Adaptive denoising for chemical exchange saturation transfer MR imaging

Accelerated CEST imaging through deep learning quantification from reduced frequency offsets

PURPOSE

To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.

METHODS

Fisher information gain analysis identified optimal frequency offsets (termed "Fisher offsets") for the multi-pool fitting model, maximizing information gain for the amplitude and the FWHM parameters. These offsets guided initial subsampling levels. A U-NET, trained on undersampled brain CEST images from 18 volunteers, produced CEST maps at 3 T with varied undersampling levels. Feasibility was first tested using retrospective undersampling at three levels, followed by prospective in vivo undersampling (15 of 53 offsets), reducing scan time significantly. Additionally, glioblastoma grade IV pathology was simulated to evaluate network performance in patient-like cases.

RESULTS

Traditional multi-pool models failed to quantify CEST maps from undersampled images (structural similarity index [SSIM] <0.2, peak SNR <20, Pearson r <0.1). Conversely, U-NET fitting successfully addressed undersampled data challenges. The study suggests CEST scan time reduction is feasible by undersampling 15, 25, or 35 of 53 Z-spectrum offsets. Prospective undersampling cut scan time by 3.5 times, with a maximum mean squared error of 4.4e-4, r = 0.82, and SSIM = 0.84, compared to the ground truth. The network also reliably predicted CEST values for simulated glioblastoma pathology.

CONCLUSION

The U-NET architecture effectively quantifies CEST maps from undersampled Z-spectra at various undersampling levels.

Synthesis of higher-B0 CEST Z-spectra from lower-B0 data via deep learning and singular value decomposition

Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B0) allow for better separation of Z-spectral "peaks," aiding signal interpretation and quantification. However, data acquisition at higher B0 is restricted by equipment access, field inhomogeneity and safety issues. Herein, we aim to synthesize higher-B0 Z-spectra from readily available data acquired with 3 T clinical scanners using a deep learning framework. Trained with simulation data using models based on Bloch-McConnell equations, this framework comprised two deep neural networks (DNNs) and a singular value decomposition (SVD) module. The first DNN identified B0 shifts in Z-spectra and aligned them to correct frequencies. After B0 correction, the lower-B0 Z-spectra were streamlined to the second DNN, casting into the key feature representations of higher-B0 Z-spectra, obtained through SVD truncation. Finally, the complete higher-B0 Z-spectra were recovered from inverse SVD, given the low-rank property of Z-spectra. This study constructed and validated two models, a phosphocreatine (PCr) model and a pseudo-in-vivo one. Each experimental dataset, including PCr phantoms, egg white phantoms, and in vivo rat brains, was sequentially acquired on a 3 T human and a 9.4 T animal scanner. Results demonstrated that the synthetic 9.4 T Z-spectra were almost identical to the experimental ground truth, showing low RMSE (0.11% ± 0.0013% for seven PCr tubes, 1.8% ± 0.2% for three egg white tubes, and 0.79% ± 0.54% for three rat slices) and high R2 (>0.99). The synthesized amide and NOE contrast maps, calculated using the Lorentzian difference, were also well matched with the experiments. Additionally, the synthesis model exhibited robustness to B0 inhomogeneities, noise, and other acquisition imperfections. In conclusion, the proposed framework enables synthesis of higher-B0 Z-spectra from lower-B0 ones, which may facilitate CEST MRI quantification and applications.

Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder

PURPOSE

To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods.

METHOD

The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of one-dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis-processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric.

RESULTS

In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings.

CONCLUSION

The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared with other methods.

Reproducibility of 3D chemical exchange saturation transfer (CEST) contrasts in the healthy brain at 3T

Chemical exchange saturation transfer (CEST) imaging may provide novel contrast for the diagnosis, prognosis, and monitoring of the progression or treatment of neurological applications. However, the reproducibility of prominent CEST contrasts like amide CEST and nuclear Overhauser enhancement (NOE) CEST must be characterized in healthy brain gray matter (GM) and white matter (GM) prior to clinical implementation. The objective of this study was to characterize the reproducibility of four different CEST contrasts in the healthy human brain. Using a 3T MRI scanner, two 3D CEST scans were acquired in 12 healthy subjects (7 females, mean age (± SD) 26 ± 4 years) approximately 10 days apart. Scan-rescan reproducibility was measured for four contrasts: amine/amide concentration-independent detection (AACID), Amide*, and inverse magnetization transfer ratio (MTRRex) contrast for amide and NOE. Reproducibility was evaluated between- and within-subjects using coefficients of variation (CV) and the percent difference between measurements. AACID and NOE-MTRRex contrasts demonstrated the lowest within-subject CVs (0.8-1.2% and 1.6-2.0%, respectively), between-subject CVs (1.2-2.1% and 3.4-4.2%, respectively), and percent difference (1.2-1.4% and 2.2-2.8%, respectively) for both GM and WM. AACID and NOE-MTRRex contrasts demonstrated the highest reproducibility and represented stable measurements suitable for characterizing changes in brain tissue caused by pathological processes.

pH Mapping of Gliomas Using Quantitative Chemical Exchange Saturation Transfer MRI: Quasi-Steady-State, Spillover-, and MT-Corrected Omega Plot Analysis

BACKGROUND

Quantitative in-situ pH mapping of gliomas is important for therapeutic interventions, given its significant association with tumor progression, invasion, and metastasis. Although chemical exchange saturation transfer (CEST) offers a noninvasive way for pH imaging based on the pH-dependent exchange rate (ksw), the reliable quantification of ksw in glioma remains constrained due to technical challenges.

