Chemical Exchange Saturation Transfer Effect Research Articles

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction.

Chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) often takes prolonged saturation duration (Ts) and relaxation delay (Td) to reach the steady state, and yet the insufficiently long Ts and Td in actual experiments may underestimate the CEST measurement. In this study, we aimed to develop a deep learning-based model for quasi-steady-state (QUASS) prediction from non-steady-state CEST acquired in experiments, therefore overcoming the limitation of the CEST effect which needs prolonged saturation time to reach a steady state. To support network training, a multi-pool Bloch-McConnell equation was designed to derive wide-ranging simulated Z-spectra, so as to solve the problem of time and labor consumption in manual annotation work. Following this, we formulated a hybrid architecture of long short-term memory (LSTM)-Attention to improve the predictive ability. The multilayer perceptron, recurrent neural network, LSTM, gated recurrent unit, BiLSTM, and LSTM-Attention were included in comparative experiments of QUASS CEST prediction, and the best performance was obtained by the proposed LSTM-Attention model. In terms of the linear regression analysis, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE), the results of LSTM-Attention demonstrate that the coefficient of determination in the linear regression analysis was at least R2 = 0.9748 for six different representative frequency offsets, the mean values of prediction accuracies in terms of SSIM, PSNR and MSE were 0.9991, 49.6714, and 1.68 × 10-4 for all frequency offsets. It was concluded that the LSTM-Attention model enabled high-quality QUASS CEST prediction.

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

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. 12 SCI and 4 sham surgery rats were subjected to CEST MRI and PET-Translocator Protein (TSPO) scans for 8weeks 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). 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 3.5ppm and -1.6ppm 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. 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.

Cucurbit[6]uril Hyperpolarized Chemical Exchange Saturation Transfer Pulse Sequence Parameter Optimization and Detectability Limit Assessment at 3.0T

AbstractThe front cover artwork is provided by Prof. Mitchell S. Albert's group at Lakehead University. The image shows the hyperpolarized chemical exchange saturation transfer (HyperCEST) effect in cucurbit[6]uril molecular biosensors within a blood vessel. Read the full text of the Research Article at 10.1002/cphc.202300346.

Multi-parametric MRI assessment of melatonin regulating the polarization of microglia in rats after cerebral ischemia/reperfusion injury

ObjectivesMulti-parametric magnetic resonance imaging (MRI) can provide comprehensive and valuable information for precise diagnosis and treatment evaluation of a number of diseases. In this study, the neuroprotective effects of melatonin (Mel) on a rat model of cerebral ischemia/reperfusion injury (CIRI) were assessed by multi-parametric MRI combined with histopathological techniques for longitudinal monitoring of the lesion microenvironment. MethodsSixty Sprague Dawley (SD) rats were randomly divided into three groups: the Sham, CIRI and CIRI + Mel groups. At multiple time points after ischemia, MRI scanning was performed on a 7.0 Tesla MRI scanner. Multi-parametric MRI includes T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and chemical exchange saturation transfer (CEST)-MRI. CEST effects were calculated by the Lorentzian difference method, 3.5 ppm indicates amide protons of mobile proteins/peptide (Amide-CEST) and 2.0 ppm indicates amine protons (Guan-CEST). Multiple histopathological techniques were used to examine the histopathological changes and explore the therapeutic effects of Mel. ResultsT2WI and DWI-MRI could localize the infarct foci and areas in CIRI rats, which was further validated by staining, 2, 3, 5-triphenyl tetrazolium chloride (TTC) staining, hematoxylin and eosin (H&E) staining, and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-biotin nick end labelling (TUNEL) staining. After Mel treatment, T2WI and DWI-MRI showed smaller infarct volume, and neurons displayed improved morphology with less apoptosis rates. Notably, Amide-CEST and Guan-CEST signal decreased as early as 2 h after CIRI (all P <0.001), reflecting the change of pH after ischemia. After Mel treatment, both Amide-CEST and Guan-CEST signal increased in ischemic cortex and striatum compared with control group (all P < 0.001). The immunofluorescence staining and western blotting analysis suggested the expression of M2 microglia increased after Mel treatment; While，after Mel treatment the inflammatory factor interleukin-1β (IL-1β) decreased compared with control CIRI rats. ConclusionsMulti-parametric MRI was shown to be an effective method to monitor the brain damage in a rat model of CIRI and assess the therapeutic effects of Mel treatment. Amide-CEST and Guan-CEST were especially sensitive to the changes in brain microenvironment during the early stage after CIRI. Furthermore, the neuroprotective effect of Mel treatment is associated with its promotion of the microglia polarized to M2 type in CIRI rats.

