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.

Evaluation of the molecular origin of amide proton transfer-weighted imaging.

PURPOSE: To evaluate the assumption in amide proton transfer weighted (APTw) imaging that the APT dominates over the relayed nuclear Overhauser enhancement (rNOE) and other CEST effects such as those from amines/guanidines, thereby providing imaging of mobile proteins/peptides. METHODS: We introduced two auxiliary asymmetric analysis metrics that can vary the relative contributions from amine/guanidinium CEST and other effects. By comparing these metrics with the conventional asymmetric analysis metric on healthy rat brains, we can approximately assess the contribution from amines/guanidines to APTw and determine whether the APT dominates over the rNOE effect. To further investigate the molecular origin of APTw, we used samples of dialyzed tissue homogenates to eliminate small metabolites and supernatants of homogenates to separate lipids from other components. RESULTS: When the APTw signal is positive using high saturation amplitudes (e.g., 2-3 µT), the contributions from amines/guanidines are significant and cannot be ignored. The APTw signal from the dialyzed homogenates and the controls has negligible changes, indicating that it primarily originates from macromolecules rather than small metabolites. Additionally, the APTw signals with low saturation amplitudes (e.g., 1 µT) were negative in tissue homogenates but positive in their supernatants, suggesting that proteins contribute positively to APTw signals, whereas lipids contribute negatively to it. CONCLUSION: The positive APTw signal using high saturation amplitudes could have significant contributions from soluble proteins through CEST, including amide/amine/guanidine proton transfer effects. In contrast, the negative APTw signal using low saturation amplitudes has significant contribution from lipids through rNOE.

Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction.

PURPOSE: To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. METHODS: For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. RESULTS: In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. CONCLUSION: The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.

Multiparametric exchange protons using Z-spectrum analysis proton (ZAP) and CEST on phantoms and human abdomen.

PURPOSE: This study aims to investigate a multiparametric exchange proton approach using CEST and Z-spectrum analysis protons (ZAP) in human abdominal organs, focusing on tissue differentiation for a potential early biomarker of abnormality. Prior to human studies, CEST and ZAP effects were studied in phantoms containing exchange protons. METHODS: Phantoms composed of iopamidol and iohexol solutions with varying pH levels, along with 12 human subjects, were scanned on a clinical 3T MR scanner. Subsequent ZAP analyses employed a two-Lorentzian pool model to provide free and restricted apparent T 2 f , r ex $$ {\mathrm{T}}_{2\ \mathrm{f},\mathrm{r}}^{\mathrm{ex}} $$ , and their fractions for data acquired across a wide range of offset frequencies (±100 kHz or ± 800 ppm), while a narrower range (±7 ppm or ± 900 Hz) was used for CEST analysis to estimate magnetization transfer ratio asymmetry (MTRAsym) for exchange protons like hydroxyl (-OH), amine (-NH2), and amide (-NH), resonating ˜1, 2, and 3.5 ppm, respectively. Differences in ZAP metrics across various organs were statistically analyzed using one-way analysis of variance (ANOVA). RESULTS: The phantom study differentiated contrast agents based on resonance peaks detected from CEST analysis, while ZAP metrics showed sensitivity to pH variations. In human, ZAP metrics revealed significant differences in abdominal organs, with a subgroup study indicating changes in ZAP metrics due to the presence of gallstones. CONCLUSION: CEST and ZAP techniques demonstrated promise in specific CEST protons and wide range ZAP protons and identifying tissue-specific characteristics. The preliminary findings underscore the necessity for more extensive study involving a broader subject pool to potentially establish biomarkers for diseased states.

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.

Quasi-steady-state (QUASS) reconstruction enhances T1 normalization in apparent exchange-dependent relaxation (AREX) analysis: A reevaluation of T1 correction in quantitative CEST MRI of rodent brain tumor models.

PURPOSE: The apparent exchange-dependent relaxation (AREX) analysis has been proposed as an effective means to correct T1 contribution in CEST quantification. However, it has been recognized that AREX T1 correction is not straightforward if CEST scans are not performed under the equilibrium condition. Our study aimed to test if quasi-steady-state (QUASS) reconstruction could boost the accuracy of the AREX metric under common non-equilibrium scan conditions. THEORY AND METHODS: Numerical simulation and in vivo scans were performed to assess the AREX metric accuracy. The CEST signal was simulated under different relaxation delays, RF saturation amplitudes, and durations. The AREX was evaluated as a function of the bulk water T1 and labile proton concentration using the multiple linear regression model. AREX MRI was also assessed in brain tumor rodent models, with both apparent CEST scans and QUASS reconstruction. RESULTS: Simulation showed that the AREX calculation from apparent CEST scans, under non-equilibrium conditions, had significant dependence on labile proton fraction ratio, RF saturation time, and T1. In comparison, QUASS-boosted AREX depended on the labile proton fraction ratio without significant dependence on T1 and RF saturation time. Whereas the apparent (2.7 ± 0.8%) and QUASS MTR asymmetry (2.8 ± 0.8%) contrast between normal and tumor regions of interest (ROIs) were significant, the difference was small. In comparison, AREX contrast between normal and tumor ROIs calculated from the apparent CEST scan and QUASS reconstruction was 3.8 ± 1.1%/s and 4.4 ± 1.2%/s, respectively, statistically different from each other. CONCLUSIONS: AREX analysis benefits from the QUASS-reconstructed equilibrium CEST effect for improved T1 correction and quantitative CEST analysis.