PURPOSE

To quantify the pH of gliomas by measuring the proton exchange rate through optimized omega plot analysis.

STUDY TYPE

Prospective.

PHANTOMS/ANIMAL MODEL/SUBJECTS

Creatine and murine brain lysates phantoms, six rats with glioma xenograft model, and three patients with World Health Organization grade 2-4 gliomas.

FIELD STRENGTH/SEQUENCE

11.7 T, 7.0 T, CEST imaging, T2-weighted (T2W) imaging, and T1-mapping.

ASSESSMENT

Omega plot analysis, quasi-steady-state (QUASS) analysis, multi-pool Lorentzian fitting, amine and amide concentration-independent detection, pH enhanced method with the combination of amide and guanidyl (pHenh), and magnetization transfer ratio (MTR) were utilized for pH metric quantification. The clinical outcomes were determined through radiologic follow-up and histopathological analysis.

STATISTICAL TESTS

Mann-Whitney U test was performed to compare glioma with normal tissue, and Pearson's correlation analysis was used to assess the relationship between ksw and other parameters.

RESULTS

In vitro experiments reveal that the determined ksw at 2 ppm increases exponentially with pH (creatine phantoms: ksw = 106 + 0.147 × 10(pH-4.198); lysates: ksw = 185.1 + 0.101 × 10(pH-3.914)). Omega plot analysis exhibits a linear correlation between 1/MTRRex and 1/ω1 2 in the glioma xenografts (R2 > 0.98) and glioma patients (R2 > 0.99). The exchange rate in the rat glioma decreases compared to the contralateral normal tissue (349.46 ± 30.40 s-1 vs. 403.54 ± 51.01 s-1, P = 0.025), while keeping independence from changes in concentration (r = 0.5037, P = 0.095). Similar pattern was observed in human data.

DATA CONCLUSION

Utilizing QUASS-based, spillover-, and MT-corrected omega plot analysis for the measurement of exchange rates, offers a feasible method for quantifying pH within glioma.

LEVEL OF EVIDENCE

NA TECHNICAL EFFICACY: Stage 1.

Improved quantification in CEST-MRI by joint spatial total generalized variation

PURPOSE

In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool-Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal-to-noise ratio (SNR) was investigated.

THEORY AND METHODS

The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel-wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss-Newton method combined with a Primal-Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal-means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values.

RESULTS

The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities.

CONCLUSION

The proposed fitting method using TGV regularization leads to improved results over a range of different data-sets and noise levels. Furthermore, it can be applied to all Z-spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence.

Development and Validation of Four Different Methods to Improve MRI-CEST Tumor pH Mapping in Presence of Fat

CEST-MRI is an emerging imaging technique suitable for various in vivo applications, including the quantification of tumor acidosis. Traditionally, CEST contrast is calculated by asymmetry analysis, but the presence of fat signals leads to wrong contrast quantification and hence to inaccurate pH measurements. In this study, we investigated four post-processing approaches to overcome fat signal influences and enable correct CEST contrast calculations and tumor pH measurements using iopamidol. The proposed methods involve replacing the Z-spectrum region affected by fat peaks by (i) using a linear interpolation of the fat frequencies, (ii) applying water pool Lorentzian fitting, (iii) considering only the positive part of the Z-spectrum, or (iv) calculating a correction factor for the ratiometric value. In vitro and in vivo studies demonstrated the possibility of using these approaches to calculate CEST contrast and then to measure tumor pH, even in the presence of moderate to high fat fraction values. However, only the method based on the water pool Lorentzian fitting produced highly accurate results in terms of pH measurement in tumor-bearing mice with low and high fat contents.

A Denoising Convolutional Autoencoder for SNR Enhancement in Chemical Exchange Saturation Transfer imaging: (DCAE-CEST)

Purpose

To develop a SNR enhancement method for chemical exchange saturation transfer (CEST) imaging using a denoising convolutional autoencoder (DCAE), and compare its performance with state-of-the-art denoising methods.

Method

The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of 1D convolutions, nonlinearity applications and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in-vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric.

Results

In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirms the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared to other methods. While no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings.

Conclusion

The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared to other methods.

