Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.

Background Oscillating gradient diffusion MRI (dMRI) enables measurements at a short diffusion‐time (td), but it is challenging for clinical systems. Particularly, the low b‐value and low resolution may give rise to cerebrospinal fluid (CSF) contamination. Purpose To assess the effect of CSF partial volume on td–dMRI measurements and efficacy of inversion‐recovery (IR) prepared oscillating and pulsed gradient dMRI sequence to improve td–dMRI measurements in the human brain. Study Type Prospective. Subjects Ten normal volunteers and six glioma patients. Field Strength/Sequence A 3 T; three‐dimensional (3D) IR‐prepared oscillating gradient‐prepared gradient spin‐echo (GRASE) and two‐dimensional (2D) IR‐prepared oscillating gradient echo‐planar imaging (EPI) sequences. Assessment We assessed the td‐dependent patterns of apparent diffusion coefficient (ADC) in several gray and white matter structures, including the hippocampal subfields (head, body, and tail), cortical gray matter, thalamus, and posterior white matter in normal volunteers. Pulsed gradient (0 Hz) and oscillating gradients at frequencies of 20 Hz, 40 Hz, and 60 Hz dMRI were acquired with GRASE and EPI sequences with or without the IR module. We also tested the td‐dependency patterns in glioma patients using the EPI sequence with or without the IR module. Statistical Tests The differences in ADC across the different tds were compared by one‐way ANOVA followed by post hoc pairwise t‐tests with Bonferroni correction. Results In the healthy subjects, brain regions that were possibly contaminated by CSF signals, such as the hippocampus (head, body, and tail) and cortical gray matter, td‐dependent ADC changes were only significant with the IR‐prepared 2D and 3D sequences but not with the non‐IR sequences. In brain glioblastomas patients, significantly higher td‐dependence was observed in the tumor region with the IR module than that without IR (slope = 0.0196 μm2/msec2 vs. 0.0034 μm2/msec2). Conclusion The IR‐prepared sequence effectively suppressed the CSF partial volume effect and significantly improved the td‐dependent measurements in the human brain. Evidence Level 1 Technical Efficacy Stage 1

Purpose To develop an auto‐calibrated technique by joint K‐space and Image‐space Parallel Imaging (KIPI) for accelerated CEST acquisition. Theory and Methods The KIPI method selects a calibration frame with a low acceleration factor (AF) and auto‐calibration signals (ACS) acquired, from which the coil sensitivity profiles and artifact correction maps are calculated after restoring the k‐space by GRAPPA. Then the other frames with high AF and without ACS can be reconstructed by SENSE and artifact suppression. The signal leakage due to the T2‐decay filtering in k‐space compromises the SENSE reconstruction, which can be corrected by the artifact suppression algorithm of KIPI. The 2D and 3D imaging experiments were done on the phantom, healthy volunteer, and brain tumor patient with a 3T scanner. Results The proposed KIPI method was evaluated by retrospectively undersampled data with variable AFs and compared against existing parallel imaging methods (SENSE/auto, GRAPPA, and ESPIRiT). KIPI enabled CEST frames with random AFs to achieve similar image quality, eliminated the strong aliasing artifacts, and generated significantly smaller errors than the other methods (p < 0.01). The KIPI method permitted an AF up to 12‐fold in both phase‐encoding and slice‐encoding directions for 3D CEST source images, achieving an overall 8.2‐fold speedup in scan time. Conclusion KIPI is a novel auto‐calibrated parallel imaging method that enables variable AFs for different CEST frames, achieves a significant reduction in scan time, and does not compromise the accuracy of CEST maps.

3D pulse sequences enable high-resolution acquisition with high SNR and ideal slice profiles, which however, is particularly difficult for diffusion MRI (dMRI) due to the additional phase errors from diffusion encoding. Here we proposed a twin-navigator based 3D diffusion-weighted gradient spin-echo (DW-GRASE) sequence to correct the phase errors between shots for human whole-brain acquisition. Moreover, we tested whether acquisitions may impact the fiber-tracking results by comparing the 3D-GRASE with 2D-EPI using the fixel-based analysis, which indicated a significant difference between the 3D and 2D sequences in several microstructural parameters of the long cerebrospinal tract and splenium of corpus callous.

The relaxation time mapping has proven to be an important diagnostic tool, but it is limited by the prolonged scan time due to the measurements of multiple frames at the same location. In this study, the recently proposed auto-calibrated reconstruction method by joint k-space and image-space parallel imaging (KIPI) is utilized for the acceleration of relaxation time mapping. Combined with the ESPIRiT method, KIPI generates improved coil sensitivity maps and allows an acceleration factor of up to 4-fold for acquiring source images, yielding the accurate parameter map without obvious errors or artifacts.