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.

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.

Whole-cerebrum guanidino and amide CEST mapping at 3 T by a 3D stack-of-spirals gradient echo acquisition.

PURPOSE: To develop a 3D, high-sensitivity CEST mapping technique based on the 3D stack-of-spirals (SOS) gradient echo readout, the proposed approach was compared with conventional acquisition techniques and evaluated for its efficacy in concurrently mapping of guanidino (Guan) and amide CEST in human brain at 3 T, leveraging the polynomial Lorentzian line-shape fitting (PLOF) method. METHODS: Saturation time and recovery delay were optimized to achieve maximum CEST time efficiency. The 3DSOS method was compared with segmented 3D EPI (3DEPI), turbo spin echo, and gradient- and spin-echo techniques. Image quality, temporal SNR (tSNR), and test-retest reliability were assessed. Maps of Guan and amide CEST derived from 3DSOS were demonstrated on a low-grade glioma patient. RESULTS: The optimized recovery delay/saturation time was determined to be 1.4/2 s for Guan and amide CEST. In addition to nearly doubling the slice number, the gradient echo techniques also outperformed spin echo sequences in tSNR: 3DEPI (193.8 ± 6.6), 3DSOS (173.9 ± 5.6), and GRASE (141.0 ± 2.7). 3DSOS, compared with 3DEPI, demonstrated comparable GuanCEST signal in gray matter (GM) (3DSOS: [2.14%-2.59%] vs. 3DEPI: [2.15%-2.61%]), and white matter (WM) (3DSOS: [1.49%-2.11%] vs. 3DEPI: [1.64%-2.09%]). 3DSOS also achieves significantly higher amideCEST in both GM (3DSOS: [2.29%-3.00%] vs. 3DEPI: [2.06%-2.92%]) and WM (3DSOS: [2.23%-2.66%] vs. 3DEPI: [1.95%-2.57%]). 3DSOS outperforms 3DEPI in terms of scan-rescan reliability (correlation coefficient: 3DSOS: 0.58-0.96 vs. 3DEPI: -0.02 to 0.75) and robustness to motion as well. CONCLUSION: The 3DSOS CEST technique shows promise for whole-cerebrum CEST imaging, offering uniform contrast and robustness against motion artifacts.

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.

Spatiotemporal Dynamics of Neuroinflammation Relate to Behavioral Recovery in Rats with Spinal Cord Injury.

PURPOSE: The degree and dynamic progression of neuroinflammation after traumatic spinal cord injuries (SCI) are crucial determinants of the severity of injury and potential for recovery. We used Positron Emission Tomography (PET) to monitor neuroinflammation longitudinally, correlating it with Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI) and behavior in contusion-injured rats. These studies help validate CEST metrics and confirm how imaging may be used to evaluate the efficacy of therapies and understand their mechanisms of action. PROCEDURES: 12 SCI and 4 sham surgery rats were subjected to CEST MRI and PET-Translocator Protein (TSPO) scans for 8 weeks following injury. Z-spectra from the SCI were analyzed using a 5-Lorentzian pool model for fitting. Weekly motor and somatosensory behavior were correlated with imaging metrics, which were validated through post-mortem histological and immuo-staining using ionized calcium-binding adaptor protein-1 (iba-1, microglia) and glial fibrillary acidic protein (GFAP, astrocytes). RESULTS: PET-TSPO showed widespread inflammation and post-mortem histology confirmed the presence of activated microglia. Changes in CEST and nuclear Overhauser Effect (NOE) peaks at 3.5 ppm and -1.6 ppm respectively were largest within the first week after injury and more pronounced in rostral versus caudal segments. These temporal indices of neuroinflammation corresponded to the recovery of locomotor behaviors and somatic sensation in rats with moderate contusion injury. The results confirm that CEST MRI metrics are sensitive indices of states of neuroinflammation within injured spinal cords. CONCLUSIONS: The detection of dynamic spatiotemporal features of neuroinflammation progression underscores the importance of considering their timings and locations for neuroprotective and anti-inflammatory therapies. The availability of noninvasive MRI indices of neuroinflammation may facilitate clinical trials aimed at treatments that promote recovery after SCI.

Ultrahigh Bifunctional Photocatalytic CO2 Reduction and H2 Evolution by Synergistic Interaction of Heteroatomic Pt-Ru Dimerization Sites.

