Magnetic resonance fingerprinting in multiple sclerosis.

BACKGROUND: In this cross sectional study, we used MRF to investigate tissue properties of normal-appearing white matter, gray matter, and lesions in relapsing remitting MS (n = 21), secondary progressive MS (n = 16) and healthy controls (n = 9). A FISP-based MRF sequence was used for acquisition, imaging time 5 min 15 s. MRF T1 and T2 relaxation times were measured from lesional tissue, normal-appearing frontal white matter, corpus callous, thalamus, and caudate. Differences between healthy controls and MS were examined using ANCOVA adjusted for age and sex. Spearman rank correlations were assessed between T1 and T2 relaxation times and clinical measures. OBJECTIVES: To examine brain T1 and T2 values using magnetic resonance fingerprinting (MRF) in healthy controls and MS. METHODS: The subjects included 21 relapsing-remitting (RR) MS, 16 secondary progressive (SP) MS, and 9 age- and sex-matched HC without manifest neurological disease participating in a longitudinal MRI study. A 3T/ FISP-based MRF sequence was acquired. Regions of interest were drawn for lesions and normal appearing white matter. ANCOVA adjusted for age and sex were used to compare the groups with significance set at 0.05. RESULTS: A step-wise increase in T1 and T2 relaxation times was found between healthy controls, relapsing remitting MS, and secondary progressive MS. Significant differences were found in T1 and T2 between MS and healthy controls in the frontal normal-appearing white matter, corpus callosum, and thalamus (p < 0.04 for all). Significant differences in T1 and T2 between RR and SPMS were found in the frontal normal-appearing white matter and T2 lesions (p < 0.02 for all). T1 relaxation from the frontal normal-appearing white matter correlated with the Expanded Disability Status Scale [ρ = 0.62, p < 0.001], timed 25 foot walk (ρ = 0.45, p = 0.01), 9 hole peg test (ρ = 0.62, p < 0.001), and paced auditory serial addition test (ρ = -0.4, p = 0.01). CONCLUSION: These results suggest that MRF may be a clinically feasible quantitative approach for characterizing tissue damage in MS.

Breast cancer imaging with glucosamine CEST (chemical exchange saturation transfer) MRI: first human experience.

OBJECTIVES: This study aims to evaluate the feasibility of imaging breast cancer with glucosamine (GlcN) chemical exchange saturation transfer (CEST) MRI technique to distinguish between tumor and surrounding tissue, compared to the conventional MRI method. METHODS: Twelve patients with newly diagnosed breast tumors (median age, 53 years) were recruited in this prospective IRB-approved study, between August 2019 and March 2020. Informed consent was obtained from all patients. All MRI measurements were performed on a 3-T clinical MRI scanner. For CEST imaging, a fat-suppressed 3D RF-spoiled gradient echo sequence with saturation pulse train was applied. CEST signals were quantified in the tumor and in the surrounding tissue based on magnetization transfer ratio asymmetry (MTRasym) and a multi-Gaussian fitting. RESULTS: GlcN CEST MRI revealed higher signal intensities in the tumor tissue compared to the surrounding breast tissue (MTRasym effect of 8.12 ± 4.09%, N = 12, p = 2.2 E-03) with the incremental increase due to GlcN uptake of 3.41 ± 0.79% (N = 12, p = 2.2 E-03), which is in line with tumor location as demonstrated by T1W and T2W MRI. GlcN CEST spectra comprise distinct peaks corresponding to proton exchange between free water and hydroxyl and amide/amine groups, and relayed nuclear Overhauser enhancement (NOE) from aliphatic groups, all yielded larger CEST integrals in the tumor tissue after GlcN uptake by an averaged factor of 2.2 ± 1.2 (p = 3.38 E-03), 1.4 ± 0.4 (p =9.88 E-03), and 1.6 ± 0.6 (p = 2.09 E-02), respectively. CONCLUSION: The results of this initial feasibility study indicate the potential of GlcN CEST MRI to diagnose breast cancer in a clinical setup. KEY POINTS: • GlcN CEST MRI method is demonstrated for its the ability to differentiate between breast tumor lesions and the surrounding tissue, based on the differential accumulation of the GlcN in the tumors. • GlcN CEST imaging may be used to identify metabolic active malignant breast tumors without using a Gd contrast agent. • The GlcN CEST MRI method may be considered for use in a clinical setup for breast cancer detection and should be tested as a complementary method to conventional clinical MRI methods.

