Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing.

PURPOSE: To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. METHOD: A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. RESULTS: The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. CONCLUSION: The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.

Effect of inhaled oxygen level on dynamic glucose-enhanced MRI in mouse brain.

PURPOSE: To investigate the effect of inhaled oxygen level on dynamic glucose enhanced (DGE) MRI in mouse brain tissue and CSF at 3 T. METHODS: DGE data of brain tissue and CSF from mice under normoxia or hyperoxia were acquired in independent and interleaved experiments using on-resonance variable delay multi-pulse (onVDMP) MRI. A bolus of 0.15 mL filtered 50% D-glucose was injected through the tail vein over 1 min during DGE acquisition. MRS was acquired before and after DGE experiments to confirm the presence of D-glucose. RESULTS: A significantly higher DGE effect under normoxia than under hyperoxia was observed in brain tissue (p = 0.0001 and p = 0.0002 for independent and interleaved experiments, respectively), but not in CSF (p > 0.3). This difference is attributed to the increased baseline MR tissue signal under hyperoxia induced by a shortened T1 and an increased BOLD effect. When switching from hyperoxia to normoxia without glucose injection, a signal change of ˜3.0% was found in brain tissue and a signal change of ˜1.5% was found in CSF. CONCLUSIONS: DGE signal was significantly lower under hyperoxia than that under normoxia in brain tissue, but not in CSF. The reason is that DGE effect size of brain tissue is affected by the baseline signal, which could be influenced by T1 change and BOLD effect. Therefore, DGE experiments in which the oxygenation level is changed from baseline need to be interpreted carefully.

Creatine mapping of the brain at 3T by CEST MRI.

PURPOSE: To assess the feasibility of CEST-based creatine (Cr) mapping in brain at 3T using the guanidino (Guan) proton resonance. METHODS: Wild type and knockout mice with guanidinoacetate N-methyltransferase deficiency and low Cr and phosphocreatine (PCr) concentrations in the brain were used to assign the Cr and protein-based arginine contributions to the GuanCEST signal at 2.0 ppm. To quantify the Cr proton exchange rate, two-step Bloch-McConnell fitting was used to fit the extracted CrCEST line-shape and multi-B1 Z-spectral data. The pH response of GuanCEST was simulated to demonstrate its potential for pH mapping. RESULTS: Brain Z-spectra of wild type and guanidinoacetate N-methyltransferase deficiency mice show a clear Guan proton peak at 2.0 ppm at 3T. The CrCEST signal contributes ∼23% to the GuanCEST signal at B1 = 0.8 µT, where a maximum CrCEST effect of 0.007 was detected. An exchange rate range of 200-300 s-1 was estimated for the Cr Guan protons. As revealed by the simulation, an elevated GuanCEST in the brain is observed when B1 is less than 0.4 µT at 3T, when intracellular pH reduces by 0.2. Conversely, the GuanCEST decreases when B1 is greater than 0.4 µT with the same pH drop. CONCLUSIONS: CrCEST mapping is possible at 3T, which has potential for detecting intracellular pH and Cr concentration in brain.

CEST effect of dimethyl sulfoxide at negative offset frequency.

Dimethyl sulfoxide (DMSO) has wide biomedical applications such as cryoprotectant and hydrophobic drug carrier. Here, we report for the first time that DMSO can generate a distinctive chemical exchange saturation transfer (CEST) signal at around -2 ppm. Structural analogs of DMSO, including aprotic and protic solvents, also demonstrated CEST signals from -1.4 to -3.8 ppm. When CEST detectable barbituric acid (BA) was dissolved in DMSO solution and was co-loaded to liposome, two obvious peaks at 5 and -2 ppm were observed, indicating that DMSO and related solvent system can be monitored in a label-free manner via CEST, which can be further applied to imaging drug nanocarriers. With reference to previous studies, there could be molecular interactions or magnetization transfer pathways, such as the relayed nuclear Overhauser enhancement (rNOE), that lead to this detectable CEST contrast at negative offset frequencies of the Z-spectrum. Our findings suggest that small molecules of organic solvents could be involved in magnetization transfer processes with water and readily detected by CEST magnetic resonance imaging (MRI), providing a new avenue for detecting solvent-water and solvent-drug interactions.

The effect of aquaporin-4 inhibition on cerebrospinal fluid-tissue water exchange in mouse brain detected by magnetization transfer indirect spin labeling MRI.

