compromise: using a 3D snapshot gradient echo (GRE) readout with optimized CEST presaturation, sampling, and postprocessing, highly resolved Z‐spectroscopy at 3T is made possible with 3D coverage at almost no extra time cost. Methods A 3D snapshot CEST sequence was optimized for low‐power CEST MRI at 3T. Pulsed saturation was optimized for saturation power and saturation duration. Spectral sampling and postprocessing (B 0 correction, denoising) was optimized for spectrally selective Lorentzian CEST effect extraction. Reproducibility was demonstrated in 3 healthy volunteers and feasibility was shown in 1 tumor patient. Results Low‐power saturation was achieved by a train of 80 pulses of duration t p = 20 ms (total saturation time t sat = 3.2 seconds at 50% duty cycle) with B 1 = 0.6 μT at 54 irradiation frequency offsets. With the 3D snapshot CEST sequence, a 180 × 220 × 54 mm field of view was acquired in 7 seconds per offset. Spectrally selective CEST effects at +3.5 and –3.5 ppm were quantified using multi‐Lorentzian fitting. Reproducibility was high with an intersubject coefficient of variation below 10% in CEST contrasts. Amide and nuclear overhauser effect CEST effects showed similar correlations in tumor and necrosis as show in previous ultra‐high field work. Conclusion A sophisticated CEST tool ready for clinical application was developed and tested for feasibility.

Purpose Relaxation‐compensated CEST‐MRI (i.e., the inverse metrics magnetization transfer ratio and apparent exchange‐dependent relaxation) has already been shown to provide valuable information for brain tumor diagnosis at ultrahigh magnetic field strengths. This study aims at translating the established acquisition protocol at 7 T to a clinically relevant magnetic field strength of 3 T. Methods Protein model solutions were analyzed at multiple magnetic field strengths to assess the spectral widths of the amide proton transfer and relayed nuclear Overhauser effect (rNOE) signals at 3 T. This prior knowledge of the spectral range of CEST signals enabled a reliable and stable Lorentzian‐fitting also at 3 T where distinct peaks are no longer resolved in the Z‐spectrum. In comparison to the established acquisition protocol at 7 T, also the image readout was extended to three dimensions. Results The observed spectral range of CEST signals at 3 T was approximately ±15 ppm. Final relaxation‐compensated amide proton transfer and relayed nuclear Overhauser effect contrasts were in line with previous results at 7 T. Examination of a patient with glioblastoma demonstrated the applicability of this acquisition protocol in a clinical setting. Conclusion The presented acquisition protocol allows relaxation‐compensated CEST‐MRI at 3 T with a 3D coverage of the human brain. Translation to a clinically relevant magnetic field strength of 3 T opens the door to trials with a large number of participants, thus enabling a comprehensive assessment of the clinical relevance of relaxation compensation in CEST‐MRI.

Purpose: The aim of this study was to translate the T 1 ρ-based dynamic glucoseenhanced (DGEρ) experiment from ultrahigh magnetic field strengths to a clinical field strength of 3 T. Although the protocol would seem to be as simple as gadoliniumenhanced imaging, several obstacles had to be addressed, including signal-to-noise ratio (SNR), robustness of contrast, and postprocessing, especially motion correction. Methods: Spin-lock based presaturation and a 3D gradient-echo snapshot readout were optimized for 3 T with regard to robustness, chemical exchange saturation transfer effect strength, and SNR. Postprocessing steps, including dynamic B 0 and motion correction, were analyzed and optimized in 7 healthy volunteers. The final protocol, including glucose injection, was applied to 3 glioblastoma patients. Results: With appropriate postprocessing, motion-related artifacts could be drastically reduced, and an SNR of approximately 90 could be achieved for a single dynamic measurement. In 2 patients with blood-brain barrier breakdown, a significant glucose uptake could be observed with a DGEρ effect strength in the range of 0.4% of the water signal. Thorough analysis of possible residual motion revealed that the statistical evidence can decrease when tested against pseudo effects attributed to uncorrected motion. Conclusion: DGEρ imaging was optimized for clinical field strengths of 3 T, and a robust protocol was established for broader application. Early experience shows that DGEρ seems possible at 3 T and could not only be attributed to motion artifacts.Observed DGEρ maps showed unique patterns, partly matching with the T 1 -ce tumor ring enhancement. However, effect sizes are small and careful clinical application is necessary.

Purpose Calculation of sophisticated MR contrasts often requires complex mathematical modeling. Data evaluation is computationally expensive, vulnerable to artifacts, and often sensitive to fit algorithm parameters. In this work, we investigate whether neural networks can provide not only fast model fitting results, but also a quality metric for the predicted values, so called uncertainty quantification, investigated here in the context of multi‐pool Lorentzian fitting of CEST MRI spectra at 3T. Methods A deep feed‐forward neural network including a probabilistic output layer allowing for uncertainty quantification was set up to take uncorrected CEST‐spectra as input and predict 3T Lorentzian parameters of a 4‐pool model (water, semisolid MT, amide CEST, NOE CEST), including the B0 inhomogeneity. Networks were trained on data from 3 subjects with and without data augmentation, and applied to untrained data from 1 additional subject and 1 brain tumor patient. Comparison to conventional Lorentzian fitting was performed on different perturbations of input data. Results The deepCEST 3T networks provided fast and accurate predictions of all Lorentzian parameters and were robust to input perturbations because of noise or B0 artifacts. The uncertainty quantification detected fluctuations in input data by increase of the uncertainty intervals. The method generalized to unseen brain tumor patient CEST data. Conclusions The deepCEST 3T neural network provides fast and robust estimation of CEST parameters, enabling online reconstruction of sophisticated CEST contrast images without the typical computational cost. Moreover, the uncertainty quantification indicates if the predictions are trustworthy, enabling confident interpretation of contrast changes.