Comprehensive 7 T CEST: A clinical MRI protocol covering multiple exchange rate regimes

Chemical exchange saturation transfer (CEST) is a magnetic resonance (MR) imaging method providing molecular image contrasts based on indirect detection of low concentrated solutes. Previous CEST studies focused predominantly on the imaging of single CEST exchange regimes (e.g., slow, intermediate or fast exchanging groups). In this work, we aim to establish a so-called comprehensive CEST protocol for 7 T, covering the different exchange regimes by three saturation B1 amplitude regimes: low, intermediate and high. We used the results of previous publications and our own simulations in pulseq-CEST to produce a 7 T CEST protocol that has sensitivity to these three B1 regimes. With postprocessing optimization (simultaneous mapping of water shift and B1, B0-fitting, multiple interleaved mode saturation B1 correction, neural network employment (deepCEST) and analytical input feature reduction), we are able to shorten our initially 40 min protocol to 15 min and generate six CEST contrast maps simultaneously. With this protocol, we measured four healthy subjects and one patient with a brain tumor. We established a comprehensive CEST protocol for clinical 7 T MRI, covering three different B1 amplitude regimes. We were able to reduce the acquisition time significantly by more than 50%, while still maintaining decent image quality and contrast in healthy subjects and one patient with a tumor. Our protocol paves the way to perform comprehensive CEST studies in clinical scan times for hypothesis generation regarding molecular properties of certain pathologies, for example, ischemic stroke or high-grade brain tumours.

Specific and rapid guanidinium CEST imaging using double saturation power and QUASS analysis in a rodent model of global ischemia

PURPOSE

Guanidinium CEST is sensitive to metabolic changes and pH variation in ischemia, and it can offer advantages over conventional pH-sensitive amide proton transfer (APT) imaging by providing hyperintense contrast in stroke lesions. However, quantifying guanidinium CEST is challenging due to multiple overlapping components and a close frequency offset from water. This study aims to evaluate the applicability of a new rapid and model-free CEST quantification method using double saturation power, termed DSP-CEST, for isolating the guanidinium CEST effect from confounding factors in ischemia. To further reduce acquisition time, the DSP-CEST was combined with a quasi-steady state (QUASS) CEST technique to process non-steady-state CEST signals.

METHODS

The specificity and accuracy of the DSP-CEST method in quantifying the guanidinium CEST effect were assessed by comparing simulated CEST signals with/without the contribution from confounding factors. The feasibility of this method for quantifying guanidinium CEST was evaluated in a rat model of global ischemia induced by cardiac arrest and compared to a conventional multiple-pool Lorentzian fit method.

RESULTS

The DSP-CEST method was successful in removing all confounding components and quantifying the guanidinium CEST signal increase in ischemia. This suggests that the DSP-CEST has the potential to provide hyperintense contrast in stroke lesions. Additionally, the DSP-CEST was shown to be a rapid method that does not require the acquisition of the entire or a portion of the CEST Z-spectrum that is required in conventional model-based fitting approaches.

CONCLUSION

This study highlights the potential of DSP-CEST as a valuable tool for rapid and specific detection of viable tissues.

Boosting quantification accuracy of chemical exchange saturation transfer MRI with a spatial-spectral redundancy-based denoising method

Chemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal-to-noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial-spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw-rank constraint and Spectral local-smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low-dimensional subspace by leveraging the global spectral low-rank prior. Subsequently, a spatial nonlocal self-similarity prior is applied to the subspace-based images. Simultaneously, the spectral local-smoothness property of Z-spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state-of-the-art algorithms in terms of noise elimination. As a cost-effective and widely available post-processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.

Improved postprocessing of dynamic glucose-enhanced CEST MRI for imaging brain metastases at 3 T

BACKGROUND

Dynamic glucose-enhanced (DGE) chemical exchange saturation transfer (CEST) has the potential to characterize glucose metabolism in brain metastases. Since the effect size of DGE CEST is small at 3 T (< 1%), measurements of signal-to-noise ratios are challenging. To improve DGE detection, we developed an acquisition pipeline and extended image analysis for DGE CEST on a hybrid 3-T positron emission tomography/magnetic resonance imaging system.

METHODS

This cross-sectional study was conducted after local ethical approval. Static Z-spectra (from -100 to 100 ppm) were acquired to compare the use of 1.2 versus 2 ppm to calculate static glucose-enhanced (glucoCEST) maps in 10 healthy volunteers before and after glucose infusion. Dynamic CEST images were acquired during glucose infusion. Image analysis was optimized using motion correction, dynamic B0 correction, and principal component analysis (PCA) to improve the detection of DGE CEST in the sagittal sinus, cerebrospinal fluid, and grey and white matter. The developed DGE CEST pipeline was applied to four patients diagnosed with brain metastases.

RESULTS

GlucoCEST was strongest in healthy tissues at 2 ppm. Correcting for motion, B0, and use of PCA locally improved DGE maps. A larger contrast between healthy tissues and enhancing regions in brain metastases was found when dynamic B0 correction and PCA denoising were applied.

CONCLUSION

We demonstrated the feasibility of DGE CEST with our developed acquisition and analysis pipeline at 3 T in patients with brain metastases. This work enables a direct comparison of DGE CEST to 18F-fluoro-deoxy-D-glucose positron emission tomography of glucose metabolism in patients with brain metastases.

RELEVANCE STATEMENT

Contrast between brain metastasis and healthy brain tissue in DGE CEST MR images is improved by including principle component analysis and dynamic magnetic field correction during postprocessing. This approach enables the detection of increased DGE CEST signal in brain metastasis, if present.