What do we know about dynamic glucose-enhanced (DGE) MRI and how close is it to the clinics? Horizon 2020 GLINT consortium report.

Cancer is one of the most devastating diseases that the world is currently facing, accounting for 10 million deaths in 2020 (WHO). In the last two decades, advanced medical imaging has played an ever more important role in the early detection of the disease, as it increases the chances of survival and the potential for full recovery. To date, dynamic glucose-enhanced (DGE) MRI using glucose-based chemical exchange saturation transfer (glucoCEST) has demonstrated the sensitivity to detect both D-glucose and glucose analogs, such as 3-oxy-methyl-D-glucose (3OMG) uptake in tumors. As one of the recent international efforts aiming at pushing the boundaries of translation of the DGE MRI technique into clinical practice, a multidisciplinary team of eight partners came together to form the "glucoCEST Imaging of Neoplastic Tumors (GLINT)" consortium, funded by the Horizon 2020 European Commission. This paper summarizes the progress made to date both by these groups and others in increasing our knowledge of the underlying mechanisms related to this technique as well as translating it into clinical practice.

GLINT: GlucoCEST in neoplastic tumors at 3 T-clinical results of GlucoCEST in gliomas.

OBJECTIVE: Clinical relevance of dynamic glucose enhanced (DGE) chemical exchange saturation transfer (CEST) imaging has mostly been demonstrated at ultra-high field (UHF) due to low effect size. Results of a cohort study at clinical field strength are shown herein. MATERIALS AND METHODS: Motion and field inhomogeneity corrected T1ρ-based DGE (DGE⍴) images were acquired before, during and after a D-glucose injection with 6.3 s temporal resolution to detect accumulation in the brain. Six glioma patients with clear blood-brain barrier (BBB) leakage, two glioma patients with suspected BBB leakage, and three glioma patients without BBB leakage were scanned at 3 T. RESULTS: In high-grade gliomas with BBB leakage, D-glucose uptake could be detected in the gadolinium (Gd) enhancing region as well as in the tumor necrosis with a maximum increase of ∆DGE⍴ around 0.25%, whereas unaffected white matter did not show any significant DGE⍴ increase. Glioma patients without Gd enhancement showed no detectable DGE⍴ effect within the tumor. CONCLUSION: First application of DGE⍴ in a patient cohort shows an association between BBB leakage and DGE signal irrespective of the tumor grade. This indicates that glucoCEST corresponds more to the disruptions of BBB with Gd uptake than to the molecular tumor profile or tumor grading.

DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks-application to CEST imaging of the human brain at 3T.

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.

Structure or Exchange? On the Feasibility of Chemical Exchange Detection with Balanced Steady-State Free Precession in Tissue - An In Vitro Study.

Balanced steady-state free precession imaging has recently been suggested for chemical exchange detection (bSSFPX). The objective of this work is to investigate the contributions of microstructural, chemical shift and chemical exchange effects to the asymmetry of the bSSFP profile at field strengths of 3 T and 9.4 T. To this end, in vitro bSSFPX experiments are performed for a range of repetition times and flip angles in glucose water solutions with different MnCl2 concentrations and tissue homogenates obtained from the brainstem of pig brains. The experimental results are compared to multi-pool Bloch-McConnell simulations. Additionally, the influence of white matter tract geometry is analyzed ex vivo in pig brain hemispheres measured at two different angles with respect to B0 . The detectable bSSFP profile asymmetry in glucose solutions with tissue-like relaxation times and white matter homogenates was consistent with Bloch-McConnell simulations but relatively small. In intact white matter tracts, the asymmetry was dominated by structural effects with a strong dependency on tract orientation relative to B0 . In tracts perpendicular to B0 , the asymmetry was ≈ 3-4 times higher than in the homogenates, thus barely affected by chemical exchange effects. In conclusion, chemical exchange-related bSSFP profile asymmetries are detectable in tissue homogenates, however, the observed asymmetry level is generally low and prone to confounding structural effects rendering in vivo chemical exchange detection with bSSFP challenging in the brain.