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.

Fast and robust pulsed chemical exchange saturation transfer (CEST) MRI using a quasi-steady-state (QUASS) algorithm at 3 T

Chemical exchange saturation transfer (CEST) has emerged as a powerful technique to image dilute labile protons. However, its measurement depends on the RF saturation duration (Tsat) and relaxation delay (Trec). Although the recently developed quasi-steady-state (QUASS) solution can reconstruct equilibrium CEST effects under continuous-wave RF saturation, it does not apply to pulsed-CEST MRI on clinical scanners with restricted hardware or specific absorption rate limits. This study proposed a QUASS algorithm for pulsed-CEST MRI and evaluated its performance in muscle CEST measurement. An approximated expression of a steady-state pulsed-CEST signal was incorporated in the off-resonance spin-lock model, from which the QUASS pulsed-CEST effect was derived. Numerical simulation, creatine phantom, and healthy volunteer scans were conducted at 3 T. The CEST effect was quantified with asymmetry analysis in the simulation and phantom experiments. CEST effects of creatine, amide proton transfer, phosphocreatine, and combined magnetization transfer and nuclear Overhauser effects were isolated from a multi-pool Lorentzian model in muscles. Apparent and QUASS CEST measurements were compared under different Tsat/Trec and duty cycles. Paired Student's t-test was employed with P < 0.05 as statistically significant. The simulation, phantom, and human studies showed the strong impact of Tsat/Trec on apparent CEST measurements, which were significantly smaller than the corresponding QUASS CEST measures, especially under short Tsat/Trec times. In comparison, the QUASS algorithm mitigates such impact and enables accurate CEST measurements under short Tsat/Trec times. In conclusion, the QUASS algorithm can accelerate robust pulsed-CEST MRI, promising the efficient detection and evaluation of muscle diseases in clinical settings.

Reproducibility of APT-weighted CEST-MRI at 3T in healthy brain and tumor across sessions and scanners

Amide proton transfer (APT)-weighted chemical exchange saturation transfer (CEST) imaging is a recent MRI technique making its way into clinical application. In this work, we investigated whether APT-weighted CEST imaging can provide reproducible measurements across scan sessions and scanners. Within-session, between-session and between scanner reproducibility was calculated for 19 healthy volunteers and 7 patients with a brain tumor on two 3T MRI scanners. The APT-weighted CEST effect was evaluated by calculating the Lorentzian Difference (LD), magnetization transfer ratio asymmetry (MTRasym), and relaxation-compensated inverse magnetization transfer ratio (MTRREX) averaged in whole brain white matter (WM), enhancing tumor and necrosis. Within subject coefficient of variation (COV) calculations, Bland–Altman plots and mixed effect modeling were performed to assess the repeatability and reproducibility of averaged values. The group median COVs of LD APT were 0.56% (N = 19), 0.84% (N = 6), 0.80% (N = 9) in WM within-session, between-session and between-scanner respectively. The between-session COV of LD APT in enhancing tumor (N = 6) and necrotic core (N = 3) were 4.57% and 5.67%, respectively. There were no significant differences in within session, between session and between scanner comparisons of the APT effect. The COVs of LD and MTRREX were consistently lower than MTRasym in all experiments, both in healthy tissues and tumor. The repeatability and reproducibility of APT-weighted CEST was clinically acceptable across scan sessions and scanners. Although MTRasym is simple to acquire and compute and sufficient to provide robust measurement, it is beneficial to include LD and MTRREX to obtain higher reproducibility for detecting minor signal difference in different tissue types.