KEY POINTS

• Despite the low signal-to-noise ratio, dynamic glucose-enhanced CEST MRI is feasible at 3 T. • Principal component analyses and dynamic magnetic field correction improve DGE CEST MRI. • DGE CEST MRI does not consequently show changes in brain metastases compared to healthy brain tissue. • Increased DGE CEST MRI in brain metastases, if present, shows overlap with contrast enhancement on T1-weighted images.

Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network

PURPOSE

To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising.

METHODS

DECENT is composed of two parallel pathways with different convolution kernel sizes aiming to extract the global and spectral features embedded in CEST images. Each pathway consists of a modified U-Net with residual Encoder-Decoder network and 3D convolution. Fusion pathway with 1 × 1 × 1 convolution kernel is utilized to concatenate two parallel pathways, and the output of DECENT is noise-reduced CEST images. The performance of DECENT was validated in numerical simulations, egg white phantom experiments, and ischemic mouse brain and human skeletal muscle experiments in comparison with existing state-of-the-art denoising methods.

RESULTS

Rician noise was added to CEST images to mimic a low SNR situation for numerical simulation, egg white phantom experiment, and mouse brain experiments, while human skeletal muscle experiments were of inherently low SNR. From the denoising results evaluated by peak SNR (PSNR) and structural similarity index (SSIM), the proposed deep learning-based denoising method (DECENT) can achieve better performance compared to existing CEST denoising methods such as NLmCED, MLSVD, and BM4D, avoiding complicated parameter tuning or time-consuming iterative processes.

CONCLUSIONS

DECENT can well exploit the prior spatiotemporal correlation knowledge of CEST images and restore the noise-free images from their noisy observations, outperforming state-of-the-art denoising methods.

Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.

Early-stage mapping of macromolecular content in APPNL-F mouse model of Alzheimer's disease using nuclear Overhauser effect MRI

Non-invasive methods of detecting early-stage Alzheimer's disease (AD) can provide valuable insight into disease pathology, improving the diagnosis and treatment of AD. Nuclear Overhauser enhancement (NOE) MRI is a technique that provides image contrast sensitive to lipid and protein content in the brain. These macromolecules have been shown to be altered in Alzheimer's pathology, with early disruptions in cell membrane integrity and signaling pathways leading to the buildup of amyloid-beta plaques and neurofibrillary tangles. We used template-based analyzes of NOE MRI data and the characteristic Z-spectrum, with parameters optimized for increase specificity to NOE, to detect changes in lipids and proteins in an AD mouse model that recapitulates features of human AD. We find changes in NOE contrast in the hippocampus, hypothalamus, entorhinal cortex, and fimbria, with these changes likely attributed to disruptions in the phospholipid bilayer of cell membranes in both gray and white matter regions. This study suggests that NOE MRI may be a useful tool for monitoring early-stage changes in lipid-mediated metabolism in AD and other disorders with high spatial resolution.

Isolation of amide proton transfer effect and relayed nuclear Overhauser enhancement effect at -3.5ppm using CEST with double saturation powers

PURPOSE

Quantifications of amide proton transfer (APT) and nuclear Overhauser enhancement (rNOE(-3.5)) mediated saturation transfer with high specificity are challenging because their signals measured in a Z-spectrum are overlapped with confounding signals from direct water saturation (DS), semi-solid magnetization transfer (MT), and CEST of fast-exchange pools. In this study, based on two canonical CEST acquisitions with double saturation powers (DSP), a new data-postprocessing method is proposed to specifically quantify the effects of APT and rNOE.

METHODS

For CEST imaging with relatively low saturation powers (ω 1 2 $$ {\upomega}_1^2 $$ ), both the fast-exchange CEST effect and the semi-solid MT effect roughly depend onω 1 2 $$ {\upomega}_1^2 $$ , whereas the slow-exchange APT/rNOE(-3.5) effect do not, which is exploited to isolate a part of the APT and rNOE effects from the confounding signals in this study. After a mathematical derivation for the establishment of the proposed method, numerical simulations based on Bloch equations are then performed to demonstrate its specificity to detections of the APT and rNOE effects. Finally, an in vivo validation of the proposed method is conducted using an animal tumor model at a 4.7 T MRI scanner.

RESULTS

The simulations show that DSP-CEST can quantify the effects of APT and rNOE and substantially eliminate the confounding signals. The in vivo experiments demonstrate that the proposed DSP-CEST method is feasible for the imaging of tumors.

CONCLUSION

The data-postprocessing method proposed in this study can quantify the APT and rNOE effects with considerably increased specificities and a reduced cost of imaging time.