Adaptive denoising for chemical exchange saturation transfer MR imaging.

High image signal-to-noise ratio (SNR) is required to reliably detect the inherently small chemical exchange saturation transfer (CEST) effects in vivo. In this study, it was demonstrated that identifying spectral redundancies of CEST data by principal component analysis (PCA) in combination with an appropriate data-driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra. The relationship between the number of relevant principal components and SNR was studied on fitted in vivo Z-spectra with artificially introduced noise. Three different data-driven criteria to automatically determine the optimal number of necessary components were investigated. In addition, these criteria facilitate straightforward assessment of data quality that could provide guidance for CEST MR protocols in terms of SNR. Insights were applied to achieve a robust denoising of highly sampled low power Z-spectra of the human brain at 3 and 7 T. The median criterion provided the best estimation for the optimal number of components consistently for all three investigated artificial noise levels. Application of the denoising technique to in vivo data revealed a considerable increase in image quality for the amide and rNOE contrast with a considerable SNR gain. At 7 T the denoising capability was quantified to be comparable or even superior to an averaging of six measurements. The proposed denoising algorithm enables an efficient and robust denoising of CEST data by combining PCA with appropriate data-driven truncation criteria. With this generally applicable technique at hand, small CEST effects can be reliably detected without the need for repeated measurements.

T1ρ-based dynamic glucose-enhanced (DGEρ) MRI at 3 T: method development and early clinical experience in the human brain.

PURPOSE: The aim of this study was to translate the T1 ρ-based dynamic glucose-enhanced (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 gadolinium-enhanced 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 B0 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 T1 -ce tumor ring enhancement. However, effect sizes are small and careful clinical application is necessary.

Partial volume mapping using magnetic resonance fingerprinting.

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient. PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T1 resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRF-mapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema. We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.

Relaxation-compensated APT and rNOE CEST-MRI of human brain tumors at 3 T.

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.

DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data - a proof of concept study.

PURPOSE: To determine the feasibility of employing the prior knowledge of well-separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z-spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach trained with 3 T and 9.4 T CEST spectral data from brains of the same subjects. METHODS: Highly spectrally resolved Z-spectra from the same volunteer were acquired by 3D-snapshot CEST MRI at 3 T and 9.4 T at low saturation power of B1 = 0.6 µT. The volume-registered 3 T Z-spectra-stack was then used as input data for a three layer deep neural network with the volume-registered 9.4 T fitted parameter stack as target data. RESULTS: An optimized neural net architecture could be found and verified in healthy volunteers. The gray-/white-matter contrast of the different CEST effects was predicted with only small deviations (Pearson R = 0.89). The 9.4 T prediction was less noisy compared to the directly measured CEST maps, although at the cost of slightly lower tissue contrast. Application to an unseen tumor patient measured at 3 T and 9.4 T revealed that tumorous tissue Z-spectra and corresponding hyper-/hypointensities of different CEST effects can also be predicted (Pearson R = 0.84). CONCLUSION: The 9.4 T CEST signals acquired at low saturation power can be accurately estimated from CEST imaging at 3 T using a neural network trained with coregistered 3 T and 9.4 T data of healthy subjects. The deepCEST approach generalizes to Z-spectra of tumor areas and might indicate whether additional ultrahigh-field (UHF) scans will be beneficial.

Possible artifacts in dynamic CEST MRI due to motion and field alterations.