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.

Numerical simulation-based assessment of pH-sensitive chemical exchange saturation transfer MRI quantification accuracy across field strengths.

Chemical exchange saturation transfer (CEST) MRI detects dilute labile protons via their exchange with bulk water, conferring pH sensitivity. Based on published exchange and relaxation properties, a 19-pool simulation was used to model the brain pH-dependent CEST effect and assess the accuracy of quantitative CEST (qCEST) analysis across magnetic field strengths under typical scan conditions. First, the optimal B1 amplitude was determined by maximizing pH-sensitive amide proton transfer (APT) contrast under the equilibrium condition. Apparent and quasi-steady-state (QUASS) CEST effects were then derived under the optimal B1 amplitude as functions of pH, RF saturation duration, relaxation delay, Ernst flip angle, and field strength. Finally, CEST effects, particularly the APT signal, were isolated with spinlock model-based Z-spectral fitting to evaluate the accuracy and consistency of CEST quantification. Our data showed that QUASS reconstruction significantly improved the consistency between simulated and equilibrium Z-spectra. The residual difference between QUASS and equilibrium CEST Z-spectra was, on average, 30 times less than that of the apparent CEST Z-spectra across field strengths, saturation, and repetition times. Also, the spinlock fitting of the QUASS CEST effect significantly reduced the residual errors 9-fold. Furthermore, the isolated APT amplitude from QUASS reconstruction was consistent and higher than the apparent CEST analysis under nonequilibrium conditions. To summarize, this study confirmed that QUASS reconstruction facilitates accurate determination of the CEST system under different scan protocols across field strengths, with the potential to help standardize CEST quantification.

Concurrent Dual-Contrast Enhancement Using Fe3O4 Nanoparticles to Achieve a CEST Signal Controllability.

Traditional T2 magnetic resonance imaging (MRI) contrast agents have defects inherent to negative contrast agents, while chemical exchange saturation transfer (CEST) contrast agents can quantify substances at trace concentrations. After reaching a certain concentration, iron-based contrast agents can "shut down" CEST signals. The application range of T2 contrast agents can be widened through a combination of CEST and T2 contrast agents, which has promising application prospects. The purpose of this study is to develop a T2 MRI negative contrast agent with a controllable size and to explore the feasibility of dual contrast enhancement by combining T2 with CEST contrast agents. The study was carried out in vitro with HCT-116 human colon cancer cells. A GE SIGNA Pioneer 3.0 T medical MRI scanner was used to acquire CEST images with different saturation radio-frequency powers (1.25/2.5/3.75/5 μT) by 2D spin echo-echo planar imaging (SE-EPI). Magnetic resonance image compilation (MAGiC) was acquired by a multidynamic multiecho 2D fast spin-echo sequence. The feasibility of this dual-contrast enhancement method was assessed by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, dynamic light scattering, ζ potential analysis, inductively coupled plasma, X-ray photoelectron spectroscopy, X-ray powder diffraction, vibrating-sample magnetometry, MRI, and a Cell Counting Kit-8 assay. The association between the transverse relaxation rate r2 and the pH of the iron-based contrast agents was analyzed by linear fitting, and the linear relationship between the CEST effect in different B1 fields and pH was analyzed by the ratio method. Fe3O4 nanoparticles (NPs) with a mean particle size of 82.6 ± 22.4 nm were prepared by a classical process, and their surface was successfully modified with -OH active functional groups. They exhibited self-aggregation in an acidic environment. The CEST effect was enhanced as the B1 field increased, and an in vitro pH map was successfully plotted using the ratio method. Fe3O4 NPs could stably serve as reference agents at different pH values. At a concentration of 30 μg/mL, Fe3O4 NPs "shut down" the CEST signals, but when the concentration of Fe3O4 NPs was less than 10 μg/mL, the two contrast agents coexisted. The prepared Fe3O4 NPs had almost no toxicity, and when their concentration rose to 200 μg/mL at pH 6.5 or 7.4, they did not reach the half-maximum inhibitory concentration (IC50). Fe3O4 magnetic NPs with a controllable size and no toxicity were successfully synthesized. By combining Fe3O4 NPs with a CEST contrast agent, the two contrast agents could be imaged simultaneously; at higher concentrations, the iron-based contrast agent "shut down" the CEST signal. An in vitro pH map was successfully plotted by the ratio method. CEST signal inhibition can be used to realize the pH mapping of solid tumors and the identification of tumor active components, thus providing a new imaging method for tumor efficacy evaluation.