The fluid transport of cerebrospinal fluid (CSF) and interstitial fluid in surrounding tissues plays an important role in the drainage pathway that facilitates waste clearance from the brain. This pathway is known as the glymphatic or perivascular system, and its functions are dependent on aquaporin-4 (AQP4). Recently, magnetization transfer indirect spin labeling (MISL) magnetic resonance imaging (MRI) has been proposed as a noninvasive and noncontrast-enhanced method for detecting water exchange between CSF and brain tissue. In this study, we first optimized the MISL sequence at preclinical 3 T MRI, and then studied the correlation of MISL in CSF with magnetization transfer (MT) in brain tissue, as well as the altered water exchange under AQP4 inhibition, using C57BL/6 mice. Results showed a strong correlation of MISL signal with MT signal. With the AQP4 inhibitor, we observed a significant decrease in MISL value (P < 0.05), suggesting that the hampered AQP4 activity led to decreased water exchange between CSF and brain tissue or the impairment of the glymphatic function. Overall, our findings demonstrate the potential application of MISL in assessing brain water exchange at 3 T MRI and its potential clinical translation.

Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI.

Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasets to fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information.

Chemical Exchange Saturation Transfer Magnetic Resonance Imaging for Longitudinal Assessment of Intracerebral Hemorrhage and Deferoxamine Treatment at 3T in a Mouse Model.

BACKGROUND: Noninvasive imaging of molecular alterations after intracerebral hemorrhage (ICH) could provide valuable information to guide and monitor treatments. Chemical exchange saturation transfer (CEST) magnetic resonance imaging has demonstrated promises in identifying proliferation, necrosis, and changes in cellularity in brain tumors. Here, we applied CEST magnetic resonance imaging to monitor molecular changes in hematoma without and with treatment noninvasively over 2 weeks at 3T using endogenous contrast. METHODS: CEST contrast related to proteins at 3.5 ppm (amide proton transfer) and proteins/lipids at -3.5 ppm (relayed nuclear overhauser effect [rNOE]) were examined over 14 days in a collagenase-induced ICH mouse model. Imaging findings were validated with immunohistochemistry based on the ICH neuropathology. We also examined iron-containing phantoms that mimicked iron concentrations in hematoma to ensure the iron will not attenuate the CEST contrast during disease progression. Based on the validity of the CEST contrast of hematoma, we further examined related molecular alterations under iron-chelation treatment with deferoxamine. RESULTS: We observed the temporal and spatial differences of CEST contrasts between rNOE at -3.5 ppm and amide proton transfer at 3.5 ppm, in which the core and perihematoma could be identified by rNOE on day 3 and day 14, and amide proton transfer on day 1, day 7, and day 14. Moreover, we observed a 25.7% significant reduction (P<0.05) of rNOE contrast after deferoxamine treatment to the ICH mice on day 3, which was not observable in amide proton transfer contrast. Our histology data indicated that rNOE primarily correlated with the myelin pathology, and amide proton transfer could reflect the cellularity increase at hematoma up to day 7. CONCLUSIONS: Significant rNOE changes correlated well with histologic findings, especially myelin lipids, and regional characteristics in hematoma indicate the uniqueness of CEST magnetic resonance imaging in monitoring molecular changes during ICH and treatment.

IL-1ß and TNF-α play an important role in modulating the risk of periodontitis and Alzheimer's disease.

BACKGROUND: Systemic activation of the immune system can exert detrimental effects on the central nervous system. Periodontitis, a chronic disease of the oral cavity, is a common source of systemic inflammation. Neuroinflammation might be a result of this to accelerate progressive deterioration of neuronal functions during aging or exacerbate pre-existing neurodegenerative diseases, such as Alzheimer's disease. With advancing age, the progressive increase in the body's pro-inflammatory status favors the state of vulnerability to both periodontitis and Alzheimer's disease. In the present study, we sought to delineate the roles of cytokines in the pathogenesis of both diseases. METHODS: To examine the impacts of periodontitis on the onset and progression of Alzheimer's disease, 6-month-old female 3 × Tg-AD mice and their age-matched non-transgenic mice were employed. Periodontitis was induced using two different experimental models: heat-killed bacterial-induced periodontitis and ligature-induced periodontitis. To delineate the roles of pro-inflammatory cytokines in the pathogenesis of periodontitis and Alzheimer's disease, interleukin 1 beta (IL-1ß) and tumor necrosis factor-alpha (TNF-α) were also injected into the buccal mandibular vestibule of mice. RESULTS: Here, we show that IL-1ß and TNF-α were two of the most important and earliest cytokines upregulated upon periodontal infection. The systemic upregulation of these two cytokines promoted a pro-inflammatory environment in the brain contributing to the development of Alzheimer's disease-like pathology and cognitive dysfunctions. Periodontitis-induced systemic inflammation also enhanced brain inflammatory responses and subsequently exacerbated Alzheimer's disease pathology and cognitive impairment in 3 × Tg-AD mice. The role of inflammation in connecting periodontitis to Alzheimer's disease was further affirmed in the conventional magnetization transfer experiment in which increased glial responses resulting from periodontitis led to decreased magnetization transfer ratios in the brain of 3 × Tg-AD mice. CONCLUSIONS: Systemic inflammation resulting from periodontitis contributed to the development of Alzheimer's disease tau pathology and subsequently led to cognitive decline in non-transgenic mice. It also potentiated Alzheimer's disease pathological features and exacerbated impairment of cognitive function in 3 × Tg-AD mice. Taken together, this study provides convincing evidence that systemic inflammation serves as a connecting link between periodontitis and Alzheimer's disease.

