This work provides a new method for fast post-processing of MRI data acquired using the WASAB1 sequence for simultaneous B 0 and B 1 mapping, used in CEST imaging for field inhomogeneity corrections. We are proposing a new processing method with outstanding acceleration of the parameter estimation procedure, without compromising the stability of the estimation. The stability of the method is demonstrated on phantom data and in vivo 3 Tesla clinical data.
This novel study explores amide proton transfer weighted (APTw) imaging in people with relapsing-remitting multiple sclerosis (pw-RRMS). We evaluated the APTw signal intensity in selected MS lesions and normal-appearing white matter (NAMW) regions in 9 pw-RRMS. Compared to NAWM regions, a statistically significant increase in APTw signal intensity was observed in the MS lesions. Elevated APTw signal intensity could mark increased mobile myelin proteins decomposition and accumulation from the demyelination process.
Purpose Dynamic glucose‐enhanced (DGE) MRI relates to a group of exchange‐based MRI techniques where the uptake of glucose analogues is studied dynamically. However, motion artifacts can be mistaken for true DGE effects, while motion correction may alter true signal effects. The aim was to design a numerical human brain phantom to simulate a realistic DGE MRI protocol at 3T that can be used to assess the influence of head movement on the signal before and after retrospective motion correction. Methods MPRAGE data from a tumor patient were used to simulate dynamic Z‐spectra under the influence of motion. The DGE responses for different tissue types were simulated, creating a ground truth. Rigid head movement patterns were applied as well as physiological dilatation and pulsation of the lateral ventricles and head‐motion‐induced B 0 ‐changes in presence of first‐order shimming. The effect of retrospective motion correction was evaluated. Results Motion artifacts similar to those previously reported for in vivo DGE data could be reproduced. Head movement of 1 mm translation and 1.5 degrees rotation led to a pseudo‐DGE effect on the order of 1% signal change. B 0 effects due to head motion altered DGE changes due to a shift in the water saturation spectrum. Pseudo DGE effects were partly reduced or enhanced by rigid motion correction depending on tissue location. Conclusion DGE MRI studies can be corrupted by motion artifacts. Designing post‐processing methods using retrospective motion correction including B 0 correction will be crucial for clinical implementation. The proposed phantom should be useful for evaluation and optimization of such techniques.
Isolated evaluation of multiparametric in vivo chemical exchange saturation transfer (CEST) MRI often requires complex computational processing for both correction of B 0 and B 1 inhomogeneity and contrast generation. For that, sufficiently densely sampled Z‐spectra need to be acquired. The list of acquired frequency offsets largely determines the total CEST acquisition time, while potentially representing redundant information. In this work, a linear projection‐based multiparametric CEST evaluation method is introduced that offers fast B 0 and B 1 inhomogeneity correction, contrast generation and feature selection for CEST data, enabling reduction of the overall measurement time. To that end, CEST data acquired at 7 T in six healthy subjects and in one brain tumor patient were conventionally evaluated by interpolation‐based inhomogeneity correction and Lorentzian curve fitting. Linear regression was used to obtain coefficient vectors that directly map uncorrected data to corrected Lorentzian target parameters. L1‐regularization was applied to find subsets of the originally acquired CEST measurements that still allow for such a linear projection mapping. The linear projection method allows fast and interpretable mapping from acquired raw data to contrast parameters of interest, generalizing from healthy subject training data to unseen healthy test data and to the tumor patient dataset. The L1‐regularization method shows that a fraction of the acquired CEST measurements is sufficient to preserve tissue contrasts, offering up to a 2.8‐fold reduction of scan time. Similar observations as for the 7‐T data can be made for data from a clinical 3‐T scanner. Being a fast and interpretable computation step, the proposed method is complementary to neural networks that have recently been employed for similar purposes. The scan time acceleration offered by the L1‐regularization (“CEST‐LASSO”) constitutes a step towards better applicability of multiparametric CEST protocols in a clinical context.
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.
High-resolution 3D MR imaging is necessary for the detailed assessment of focal pathologies, such as cortical lesions. However, high-resolution demands tradeoffs with acceleration and SNR, which is difficult to address with standard machine learning reconstructions due to the infeasibility of collecting large datasets of fully sampled data. Using a dataset of high-resolution (0.5mm isotropic), 3D, 7T MP2RAGE scans of multiple sclerosis patients, we show that a self-supervised reconstruction from one scan, requiring no fully sampled data, has higher apparent SNR than a median of three scans, currently used for assessment, with comparable tissue contrast and lesion conspicuity.
Parallel transmission (pTx) can significantly improve readout-segment EPI (rsEPI) diffusion-weighted imaging (DWI) at 7T when compared to the non-pTx sequence. However, no study has been done to assess the repeatability of pTx-DWI. Thus, we conducted a test-retest study to evaluate the impact pTx pulses have on the repeatability of ADC measures in a rsEPI[DP1] DWI sequence at 7T. Overall, pTx-DWI had higher SNR and can potentially improve the repeatability for intra- and inter-session ADC measures even when different B1-shim coefficients are used for different sessions. This suggests that pTx has an important role in quantittaive imaging studies at 7T.
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 B 1 inhomogeneities. The input data for a neural feed‐forward network consisted of 7 T in vivo uncorrected Z ‐spectra of a single B 1 level, and a B 1 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, B 0 ‐ and B 1 ‐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 B 1 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 B 0 ‐ and B 1 ‐corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.
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