A machine learning approach that measures pH using acidoCEST MRI of iopamidol.

Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, five T1 values, and eight pH values at five temperatures, acquired at six saturation powers and six saturation times. We also acquired T1 , T2 , B1 RF power, and B0 magnetic field strength supplementary MR information. These MR images were used to train and validate machine learning models for the tasks of pH classification and pH regression. Specifically, we tested the L1-penalized logistic regression classification (LRC) model and the random forest classification (RFC) model for classifying the CEST Z-spectra for thresholds at pH 6.5 and 7.0. Our results showed that both RFC and LRC were effective for pH classification, although the RFC model achieved higher predictive value, and improved the accuracy of classification accuracy with CEST Z-spectra with a more limited set of saturation frequencies. Furthermore, we used LASSO and random forest regression (RFR) models to explore pH regression, which showed that the RFR model achieved higher accuracy and precision for estimating pH across the entire pH range of 6.2-7.3, especially when using a more limited set of features. Based on these results, machine learning for analysis of acidoCEST MRI is promising for eventual in vivo determination of tumor pHe.

Feasibility of clinical studies of chemical exchange saturation transfer magnetic resonance imaging of prostate cancer at 7 T.

Chemical exchange saturation transfer (CEST) has been explored for differentiation between tumour and benign tissue in prostate cancer (PCa) patients. With ultrahigh field strengths such as 7-T, the increase of spectral resolution and sensitivity could allow for selective detection of amide proton transfer (APT) at 3.5 ppm and a group of compounds that resonate at 2 ppm (i.e., [poly]amines and/or creatine). The potential of 7-T multipool CEST analysis of the prostate and the detection of PCa was studied in patients with proven localised PCa who were scheduled to undergo robot-assisted radical prostatectomy (RARP). Twelve patients were prospectively included (mean age 68.0 years, mean serum prostate-specific antigen 7.8ng/mL). A total of 24 lesions larger than 2 mm were analysed. Used were 7-T T2-weighted (T2W) imaging and 48 spectral CEST points. Patients received 1.5-T/3-T prostate magnetic resonance imaging and galium-68-prostate-specific membrane antigen-positron emission tomography/computerised tomography to determine the location of the single-slice CEST. Based on the histopathological results after RARP, three regions of interest were drawn on the T2W images from a known malignant zone and benign zone in the central and peripheral zones. These areas were transposed to the CEST data, from which the APT and 2-ppm CEST were calculated. The statistical significance of the CEST between the central zone, the peripheral zone, and tumour was calculated using a Kruskal-Wallis test. The z-spectra showed that APT and even a distinct pool that resonated at 2 ppm were detectable. This study showed a difference trend in the APT levels, but no difference in the 2-ppm levels when tested between the central zone, the peripheral zone, and tumour (H(2) = 4.8, p= 0.093 and H(2) = 0.86, p= 0.651, respectively). Thus, to conclude, we could most likely detect APT and amines and/or creatine levels noninvasively in prostate using the CEST effect. At group level, CEST showed a higher level of APT in the peripheral versus the central zone; however, no differences of APT and 2-ppm levels were observed in tumours.

Deep learning to reconstruct quasi-steady-state chemical exchange saturation transfer from a non-steady-state experiment.