7 tricks for 7 T CEST: Improving the reproducibility of multipool evaluation provides insights into the effects of age and the early stages of Parkinson's disease

The objective of the current study was to optimize the postprocessing pipeline of 7 T chemical exchange saturation transfer (CEST) imaging for reproducibility and to prove this optimization for the detection of age differences and differences between patients with Parkinson's disease versus normal subjects. The following 7 T CEST MRI experiments were analyzed: repeated measurements of a healthy subject, subjects of two age cohorts (14 older, seven younger subjects), and measurements of 12 patients with Parkinson's disease. A slab-selective, B 1 + -homogeneous parallel transmit protocol was used. The postprocessing, consisting of motion correction, smoothing, B 0 -correction, normalization, denoising, B 1 + -correction and Lorentzian fitting, was optimized regarding the intrasubject and intersubject coefficient of variation (CoV) of the amplitudes of the amide pool and the aliphatic relayed nuclear Overhauser effect (rNOE) pool within the brain. Seven "tricks" for postprocessing accomplished an improvement of the mean voxel CoV of the amide pool and the aliphatic rNOE pool amplitudes of less than 5% and 3%, respectively. These postprocessing steps are: motion correction with interpolation of the motion of low-signal offsets (1) using the amide pool frequency offset image as reference (2), normalization of the Z-spectrum using the outermost saturated measurements (3), B 0 correction of the Z-spectrum with moderate spline smoothing (4), denoising using principal component analysis preserving the 11 highest intensity components (5), B 1 + correction using a linear fit (6) and Lorentzian fitting using the five-pool fit model (7). With the optimized postprocessing pipeline, a significant age effect in the amide pool can be detected. Additionally, for the first time, an aliphatic rNOE contrast between subjects with Parkinson's disease and age-matched healthy controls in the substantia nigra is detected. We propose an optimized postprocessing pipeline for CEST multipool evaluation. It is shown that by the use of these seven "tricks", the reproducibility and, thus, the statistical power of a CEST measurement, can be greatly improved and subtle changes can be detected.

Linear projection-based chemical exchange saturation transfer parameter estimation

Isolated evaluation of multiparametric in vivo chemical exchange saturation transfer (CEST) MRI often requires complex computational processing for both correction of B0 and B1 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 B0 and B1 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.

CEST Imaging of the APT and ssMT predict the overall survival of patients with glioma at the first follow-up after completion of radiotherapy at 3T

BACKGROUND AND PURPOSE

Outcome prediction of patients with glioma early after the completion of radiotherapy represents a major clinical challenge. Previously, the prognostic value of chemical exchange saturation transfer (CEST) imaging has been demonstrated in patients with newly diagnosed glioma. The objective of this study was to assess the potential of amide proton transfer (APT)-, relayed nuclear Overhauser effect (rNOE)- and semi-solid magnetization transfer (ssMT)-imaging according to Zhou et al. (APTw_asym), Goerke et al. (MTR_RexAPT, MTR_RexNOE and MTR_RexMT) and Mehrabian et al. (PeakAreaAPT, PeakAreaNOE and MT_const) for the prognostication of the overall survival (OS) of patients with glioma at the first follow-up after the completion of radiotherapy.

MATERIALS AND METHODS

49 of 72 participants with diffuse glioma, who underwent CEST MRI at 3T between July 2018 and December 2021 4 to 6 weeks after the completion of radiotherapy, were analyzed. Contrast-enhancing tumor (CE) and whole tumor (WT) volumes were segmented on T2w-FLAIR and contrast-enhanced T1w images. Kaplan-Meier analysis and logrank-test were used for statistical analyses.

RESULTS

APTw imaging demonstrated the strongest association with OS (HR=4.66, p<0.001). The MT_const (HR=2.54, p=0.044) was associated with the OS of participants with residual contrast-enhancing glioma tissue, whilst the MTR_RexAPT (HR=2.44, p=0.056) showed a trend in this sub-cohort. The MTR_RexNOE, MTR_RexMT and PeakAreaNOE were not associated with survival.

CONCLUSION

Imaging of the APT and ssMT at the first follow-up 4 to 6 weeks after the completion of radiotherapy at 3T were associated with the overall survival of patients with glioma.

Ultrafast Z-spectroscopic imaging in vivo at 3T using through-slice spectral encoding (TS-UFZ)

PURPOSE

Acquisition of high-resolution Z-spectra for CEST or magnetization transfer contrast (MTC) MRI requires excessive scan times. Ultrafast Z-spectroscopy (UFZ) has been proposed to address this; however, the quality of in vivo UFZ spectra has been insufficient. Here, we present a simple approach to improve this.

THEORY AND METHODS

UFZ imaging acquires full Z-spectra by encoding the spectral dimension spatially via a gradient applied concurrently with the RF saturation pulse. Different from previous implementations, both this saturation gradient and its readout were applied in the slice direction, resulting in a relatively uniform voxel composition. Phase-encoding was applied in both in-plane directions, allowing additional under-sampling and acceleration.

RESULTS

In phantoms, UFZ imaging with through-slice Z-spectral encoding (TS-UFZ) provided Z-spectra of salicylic acid and egg white in excellent agreement with conventional acquisitions. In vivo brain Z-spectra were influenced by flow through the imaging slice which affected the Z-spectral baseline. Still, CEST signals could be quantified after baseline fitting and mapping the residual CEST signal. Amide proton transfer (APT) contrast intensities obtained by TS-UFZ were on the same order of magnitude as conventional CEST but with different contrast across slice which likely is a result of different tissue regions contributing.