PURPOSE: Dynamic CEST studies such as dynamic glucose enhanced imaging, have gained a lot of attention recently. The expected CEST effects after injection are rather small in tissue especially at clinical field strengths (0.5-2%). Small movements during the dynamic CEST measurement together with a subtraction-based evaluation can lead to pseudo CEST effects of the same order of magnitude. These artifacts are studied herein. METHODS: A brain tumor patient 3D-CEST baseline scan without glucose injection performed at 3 T is used to generate a virtual dynamic measurement introducing different kinds of simulated motion and B0 shifts. RESULTS: Minor motion (0.6 mm translations) and B0 artifacts (7 Hz shift) can lead to pseudo effects in the order of 1% in dynamic CEST imaging. Especially around tissue interfaces such as CSF borders or tumor affected areas, the pseudo effect patterns are non-intuitive and can be mistaken as dynamic agent uptake. CONCLUSION: Correction or mitigation for small motions is crucial for dynamic CEST imaging, especially in subjects with lesions. Concomitant B0 alterations can as well induce pseudo CEST effects at 3 T.

CEST imaging at 9.4 T using adjusted adiabatic spin-lock pulses for on- and off-resonant T1⍴-dominated Z-spectrum acquisition.

PURPOSE: The CEST experiment, with its correlation to rare proton species that are in exchange with the water pool, is very similar to the off-resonant water spin-lock (SL) experiment. In particular, low-power SL Z-spectrum acquisition allows insight into T1ρ and exchange effects with decreased direct water saturation. Because the available SL methods either require high B1 power or are instable in the presence of strong B1 and B0 inhomogeneity present at ultra-high fields, the goal of this study was to find a robust adiabatic SL pulse for on- and off-resonant application in the human brain at 9.4 T. METHODS: A series of Bloch simulations were used to find optimal pulse shape parameters of an adjusted hyperbolic secant pulse applicable in the low power regime typically used for exchange-weighted SL experiments. The optimized pulse was implemented and tested in phantom and in vivo experiments on a 9.4 T human scanner for on- and off-resonant T1ρ - and Z-spectrum measurements. RESULTS: The simulation yielded a feasible pulse shape, which yielded robust images, less sensitivity to B1 and B0 inhomogeneity compared with previous SL approaches and less direct water saturation, as well as a higher chemical exchange weighting compared with conventional CEST approaches. CONCLUSION: By adapting a pulse shape for low-power SL experiments, we were able to acquire robust on- and off-resonant adiabatic SL prepared images in vivo at 9.4 T. This development leads directly to SL Z-spectrum acquisition, beneficial for chemical-exchange-weighted MRI.

3D gradient echo snapshot CEST MRI with low power saturation for human studies at 3T.

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 (B0 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 tp  = 20 ms (total saturation time tsat = 3.2 seconds at 50% duty cycle) with B1 = 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.

Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions.

BACKGROUND: Conventional MRI can be limited in detecting subtle epileptic lesions or identifying active/epileptic lesions among widespread, multifocal lesions. PURPOSE: We developed a high-resolution 3D MR fingerprinting (MRF) protocol to simultaneously provide quantitative T1 , T2 , proton density, and tissue fraction maps for detection and characterization of epileptic lesions. STUDY TYPE: Prospective. POPULATION: National Institute of Standards and Technology (NIST) / International Society for Magnetic Resonance in Medicine (ISMRM) phantom, five healthy volunteers and 15 patients with medically intractable epilepsy undergoing presurgical evaluation with noninvasive or invasive electroclinical data. FIELD STRENGTH/SEQUENCE: 3D MRF scans and routine clinical epilepsy MR protocols were acquired at 3 T. ASSESSMENT: The accuracy of the T1 and T2 values were first evaluated using the NIST/ISMRM phantom. The repeatability was then estimated with both phantom and volunteers based on the coefficient of variance (CV). For epilepsy patients, all the maps were qualitatively reviewed for lesion detection by three independent reviewers (S.E.J., M.L., I.N.) blinded to clinical data. Region of interest (ROI) analysis was performed on T1 and T2 maps to quantify the multiparametric signal differences between lesion and normal tissues. Findings from qualitative review and quantitative ROI analysis were compared with patients' electroclinical data to assess concordance. STATISTICAL TESTS: Phantom results were compared using R-squared, and patient results were compared using linear regression models. RESULTS: The phantom study showed high accuracy with the standard values, with an R2 of 0.99. The volunteer study showed high repeatability, with an average CV of 4.3% for T1 and T2 in various tissue regions. For the 15 patients, MRF showed additional findings in four patients, with the remaining 11 patients showing findings consistent with conventional MRI. The additional MRF findings were highly concordant with patients' electroclinical presentation. DATA CONCLUSION: The 3D MRF protocol showed potential to identify otherwise inconspicuous epileptogenic lesions from the patients with negative conventional MRI diagnosis, as well as to correlate with different levels of epileptogenicity when widespread lesions were present. LEVEL OF EVIDENCE: 3. Technical Efficacy Stage: 3. J. Magn. Reson. Imaging 2019;49:1333-1346.