Chemical exchange saturation transfer MRI detects myelin changes in cuprizone mouse model at 3T.

Chemical exchange saturation transfer (CEST) sensitively detects molecular alterations in the brain, such as relayed nuclear Overhauser effect (rNOE) CEST contrast at -3.5 ppm representing aliphatic protons in both lipids and proteins, and CEST contrast at 3.5 ppm correlating with amide proton in proteins. Myelin is rich in lipids and proteins, and therefore CEST can be explored as a biomarker for myelin pathology, which could contribute to the diagnosis and prognosis of multiple sclerosis (MS). In the current study, we investigate the specificity of aliphatic rNOE and the amide pool in myelin detection using the cuprizone (CPZ) mouse model, which recapitulates the demyelination and remyelination of MS. In this study, preclinical 3T MRI was performed in 19 male C57BL/6 mice. Mice in the normal control (NC) group (n = 9) were fed a normal diet for the whole course, while mice in the CPZ group (n = 10) were fed with CPZ for 10 weeks, followed by 4 weeks with a normal diet. The CEST contrast of rNOE (-3.5 ppm) and amide (3.5 ppm) in brain regions of the corpus callosum (CC) and the caudate putamen were compared. Statistical differences between the groups were calculated using two-way ANOVA. We observed significantly decreased rNOE (NC: 4.85% ± 0.09%/s vs. CPZ: 3.88% ± 0.18%/s, p = 0.007) and amide pool (NC: 3.20% ± 0.10%/s vs. CPZ: 2.46% ± 0.16%/s, p = 0.02) in the CC after 8 weeks on CPZ diet (p < 0.05). Moreover, the rNOE in the CPZ group recovered to a level comparable with the NC group at week 14 (p = 0.39), while amide remained at a level as low as that for the NC group (p = 0.051). Significant rNOE and amide changes, validated by immunohistochemistry results for demyelination and remyelination, demonstrate the huge potential of CEST for revealing myelin pathology, which has implications for MS identification at the clinical field strength of 3T.

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

The article from this special issue was previously published in NMR In Biomedicine , Volume 35, Issue 3, 2022. For completeness we are including the title page of the article below. The full text of the article can be read in Issue 35:3 on Wiley Online Library: https://doi.org/10.1002/nbm.4640.

Ultrafast water-fat separation using deep learning-based single-shot MRI.

PURPOSE: To present a deep learning-based reconstruction method for spatiotemporally encoded single-shot MRI to simultaneously obtain water and fat images. METHODS: Spatiotemporally encoded MRI is an ultrafast branch that can encode chemical shift information due to its special quadratic phase modulation. A deep learning approach using a 2D U-Net was proposed to reconstruct spatiotemporally encoded signal and obtain water and fat images simultaneously. The training data for U-Net were generated by MRiLab software (version 1.3) with various synthetic models. Numerical simulations and experiments on ex vivo pork and in vivo rats at a 7.0 T Varian MRI system (Agilent Technologies, Santa Clara, CA) were performed, and the deep learning results were compared with those obtained by state-of-the-art algorithms. The structural similarity index and signal-to-ghost ratio were used to evaluate the residual artifact of different reconstruction methods. RESULTS: With a well-trained neural network, the proposed deep learning approach can accomplish signal reconstruction within 0.46 s on a personal computer, which is comparable with the conjugate gradient method (0.41 s) and much faster than the state-of-the-art super-resolved water-fat image reconstruction method (30.31 s). The results of numerical simulations, ex vivo pork experiments, and in vivo rat experiments demonstrate that the deep learning approach can achieve better fidelity and higher spatial resolution compared to the other 2 methods. The deep learning approach also has a great advantage in artifact suppression, as indicated by the signal-to-ghost ratio results. CONCLUSION: Spatiotemporally encoded MRI with deep learning can provide ultrafast water-fat separation with better performance compared to the state-of-the-art methods.

