Purpose: To mitigate spatial flip angle (FA) variations under strict specific absorption rate (SAR) constraints for ultra‐high field MRI using a combination of universal parallel transmit (pTx) pulses and fast subject‐specific optimization.Methods: Data sets consisting of B₀, maps, and virtual observation point (VOP) data were acquired from 72 subjects (study groups of 48/12 healthy Europeans/Asians and 12 Europeans with pathological or incidental findings) using an 8Tx/32Rx head coil on a 7T whole‐body MR system. Combined optimization values (COV) were defined as combination of spiral‐nonselective (SPINS) trajectory parameters and an energy regularization weight. A set of COV was optimized universally by simulating the individual RF pulse optimizations of 12 training data sets (healthy Europeans). Subsequently, corresponding universal pulses (UPs) were calculated. Using COV and UPs, individually optimized pulses (IOPs) were calculated during the sequence preparation phase (maximum 15 s). Two different UPs and IOPs were evaluated by calculating their normalized root‐mean‐square error (NRMSE) of the FA and SAR in simulations of all data sets. Seven additional subjects were examined using an MPRAGE sequence that uses the designed pTx excitation pulses and a conventional adiabatic inversion.Results: All pTx pulses resulted in decreased mean NRMSE compared to a circularly polarized (CP) pulse (CP = ~28%, UPs = ~17%, and IOPs = ~12%). UPs and IOPs improved homogeneity for all subjects. Differences in NRMSE between study groups were much lower than differences between different pulse types.Conclusion: UPs can be used to generate fast online‐customized (FOCUS) pulses gaining lower NRMSE and/or lower SAR values.

Purpose: In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi‐pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use.Methods: We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B₁ inhomogeneities. The input data for a neural feed‐forward network consisted of 7 T in vivo uncorrected Z‐spectra of a single B₁ level, and a B₁ map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel‐wise to target data consisting of Lorentzian amplitudes generated conventionally by 5‐pool Lorentzian fitting of normalized, denoised, B₀‐ and B₁‐corrected Z‐spectra. The deepCEST network was trained with Gaussian negative log‐likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes.Results: The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B₁ level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably.Conclusion: The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B₀‐ and B₁‐corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.

Purpose: To enable whole‐brain quantitative CEST MRI at ultra‐high magnetic field strengths (B₀ ≥ 7T) within short acquisition times.Methods: Multiple interleaved mode saturation (MIMOSA) was combined with fast online‐customized (FOCUS) parallel transmission (pTx) excitation pulses and correction to achieve homogenous whole‐brain coverage. Examinations of 13 volunteers were performed on a 7T MRI system with 3 different types of pulse sequences: (1) saturation in circular polarized (CP) mode and CP mode readout, (2) MIMOSA and CP readout, and (3) MIMOSA and FOCUS readout. For comparison, the inverse magnetic transfer ratio metric for relayed nuclear Overhauser effect and amide proton transfer were calculated. To investigate the number of required acquisitions for a good correction, 4 volunteers were measured with 6 different B₁ amplitudes. Finally, time point repeatability was investigated for 6 volunteers.Results: MIMOSA FOCUS sequence using correction, with both single and multiple points, reduced inhomogeneity of the CEST contrasts around the occipital lobe and cerebellum. Results indicate that the most stable inter‐subject coefficient of variation was achieved using the MIMOSA FOCUS sequence. Time point repeatability of MIMOSA FOCUS with single‐point correction showed a maximum coefficient of variation below 8% for 3 measurements in a single volunteer.Conclusion: A combination of MIMOSA FOCUS with a single‐point correction can be used to achieve quantitative CEST measurements at ultra‐high magnetic field strengths. Compared to previous correction methods, acquisition time can be reduced as additional scans required for correction can be omitted.