Chemical exchange saturation transfer MRI contrast in the human brain at 9.4 T.

PURPOSE: The high chemical shift separation at 9.4 T allows for selective saturation of proton pools in exchange with water protons. For the first time, highly selective and comprehensive chemical exchange saturation transfer (CEST) experiments were performed in the human brain at 9.4 T. This work provides insight into CEST signals in the human brain in comparison with existing animal studies, as well as with CEST effects in vivo at lower field strengths. METHODS: A novel snapshot-CEST method for human brain scans at 9.4 T was optimized and employed for highly-spectrally-resolved (95 offsets) CEST measurements in healthy subjects and one brain tumor patient. Reproducibility and stability between scans was verified in grey and white matter after B0, B1, and motion correction of the acquired 3D CEST volumes. Two-step Lorentzian fitting was used to further improve separation of spectrally discernible signals to create known and novel CEST contrast maps at 9.4 T. RESULTS: At a saturation power of B1 = 0.5 µT most selective CEST effects could be obtained in the human brain with high inter-scan reproducibility. While contrast behavior of previously measured signals at lower field, namely amide-, guanidyl- and NOE-CEST effects, could be reproduced, novel signals at 2.7 ppm, and -1.6 ppm could be verified in healthy subjects and in a brain tumor patient for the first time. CONCLUSION: High spectral resolution chemical exchange saturation transfer at 9.4 T allows deeper insights into the Z-spectrum structure of the human brain, and provides many different contrasts showing different correlations in healthy tissue and in tumor-affected areas of the brain, generating hypotheses for future investigations of in-vivo-CEST at UHF.

Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting.

PURPOSE: To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori. THEORY: Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity-promoting priors can be placed upon the solution. METHODS: An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior probability density to determine the magnetic resonance fingerprinting dictionary entries that contribute most significantly to mixed or pure voxels. RESULTS: Simulation results show that the algorithm is robust in finding the component tissues of mixed voxels. Preliminary in vivo data confirm this result, and show good agreement in voxels containing pure tissue. CONCLUSIONS: The Bayesian framework and algorithm shown provide accurate solutions for the partial-volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model. Magn Reson Med 80:159-170, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding.

PURPOSE: In radial imaging, projections may become "miscentered" due to gradient errors such as delays and eddy currents. These errors may result in image artifacts and can disrupt the reliability of direct current (DC) navigation. The proposed parallel imaging-based technique retrospectively estimates trajectory error from miscentered radial data without extra acquisitions, hardware, or sequence modification. THEORY AND METHODS: After phase correction, self-calibrated GRAPPA operator gridding (GROG) weights are iteratively applied to shift-miscentered projections toward the center of k-space. A search algorithm identifies the shift that aligns the peak k-space signals by maximizing the sum-of-squares DC signal estimate of each projection. The algorithm returns a trajectory estimate and a corrected radial k-space signal. RESULTS: Data from a spherical phantom, the head, and the heart demonstrate that image reconstruction with the estimated trajectory restores image quality and reduces artifacts such as streaks and signal voids. The DC signal level is increased and variability is reduced. CONCLUSION: Retrospective phase correction and iterative application of GROG can be used to successfully estimate the trajectory error in two-dimensional radial acquisitions for improved image reconstruction without requiring extra data acquisition or sequence modification.

Parallel MR imaging.

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.