Deep neural network based CEST and AREX processing: Application in imaging a model of Alzheimer's disease at 3 T.

PURPOSE: To optimize and apply deep neural network based CEST (deepCEST) and apparent exchange dependent-relaxation (deepAREX) for imaging the mouse brain with Alzheimer's disease (AD) at 3T MRI. METHODS: CEST and T1 data of central and anterior brain slices of 10 AD mice and 10 age-matched wild type (WT) mice were acquired at a 3T animal MRI scanner. The networks of deepCEST/deepAREX were optimized and trained on the WT data. The CEST/AREX contrasts of AD and WT mice predicted by the networks were analyzed and further validated by immunohistochemistry. RESULTS: After optimization and training on CEST data of WT mice, deepCEST/deepAREX could rapidly (~1 s) generate precise CEST and AREX results for unseen CEST data of AD mice, indicating the accuracy and generalization of the networks. Significant lower amide weighted (3.5 ppm) signal related to amyloid ß-peptide (Aß) plaque depositions, which was validated by immunohistochemistry results, was detected in both central and anterior brain slices of AD mice compared to WT mice. Decreased magnetization transfer (MT) signal was also found in AD mice especially in the anterior slice. CONCLUSION: DeepCEST/deepAREX could rapidly generate accurate CEST/AREX contrasts in animal study. The well-optimized deepCEST/deepAREX have potential for AD differentiation at 3T MRI.

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.

Dynamic contrast-enhanced CEST MRI using a low molecular weight dextran.

Natural and synthetic sugars have great potential for developing highly biocompatible and translatable chemical exchange saturation transfer (CEST) MRI contrast agents. In this study, we aimed to develop the smallest clinically available form of dextran, Dex1 (molecular weight, MW ~ 1 kDa), as a new CEST agent. We first characterized the CEST properties of Dex1 in vitro at 11.7 T and showed that the Dex1 had a detectable CEST signal at ~1.2 ppm, attributed to hydroxyl protons. In vivo CEST MRI studies were then carried out on C57BL6 mice bearing orthotopic GL261 brain tumors (n = 5) using a Bruker BioSpec 11.7 T MRI scanner. Both steady-state full Z-spectral images and single offset (1.2 ppm) dynamic dextran-enhanced (DDE) images were acquired before and after the intravenous injection of Dex1 (2 g/kg). The steady-state Z-spectral analysis showed a significantly higher CEST contrast enhancement in the tumor than in contralateral brain (∆MTRasym1.2 ppm  = 0.010 ± 0.006 versus 0.002 ± 0.008, P = 0.0069) at 20 min after the injection of Dex1. Pharmacokinetic analyses of DDE were performed using the area under the curve (AUC) in the first 10 min after Dex1 injection, revealing a significantly higher uptake of Dex1 in the tumor than in brain tissue for tumor-bearing mice (AUC[0-10 min] = 21.9 ± 4.2 versus 5.3 ± 6.4%·min, P = 0.0294). In contrast, no Dex1 uptake was foundling in the brains of non-tumor-bearing mice (AUC[0-10 min] = -1.59 ± 2.43%·min). Importantly, the CEST MRI findings were consistent with the measurements obtained using DCE MRI and fluorescence microscopy, demonstrating the potential of Dex1 as a highly translatable CEST MRI contrast agent for assessing tumor hemodynamics.

Relayed nuclear Overhauser enhancement imaging with magnetization transfer contrast suppression at 3 T.

PURPOSE: To develop a pulsed CEST magnetization-transfer method for rapidly acquiring relayed nuclear Overhauser enhancement (rNOE)-weighted images with magnetic transfer contrast (MTC) suppression at clinical field strength (3 T). METHODS: Using a pulsed CEST magnetization-transfer method with low saturation powers (B1 ) and long mixing time (tmix ) to suppress contributions due to strong MTC from solid-like macromolecules, a low B1 also minimized direct water saturation. These MTC contributions were further reduced by subtracting the Z-spectral signals at two or three offsets by assuming that the residual MTC is a linear function between -3.5 ppm and -12.5 ppm. RESULTS: Phantom studies of a lactic acid (Lac) solution mixed with cross-linked bovine serum albumin show that strong MTC interference has a significant impact on the optimum B1 for detecting rNOEs, due to lactate binding. The MTC could be effectively suppressed using a pulse train with a B1 of 0.8 µT, a pulse duration (tp ) of 40 ms, a tmix of 60 ms, and a pulse number (N) of 30, while rNOE signal was well maintained. As a proof of concept, we applied the method in mouse brain with injected hydrogel and a cell-hydrogel phantom. Results showed that rNOE-weighted images could provide good contrast between brain/cell and hydrogel. CONCLUSION: The developed pulsed CEST magnetization-transfer method can achieve MTC suppression while preserving most of the rNOE signal at 3 T, which indicates the potential for translation of this technique to clinical applications related to mobile proteins/lipids change.