The insufficiently long RF saturation duration and relaxation delay in chemical exchange saturation transfer (CEST)-MRI experiments may result in underestimation of CEST measurements. To maintain the CEST effect without prolonging the saturation duration and reach quasi-steady state (QUASS), a deep learning method was developed to reconstruct a QUASS CEST image pixel by pixel from non-steady-state CEST acquired in experiments. In this work, we established a tumor-bearing rat model on a 7 T horizontal bore small-animal MRI scanner, allowing ground-truth generation, after which a bidirectional long short-term memory network was formulated and trained on simulated CEST Z-spectra to reconstruct the QUASS CEST; finally, the ground truth yielded by experiments was used to evaluate the performance of the reconstruction model by comparing the estimates with the ground truth. For quantitation evaluation, linear regression analysis, structural similarity index (SSIM) and peak signal-to-noise ratio (peak SNR) were used to assess the proposed model in the QUASS CEST reconstruction. In the linear regression analysis of in vivo data, the coefficient of determination for six different representative frequency offsets was at least R2 = 0.9521. Using the SSIM and peak SNR as evaluation metrics, the reconstruction accuracies of in vivo QUASS CEST were found to be 0.9991 and 46.7076, respectively. Experimental results demonstrate that the proposed model provides a robust and accurate solution for QUASS CEST reconstruction using a deep learning mechanism.

Angiopep-2, an MRI Biomarker, Dynamically Monitors Amyloid Deposition in Early Alzheimer's Disease.

The reliable and dynamic detection of amyloid β-protein (Aβ) deposition using imaging technology is necessary for preclinical Alzheimer's disease (AD), which may significantly improve prognosis. The present study aimed to evaluate the feasibility of applying angiopep-2 (ANG), a chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) biomarker, for monitoring Aβ deposition in vivo. ANG exerted a good chemical exchange saturation transfer (CEST) effect and displayed a moderate binding affinity to Aβ1-42 in vitro. Six-month-old mice with AD injected with ANG exhibited a significantly enhanced CEST effect than controls in vivo; this effect gradually became more apparent at 8, 10, and 12 months. Spatial learning impairment caused by abundant Aβ deposition (representing mild cognitive impairment in AD patients) develops at 12 months in APPswe/PSEN1dE9 (line 85) AD mice. To conclude, the CEST of ANG could display very earlier age-related Aβ pathological progress in mice with AD, consistent with immunohistochemistry. ANG has extraordinary potential for clinical transformation as an imaging biomarker to diagnose early AD and track its progress dynamically and nonradiationally.

31P ParaCEST: 31P MRI-CEST Imaging Based on the Formation of a Ternary Adduct between Inorganic Phosphate and Eu-DO3A.

Development of the field of magnetic resonance imaging (MRI) chemical exchange saturation transfer (CEST) contrast agents is hampered by the limited sensitivity of the technique. In water, the high proton concentration allows for an enormous amplification of the exchanging proton pool. However, the 1H CEST in water implies that the number of nuclear spins of the CEST-generating species has to be in the millimolar range. The use of nuclei other than a proton allows exploitation of signals different from that of water, thus lowering the concentration of the exchanging pool as the source of the CEST effect. In this work, we report on the detection of a 31P signal from endogenous inorganic phosphate (Pifree) as the source of CEST contrast by promoting its exchange with the Pi bound to the exogenous complex 1,4,7,10-tetraazacyclododecane-1,4,7-triacetic acid (Pibound). The herein-reported results demonstrate that this approach can improve the detectability threshold by 3 orders of magnitude with respect to the conventional 1H CEST detection (considered per single proton). This achievement reflects the decrease of the bulk concentration of the detected signal from 111.2 M (water) to 10 mM (Pi). This method paves the way to a number of biological studies and clinically translatable applications, herein addressed with a proof-of-concept in the field of cellular imaging.