CONCLUSION

TS-UFZ approach improves signal stability and spectral uniformity over previous implementations and allows high spectral-resolution imaging of saturation transfer effects in the human brain at 3T. This implementation allows for further acceleration by reducing phase encoding steps and thus opens up the possibility of mapping dynamic CEST signals in vivo with a practical temporal resolution.

DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification

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.

calf - Software for CEST Analysis with Lorentzian Fitting

Analysis of chemical exchange saturation transfer (CEST) MRI data requires sophisticated methods to obtain reliable results about metabolites in the tissue under study. CEST generates z-spectra with multiple components, each originating from individual molecular groups. The individual lines with Lorentzian line shape are mostly overlapping and disturbed by various effects. We present an elaborate method based on an adaptive nonlinear least squares algorithm that provides robust quantification of z-spectra and incorporates prior knowledge in the fitting process. To disseminate CEST to the research community, we developed software as part of this study that runs on the Microsoft Windows operating system and will be made freely available to the community. Special attention has been paid to establish a low entrance threshold and high usability, so that even less experienced users can successfully analyze CEST data.

Optimization Model of Signal-to-Noise Ratio for a Typical Polarization Multispectral Imaging Remote Sensor

The signal-to-noise ratio (SNR) is an important performance evaluation index of polarization spectral imaging remote sensors. The SNR-estimation method based on the existing remote sensor is not perfect. To improve the SNR of this model, a partial detector check slant direction is presented in this study, and a polarization extinction ratio related to the internal SNR model of a typical multispectral imaging remote sensor is combined with the vector radiative transfer model to construct the atmosphere 6SV-SNR coupling model. The new result is that the central wavelength of the detection spectrum, the observation zenith angle, and the extinction ratio all affect the SNR of the remote sensor, and the SNR increases with the increase in the central wavelength of the detection spectrum. It is proved that the model can comprehensively estimate the SNR of a typical polarization multispectral imaging remote sensor under different detection conditions, and it provides an important basis for the application evaluation of such remote sensors.

Sensitivity schemes for dynamic glucose-enhanced magnetic resonance imaging to detect glucose uptake and clearance in mouse brain at 3 T

We investigated three dynamic glucose-enhanced (DGE) MRI methods for sensitively monitoring glucose uptake and clearance in both brain parenchyma and cerebrospinal fluid (CSF) at clinical field strength (3 T). By comparing three sequences, namely, Carr-Purcell-Meiboom-Gill (CPMG), on-resonance variable delay multipulse (onVDMP), and on-resonance spin-lock (onSL), a high-sensitivity DGE MRI scheme with truncated multilinear singular value decomposition (MLSVD) denoising was proposed. The CPMG method showed the highest sensitivity in detecting the parenchymal DGE signal among the three methods, while both onVDMP and onSL were more robust for CSF DGE imaging. Here, onVDMP was applied for CSF imaging, as it displayed the best stability of the DGE results in this study. The truncated MLSVD denoising method was incorporated to further improve the sensitivity. The proposed DGE MRI scheme was examined in mouse brain with 50%/25%/12.5% w/w D-glucose injections. The results showed that this combination could detect DGE signal changes from the brain parenchyma and CSF with as low as a 12.5% w/w D-glucose injection. The proposed DGE MRI schemes could sensitively detect the glucose signal change from brain parenchyma and CSF after D-glucose injection at a clinically relevant concentration, demonstrating high potential for clinical translation.

Current emerging MRI tools for radionecrosis and pseudoprogression diagnosis

PURPOSE OF REVIEW

This review aims to cover current MRI techniques for assessing treatment response in brain tumors, with a focus on radio-induced lesions.

RECENT FINDINGS

Pseudoprogression and radionecrosis are common radiological entities after brain tumor irradiation and are difficult to distinguish from real progression, with major consequences on daily patient care. To date, shortcomings of conventional MRI have been largely recognized but morphological sequences are still used in official response assessment criteria. Several complementary advanced techniques have been proposed but none of them have been validated, hampering their clinical use. Among advanced MRI, brain perfusion measures increase diagnostic accuracy, especially when added with spectroscopy and susceptibility-weighted imaging. However, lack of reproducibility, because of several hard-to-control variables, is still a major limitation for their standardization in routine protocols. Amide Proton Transfer is an emerging molecular imaging technique that promises to offer new metrics by indirectly quantifying intracellular mobile proteins and peptide concentration. Preliminary studies suggest that this noncontrast sequence may add key biomarkers in tumor evaluation, especially in posttherapeutic settings.

SUMMARY

Benefits and pitfalls of conventional and advanced imaging on posttreatment assessment are discussed and the potential added value of APT in this clinicoradiological evolving scenario is introduced.

Pulseq-CEST: Towards multi-site multi-vendor compatibility and reproducibility of CEST experiments using an open-source sequence standard

PURPOSE

As the field of CEST grows, various novel preparation periods using different parameters are being introduced. At the same time, large, multisite clinical studies require clearly defined protocols, especially across different vendors. Here, we propose a CEST definition standard using the open Pulseq format for a shareable, simple, and exact definition of CEST protocols.