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.

A fast chemical exchange saturation transfer imaging scheme based on single-shot spatiotemporal encoding.

PURPOSE: To design a new approach that can not only keep the spatial and temporal resolution but also have better built-in immunity to magnetic field inhomogeneity and chemical shift effects than the single-shot echo planar imaging (EPI) for chemical exchange saturation transfer (CEST) MRI. METHOD: The single-shot spatiotemporally encoded (SPEN) MRI sequence was combined with a continuous wave saturation pulse for fast CEST MRI (CEST-SPEN MRI). The resulting images were super-resolved reconstructed by a hybrid method that solves the l1 norm minimization together with total variation (TV) regularization. Partial Lorentzian fitting was used to analyze the subsequent Z-spectra. RESULTS: Experimental results of a creatine phantom and in vivo tumor rat brains show that CEST-SPEN MRI has good capability in providing CEST-based and NOE-based contrast images. CONCLUSIONS: Compared with CEST-EPI, CEST-SPEN MRI has better immunity to magnetic field inhomogeneity and provides better contrast images within identical acquisition time, especially under an identical inhomogeneous field. Magn Reson Med 77:1786-1796, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI.

One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to accelerate MRI acquisition, while this could introduce artifacts and reduce signal-to-noise ratio (SNR). To accelerate CEST-MRI acquisition while maintaining suitable image quality, we proposed an attention-based multioffset deep learning reconstruction network (AMO-CEST) with a multiple radial k-space sampling strategy for CEST-MRI. The AMO-CEST also contains dilated convolution to enlarge the receptive field and data consistency module to preserve the sampled k-space data. We evaluated the proposed method on a mouse brain dataset containing 5760 CEST images acquired at a pre-clinical 3 T MRI scanner. Quantitative results demonstrated that AMO-CEST showed obvious improvement over zero-filling method with a PSNR enhancement of 11 dB, a SSIM enhancement of 0.15, and a NMSE decrease of [Formula: see text] in three acquisition orientations. Compared with other deep learning-based models, AMO-CEST showed visual and quantitative improvements in images from three different orientations. We also extracted molecular contrast maps, including the amide proton transfer (APT) and the relayed nuclear Overhauser enhancement (rNOE). The results demonstrated that the CEST contrast maps derived from the CEST images of AMO-CEST were comparable to those derived from the original high-resolution CEST images. The proposed AMO-CEST can efficiently reconstruct high-quality CEST images from under-sampled k-space data and thus has the potential to accelerate CEST-MRI acquisition.

Preclinical Application of CEST MRI to Detect Early and Regional Tumor Response to Local Brain Tumor Treatment.

Treating glioblastoma and monitoring treatment response non-invasively remain challenging. Here, we developed a robust approach using a drug-loaded liposomal hydrogel that is mechanically compatible with the brain, and, simultaneously, we successfully monitored early tumor response using Chemical Exchange Saturation Transfer (CEST) MRI. This CEST-detectable liposomal hydrogel was optimized based on a sustainable drug release and a soft hydrogel for the brain tumor, which is unfavorable for tumor cell proliferation. After injecting the hydrogel next to the tumor, three distinctive CEST contrasts enabled the monitoring of tumor response and drug release longitudinally at 3T. As a result, a continuous tumor volume decrease was observed in the treatment group along with a significant decrease in CEST contrasts relating to the tumor response at 3.5 ppm (Amide Proton Transfer; APT) and at -3.5 ppm (relayed Nuclear Overhauser Effect; rNOE) when compared to the control group (p < 0.05). Interestingly, the molecular change at 3.5 ppm on day 3 (p < 0.05) was found to be prior to the significant decrease in tumor volume on day 5. An APT signal also showed a strong correlation with the number of proliferating cells in the tumors. This demonstrated that APT detected a distinctive decrease in mobile proteins and peptides in tumors before the change in tumor morphology. Moreover, the APT signal showed a regional response to the treatment, associated with proliferating and apoptotic cells, which allowed an in-depth evaluation and prediction of the tumor treatment response. This newly developed liposomal hydrogel allows image-guided brain tumor treatment to address clinical needs using CEST MRI.