Demonstration of pH imaging in acute stroke with endogenous ratiometric chemical exchange saturation transfer magnetic resonance imaging at 2ppm.

pH change is often considered a hallmark of metabolic disruption in diseases such as ischemic stroke and cancer. Chemical exchange saturation transfer (CEST) MRI, particularly amide proton transfer (APT), has emerged as a noninvasive pH imaging approach. However, there are changes in multipool CEST effects besides APT MRI. Our study investigated radiofrequency (RF) amplitude-based ratiometric CEST pH imaging in acute stroke. Briefly, adult male Wistar rats underwent CEST MRI under two RF saturation (B1 ) levels of 0.75 and 1.5 μT following middle cerebral artery occlusion. Magnetization transfer (MT), direct water saturation, CEST at 2ppm (CEST@2ppm), amine (2.75 ppm), and APT (3.5ppm) effects were resolved with the multipool Lorentzian fitting approach. The ratiometric analysis was measured in the ischemic lesion and the contralateral normal area, which was also correlated with pH-specific MT and the relaxation normalized APT (MRAPT) index. MT, amine CEST effect, and their respective ratiometric indices did not show significant changes in ischemic regions (p> 0.05), as expected. Whereas APT decreased in the ischemic lesion for B1 of 1.5 μT (p< 0.01), the correlation between the amide ratio with MRAPT index was moderate (r = 0.52, p= 0.02). By comparison, the ischemic tissue showed a significantly increased CEST@2ppm for both saturation levels from the contralateral normal area (p≤0.01). Importantly, the CEST@2ppm ratio decreased in the ischemic lesion (p< 0.01), which highly correlated with the MRAPT index (r = 0.93, p< 0.001). To summarize, our study demonstrated the feasibility of endogenous CEST@2ppm ratiometric imaging of pH upon acute stroke, promising to detect pH changes in metabolic diseases.

Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging can provide surrogate biomarkers for disease diagnosis. However, endogenous CEST effects are always diluted and contaminated by competing effects, which results in unwanted signal contributions that lessen the specificity of CEST to underlying biochemical exchange processes. The aim of this study was to examine a method for the accurate quantification of CEST effects. A Markov chain Monte Carlo (MCMC)-based Bayesian inference approach was proposed to estimate the exchange parameters, and CEST effects could be fitted using these estimations. This approach was tested in Bloch simulation and ischemic stroke rat experiments, and its performance was evaluated using quantification maps and numerical metrics. With 12 groups of simulations, the MCMC method achieved satisfactory fittings on both 2-pool and 5-pool models. The sum of squares error values and the root mean square error of the fitted Z-spectra were smaller than 10-3, and the coefficient of determination (R-squared) values were close to 1. The corresponding CEST quantification spectra were also well fitted and successfully separated the mixed CEST effects. The estimated parameters showed little bias relative to the ground truth, with errors between the true and estimated values of each parameter of less than 0.5%. In the animal experiments, fitted using the MCMC method showed obvious contrast between ischemic and contralateral regions at the early stage. Compared with other quantification methods, it displayed the highest contrast-to-noise ratios (3.9, 2.73, and 3.93) and the lowest coefficient of variation values (0.181, 0.2224, and 0.2897) in all three stroke periods. The MCMC method provided an efficient approach for parameter estimation and CEST effect quantification. The method may therefore be useful in achieving an accurate pathological diagnosis.

Dual-Peak Lorentzian CEST MRI for antiretroviral drug brain distribution.

Spatial-temporal biodistribution of antiretroviral drugs (ARVs) can now be achieved using MRI by utilizing chemical exchange saturation transfer (CEST) contrasts. However, the presence of biomolecules in tissue limits the specificity of current CEST methods. To overcome this limitation, a Lorentzian line-shape fitting algorithm was developed that simultaneously fits CEST peaks of ARV protons on its Z-spectrum. This algorithm was tested on the common first line ARV, lamivudine (3TC), that has two peaks resulting from amino (-NH2) and hydroxyl (-OH) protons in 3TC. The developed dual-peak Lorentzian function fitted these two peaks simultaneously, and used the ratio of -NH2 and -OH CEST contrasts as a constraint parameter to measure 3TC presence in brains of drug-treated mice. 3TC biodistribution calculated using the new algorithm was compared against actual drug levels measured using UPLC-MS/MS. In comparison to the method that employs the -NH2 CEST peak only, the dual-peak Lorentzian fitting algorithm showed stronger correlation with brain tissue 3TC levels, signifying estimation of actual drug levels. We concluded that 3TC levels can be extracted from confounding CEST effects of tissue biomolecules resulting in improved specificity for drug mapping. This algorithm can be expanded to measure a variety of ARVs using CEST MRI.