METHODS

We present the benefits of such a standard in three ways: (1) an open database on GitHub, where fully defined, human-readable CEST protocols can be shared; (2) an open-source Bloch-McConnell simulation to test and optimize CEST preparation periods in silico; and (3) a hybrid MR sequence that plays out the CEST preparation period and can be combined with any existing readout module.

RESULTS

The exact definition of the CEST preparation period, in combination with the flexible simulation, leads to a good match between simulations and measurements. The standard allowed finding consensus on three amide proton transfer-weighted protocols that could be compared in healthy subjects and a tumor patient. In addition, we could show coherent multisite results for a sophisticated CEST method, highlighting the benefits regarding protocol sharing and reproducibility.

CONCLUSION

With Pulseq-CEST, we provide a straightforward approach to standardize, share, simulate, and measure different CEST preparation schemes, which are inherently completely defined.

GlucoCEST MRI for the Evaluation Response to Chemotherapeutic and Metabolic Treatments in a Murine Triple-Negative Breast Cancer: A Comparison with[18F]F-FDG-PET

PURPOSE

Triple-negative breast cancer (TNBC) patients have usually poor outcome after chemotherapy and early prediction of therapeutic response would be helpful. [¹⁸F]F-FDG-PET/CT acquisitions are often carried out to monitor variation in metabolic activity associated with response to the therapy, despite moderate accuracy and radiation exposure limit its application. The glucoCEST technique relies on the use of unlabelled D-glucose to assess glucose uptake with conventional MRI scanners and is currently under active investigations at clinical level. This work aims at validating the potential of MRI-glucoCEST in monitoring the therapeutic responses in a TNBC tumor murine model.

PROCEDURES

Breast tumor (4T1)-bearing mice were treated with doxorubicin or dichloroacetate for 1 week. PET/CT with [¹⁸F]F-FDG and MRI-glucoCEST were performed at baseline and after 3 cycles of treatment. Metabolic changes measured with [¹⁸F]F-FDG-PET and glucoCEST were compared and evaluated with changes in tumor volumes.

RESULTS

Doxorubicin-treated mice showed a significant decrease in tumor growth when compared to the control group. GlucoCEST imaging provided metabolic response after three cycles of treatment. Conversely, no variations were detected in [¹⁸F]F-FDG uptake. Dichloroacetate-treated mice did not show any decrease either in tumor volume or in tumor metabolic activity as assessed by both glucoCEST and [¹⁸F]F-FDG-PET.

CONCLUSIONS

Metabolic changes during doxorubicin treatment can be predicted by glucoCEST imaging that appears more sensitive than [¹⁸F]F-FDG-PET in reporting on therapeutic response. These findings support the view that glucoCEST may be a sensitive technique for monitoring metabolic response, but future studies are needed to explore the accuracy of this approach in other tumor types and treatments.

Whole-brain amide CEST imaging at 3T with a steady-state radial MRI acquisition

PURPOSE

To develop a steady-state saturation with radial readout chemical exchange saturation transfer (starCEST) for acquiring CEST images at 3 Tesla (T). The polynomial Lorentzian line-shape fitting approach was further developed for extracting amideCEST intensities at this field.

METHOD

StarCEST MRI using periodically rotated overlapping parallel lines with enhanced reconstruction-based spatial sampling was implemented to acquire Z-spectra that are robust to brain motion. Multi-linear singular value decomposition postprocessing was applied to enhance the CEST SNR. The egg white phantom studies were performed at 3T to reveal the contributions to the 3.5 ppm CEST signal. Based on the phantom validation, the amideCEST peak was quantified using the polynomial Lorentzian line-shape fitting, which exploits the inverse relationship between Z-spectral intensity and the longitudinal relaxation rate in the rotating frame. The 3D turbo spin echo CEST was also performed to compare with the starCEST method.

RESULTS

The amideCEST peak showed a negligible peak B1 dependence between 1.2 µT and 2.4 µT. The amideCEST images acquired with starCEST showed much improved image quality, SNR, and motion robustness compared to the conventional 3D turbo spin echo CEST method with the same scan time. The amideCEST contrast extracted by the polynomial Lorentzian line-shape fitting method trended toward a stronger gray matter signal (1.32% ± 0.30%) than white matter (0.92% ± 0.08%; P = .02, n = 5). When calculating the magnetization transfer contrast and T1 -corrected rotating frame relaxation rate maps, amideCEST again was not significantly different for white matter and gray matter.

CONCLUSION

Rapid multi-slice amideCEST mapping can be achieved by the starCEST method (< 5 min) at 3T by combing with the polynomial Lorentzian line-shape fitting method.

Brain tissues have single-voxel signatures in multi-spectral MRI

Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

The traveling heads 2.0: Multicenter reproducibility of quantitative imaging methods at 7 Tesla

OBJECT

This study evaluates inter-site and intra-site reproducibility at ten different 7 T sites for quantitative brain imaging.

MATERIAL AND METHODS

Two subjects - termed the "traveling heads" - were imaged at ten different 7 T sites with a harmonized quantitative brain MR imaging protocol. In conjunction with the system calibration, MP2RAGE, QSM, CEST and multi-parametric mapping/relaxometry were examined.