Purpose: Tailored parallel‐transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real‐time pulse redesign. A deep learning framework is proposed to estimate pTx distributions following within‐slice motion, which can then be used for tailored pTx pulse redesign.Methods: Using simulated data, conditional generative adversarial networks were trained to predict distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground‐truth (simulated, following motion) B₁ maps. Tailored pTx pulses were designed using B₁ maps at the original position (simulated, no motion) and evaluated using simulated B₁ maps at displaced position (ground‐truth maps) to quantify motion‐related excitation error. A second pulse was designed using predicted maps (also evaluated on ground‐truth maps) to investigate improvement offered by the proposed method.Results: Predicted maps corresponded well with ground‐truth maps. Error in predicted maps was lower than motion‐related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst‐case flip‐angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps.Conclusion: We propose a framework for predicting maps online with deep neural networks. Predicted maps can then be used for real‐time tailored pulse redesign, helping to overcome head motion–related error in pTx.

Purpose: Amide proton transfer‐weighted (APTw) MRI at 3T provides a unique contrast for brain tumor imaging. However, APTw imaging suffers from hyperintensities in liquid compartments such as cystic or necrotic structures and provides a distorted APTw signal intensity. Recently, it has been shown that heuristically motivated fluid suppression can remove such artifacts and significantly improve the readability of APTw imaging.Theory and Methods: In this work, we show that the fluid suppression can actually be understood by the known concept of spillover dilution, which itself can be derived from the Bloch‐McConnell equations in comparison to the heuristic approach. Therefore, we derive a novel post‐processing formula that efficiently removes fluid artifact, and explains previous approaches. We demonstrate the utility of this APTw assessment in silico, in vitro, and in vivo in brain tumor patients acquired at MR scanners from different vendors.Results: Our results show a reduction of the CEST signals from fluid environments while keeping the APTw‐CEST signal intensity almost unchanged for semi‐solid tissue structures such as the contralateral normal appearing white matter. This further allows us to use the same color bar settings as for conventional APTw imaging.Conclusion: Fluid suppression has considerable value in improving the readability of APTw maps in the neuro‐oncological field. In this work, we derive a novel post‐processing formula from the underlying Bloch‐McConnell equations that efficiently removes fluid artifact, and explains previous approaches which justify the derivation of this metric from a theoretical point of view, to reassure the scientific and medical field about its use.

For precise delineation of glioma extent, amino acid PET is superior to conventional MR imaging. Since metabolic MR sequences such as chemical exchange saturation transfer (CEST) imaging and MR spectroscopy (MRS) were developed, we aimed to evaluate the diagnostic accuracy of combined CEST and MRS to predict glioma infiltration. Eighteen glioma patients of different tumor grades were enrolled in this study; ¹⁸F-fluoroethyltyrosine (FET)-PET, amide proton transfer CEST at 7 Tesla(T), MRS and conventional MR at 3T were conducted preoperatively. Multi modalities and their association were evaluated using Pearson correlation analysis patient-wise and voxel-wise. Both CEST (R = 0.736, p < 0.001) and MRS (R = 0.495, p = 0.037) correlated with FET-PET, while the correlation between CEST and MRS was weaker. In subgroup analysis, APT values were significantly higher in high grade glioma (3.923 ± 1.239) and IDH wildtype group (3.932 ± 1.264) than low grade glioma (3.317 ± 0.868, p < 0.001) or IDH mutant group (3.358 ± 0.847, p < 0.001). Using high FET uptake as the standard, the CEST/MRS combination (AUC, 95% CI: 0.910, 0.907–0.913) predicted tumor infiltration better than CEST (0.812, 0.808–0.815) or MRS (0.888, 0.885–0.891) alone, consistent with contrast-enhancing and T2-hyperintense areas. Probability maps of tumor presence constructed from the CEST/MRS combination were preliminarily verified by multi-region biopsies. The combination of 7T CEST/MRS might serve as a promising non-radioactive alternative to delineate glioma infiltration, thus reshaping the guidance for tumor resection and irradiation.