Diatomic-site catalysts (DASCs) inherit the excellent performance of single-atom catalysts (SACs) by utilizing two adjacent atomic metal species to achieve functional complementarity and synergistic effects that improve the carbon dioxide reduction reaction (CO2RR) and H2 evolution reaction (HER) kinetics. Herein, we report a method to further improve the catalytic efficiency of Pt by using Pt and Ru single atoms randomly anchored on a g-C3N4 surface, yielding partial Pt-Ru dimers. The synthesized catalyst exhibits extraordinary photocatalytic activity and stability in both the CO2RR and HER processes. In-depth experimentation, the pH-dependent chemical exchange saturation transfer (CEST) imaging nuclear magnetic resonance (NMR) method, and theoretical analyses reveal that the excellent performance is attributed to orbital coupling between the Pt atoms and the neighboring Ru atoms (mainly dxy and dxz), which decreases the orbital energy levels and weakens the bond strength with intermediates, resulting in improved CO2RR and HER performance. This study successfully applies the pH-dependent CEST imaging NMR method to catalytic reactions, and CO2 adsorption is directly observed using CEST 2D imaging maps. This work presents significant potential for a variety of catalytic reaction applications by systematically designing bimetallic dimers with higher activity and stability.

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.

Assessment of rhabdomyolysis-induced acute kidney injury with chemical exchange saturation transfer magnetic resonance imaging.

Background: Rhabdomyolysis (RM)-induced acute kidney injury (AKI) is a common renal disease with low survival rate and inadequate prognosis. In this study, we investigate the feasibility of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) for assessing the progression of RM-induced AKI in a mouse model. Methods: AKI was induced in C57BL/6J mice via intramuscular injection of 7.5 mL/kg glycerol (n=30). Subsequently, serum creatinine (SCr), blood urea nitrogen (BUN), and hematoxylin-eosin (HE) and Masson staining, were performed. Longitudinal CEST-MRI was conducted on days 1, 3, 7, 15, and 30 after AKI induction using a 7.0-T MRI system. CEST-MRI quantification parameters including magnetization transfer ratio (MTR), MTR asymmetric analysis (MTRasym), apparent amide proton transfer (APT*), and apparent relayed nuclear Overhauser effect (rNOE*) were used to investigate the feasibility of detecting RM-induced renal damage. Results: Significant increases of SCr and BUN demonstrated established AKI. The HE staining revealed various degrees of tubular damage, and Masson staining indicted an increase in the degree of fibrosis in the injured kidneys. Among CEST parameters, the cortical MTR presented a significant difference, and it also showed the best diagnostic performance for AKI [area under the receiver operating characteristic curve (AUC) =0.915] and moderate negative correlations with SCr and BUN. On the first day of renal damage, MTR was significantly reduced in cortex (22.7%±0.04%, P=0.013), outer stripe of outer medulla (24.7%±1.6%, P<0.001), and inner stripe of outer medulla (27.0%±1.5%, P<0.001) compared to the control group. Longitudinally, MTR increased steadily with AKI progression. Conclusions: The MTR obtained from CEST-MRI is sensitive to the pathological changes in RM-induced AKI, indicating its potential clinical utility for the assessment of kidney diseases.

Investigation of relayed nuclear Overhauser enhancement effect at -1.6 ppm in an ischemic stroke model.

Background: When an ischemic stroke happens, it triggers a complex signalling cascade that may eventually lead to neuronal cell death if no reperfusion. Recently, the relayed nuclear Overhauser enhancement effect at -1.6 ppm [NOE(-1.6 ppm)] has been postulated may allow for a more in-depth analysis of the ischemic injury. This study assessed the potential utility of NOE(-1.6 ppm) in an ischemic stroke model. Methods: Diffusion-weighted imaging, perfusion-weighted imaging, and chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) data were acquired from five rats that underwent scans at 9.4 T after middle cerebral artery occlusion. Results: The apparent diffusion coefficient (ADC), cerebral blood flow (CBF), and apparent exchange-dependent relaxations (AREX) at 3.5 ppm and NOE(-1.6 ppm) were quantified. AREX(3.5 ppm) and NOE(-1.6 ppm) were found to be hypointense and exhibited different signal patterns within the ischemic tissue. The NOE(-1.6 ppm) deficit areas were equal to or larger than the ADC deficit areas, but smaller than the AREX(3.5 ppm) deficit areas. This suggested that NOE(-1.6 ppm) might further delineate the acidotic tissue estimated using AREX(3.5 ppm). Since NOE(-1.6 ppm) is closely related to membrane phospholipids, NOE(-1.6 ppm) potentially highlighted at-risk tissue affected by lipid peroxidation and membrane damage. Altogether, the ADC/NOE(-1.6 ppm)/AREX(3.5 ppm)/CBF mismatches revealed four zones of increasing sizes within the ischemic tissue, potentially reflecting different pathophysiological information. Conclusions: Using CEST coupled with ADC and CBF, the ischemic tissue may thus potentially be separated into four zones to better understand the pathophysiology after stroke and improve ischemic tissue fate definition. Further verification of the potential utility of NOE(-1.6 ppm) may therefore lead to a more precise diagnosis.