RESULTS

Quantitative measurements with MP2RAGE showed very high reproducibility across sites and subjects, and errors were in concordance with previous results and other field strengths. QSM had high inter-site reproducibility for relevant subcortical volumes. CEST imaging revealed systematic differences between the sites, but reproducibility was comparable to results in the literature. Relaxometry had also very high agreement between sites, but due to the high sensitivity, differences caused by different applications of the B1 calibration of the two RF coil types used were observed.

CONCLUSION

Our results show that quantitative brain imaging can be performed with high reproducibility at 7 T and with similar reliability as found at 3 T for multicenter studies of the supratentorial brain.

Clinical routine acquisition protocol for 3D relaxation-compensated APT and rNOE CEST-MRI of the human brain at 3T

PURPOSE

The value of relaxation-compensated amide proton transfer (APT) and relayed nuclear Overhauser effect (rNOE) chemical exchange saturation transfer (CEST)-MRI has already been demonstrated in various neuro-oncological clinical applications. Recently, we translated the approach from 7T to a clinically relevant magnetic field strength of 3T. However, the overall acquisition time was still too long for a broad application in the clinical setting. The aim of this study was to establish a shorter acquisition protocol whilst maintaining the contrast behavior and reproducibility.

METHODS

Ten patients with glioblastoma were examined using the previous state-of-the-art acquisition protocol at 3T. The acquired spectral data were retrospectively reduced to find the minimal amount of required information that allows obtaining the same contrast behavior. To further reduce the acquisition time, also the image readout was accelerated and the pre-saturation parameters were further optimized.

RESULTS

In total, the overall acquisition time could be reduced from 19 min to under 7 min. One key finding was that, when evaluated by the relaxation-compensated inverse metric, a contrast correction for B₁ -field inhomogeneities at 3T can also be achieved reliably with CEST data at only one B₁ value. In contrast, a 1-point B₁ -correction was not sufficient for the common linear difference evaluation. The reproducibility of the new clinical routine acquisition protocol was similar to the previous state-of-the-art protocol with limits of agreement below 20%.

CONCLUSIONS

The substantial reduction in acquisition time by about 64% now allows the application of 3D relaxation-compensated APT and rNOE CEST-MRI for examinations of the human brain at 3T in clinical routine.

High-sensitivity CEST mapping using a spatiotemporal correlation-enhanced method

PURPOSE

To obtain high-sensitivity CEST maps by exploiting the spatiotemporal correlation between CEST images.

METHODS

A postprocessing method accomplished by multilinear singular value decomposition (MLSVD) was used to enhance the CEST SNR by exploiting the correlation between the Z-spectrum for each voxel and the low-rank property of the overall CEST data. The performance of this method was evaluated using CrCEST in ischemic mouse brain at 11.7 tesla. Then, MLSVD CEST was applied to obtain Cr, amide, and amine CEST maps of the ischemic mouse brain to demonstrate its general applications.

RESULTS

Complex-valued Gaussian noise was added to CEST k-space data to mimic a low SNR situation. MLSVD CEST analysis was able to suppress the noise, recover the degraded CEST peak, and provide better CrCEST quality compared to the smoothing and singular value decomposition (SVD)-based denoising methods. High-resolution Cr, amide, and amine CEST maps of an ischemic stroke using MLSVD CEST suggest that CrCEST is also a sensitive pH mapping method, and a wide range of pH changes can be detected by combing CrCEST with amine CEST at high magnetic fields.

CONCLUSION

MLSVD CEST provides a simple and efficient way to improve the SNR of CEST images.

Optimized dualCEST-MRI for imaging of endogenous bulk mobile proteins in the human brain

Dual-frequency irradiation chemical exchange saturation transfer (dualCEST) allows imaging of endogenous bulk mobile proteins by selectively measuring the intramolecular spin diffusion. The resulting specificity to changes in the concentration, molecular size, and folding state of mobile proteins is of particular interest as a marker for neurodegenerative diseases and cancer. Until now, application of dualCEST in clinical trials was prevented by the inherently small signal-to-noise ratio and the resulting comparatively long examination time. In this study, we present an optimized acquisition protocol allowing 3D dualCEST-MRI examinations in a clinically relevant time frame. The optimization comprised the extension of the image readout to 3D, allowing a retrospective co-registration and application of denoising strategies. In addition, cosine-modulated dual-frequency presaturation pulses were implemented with a weighted acquisition scheme of the necessary frequency offsets. The optimization resulted in a signal-to-noise ratio gain by a factor of approximately 8. In particular, the application of denoising and the motion correction were the most crucial improvement steps. In vitro experiments verified the preservation of specificity of the dualCEST signal to proteins. Good-to-excellent intra-session and good inter-session repeatability was achieved, allowing reliable detection of relative signal differences of about 16% or higher. Applicability in a clinical setting was demonstrated by examining a patient with glioblastoma. The optimized acquisition protocol for dualCEST-MRI at 3 T enables selective imaging of endogenous bulk mobile proteins under clinically relevant conditions.