In vivo pH mapping with omega plot-based quantitative chemical exchange saturation transfer MRI.

Chemical exchange saturation transfer (CEST) MRI is promising for detecting dilute metabolites and microenvironment properties, which has been increasingly adopted in imaging disorders such as acute stroke and cancer. However, in vivo CEST MRI quantification remains challenging because routine asymmetry analysis (MTRasym ) or Lorentzian decoupling measures a combined effect of the labile proton concentration and its exchange rate. Therefore, our study aimed to quantify amide proton concentration and exchange rate independently in a cardiac arrest-induced global ischemia rat model. The amide proton CEST (APT) effect was decoupled from tissue water, macromolecular magnetization transfer, nuclear Overhauser enhancement, guanidinium, and amine protons using the image downsampling expedited adaptive least-squares (IDEAL) fitting algorithm on Z-spectra obtained under multiple RF saturation power levels, before and after global ischemia. Omega plot analysis was applied to determine amide proton concentration and exchange rate simultaneously. Global ischemia induces a significant APT signal drop from intact tissue. Using the modified omega plot analysis, we found that the amide proton exchange rate decreased from 29.6 ± 5.6 to 12.1 ± 1.3s-1 (P < 0.001), whereas the amide proton concentration showed little change (0.241 ± 0.035% vs. 0.202 ± 0.034%, P=0.074) following global ischemia. Our study determined the labile proton concentration and exchange rate underlying the in vivo APT MRI. The significant change in the exchange rate, but not the concentration of amide proton demonstrated that the pH effect dominates the APT contrast during tissue ischemia.

Quasi-steady-state amide proton transfer (QUASS APT) MRI enhances pH-weighted imaging of acute stroke.

Chemical exchange saturation transfer (CEST) imaging measurement depends not only on the labile proton concentration and pH-dependent exchange rate but also on experimental conditions, including the relaxation delay and radiofrequency (RF) saturation time. Our study aimed to extend a quasi-steady-state (QUASS) solution to a modified multi-slice CEST MRI sequence and test if it provides enhanced pH imaging after acute stroke. Our study derived the QUASS solution for a modified multislice CEST MRI sequence with an unevenly segmented RF saturation between image readout and signal averaging. Numerical simulation was performed to test if the generalized QUASS solution corrects the impact of insufficiently long relaxation delay, primary and secondary saturation times, and multi-slice readout. In addition, multiparametric MRI scans were obtained after middle cerebral artery occlusion, including relaxation and CEST Z-spectrum, to evaluate the performance of QUASS CEST MRI in a rodent acute stroke model. We also performed Lorentzian fitting to isolate multi-pool CEST contributions. The QUASS analysis enhanced pH-weighted magnetization transfer asymmetry contrast over the routine apparent CEST measurements in both contralateral normal (-3.46% ± 0.62% (apparent) vs. -3.67% ± 0.66% (QUASS), P&lt; 0.05) and ischemic tissue (-5.53% ± 0.68% (apparent) vs. -5.94% ± 0.73% (QUASS), P&lt; 0.05). Lorentzian fitting also showed significant differences between routine and QUASS analysis of ischemia-induced changes in magnetization transfer, amide, amine, guanidyl CEST, and nuclear Overhauser enhancement (-1.6parts per million) effects. Our study demonstrated that generalized QUASS analysis enhanced pH MRI contrast and improved quantification of the underlying CEST contrast mechanism, promising for further in vivo applications.