Chemical Exchange Saturation Transfer Imaging Research Articles

Amide Proton Transfer-Weighted MR Imaging and Signal Variations in a Rat Model of Lipopolysaccharide-Induced Sepsis-Associated Encephalopathy

Introduction: Sepsis-associated encephalopathy (SAE) is an acute brain dysfunction secondary to systemic infection, occurring without direct central nervous system involvement. Despite its clinical relevance, reliable biomarkers for diagnosing SAE and assessing its severity remain limited. This study aimed to evaluate the feasibility of amide proton transfer-weighted (APTw) chemical exchange saturation transfer (CEST) MRI as a non-invasive molecular imaging technique for detecting metabolic alterations related to neuroinflammation in SAE. Using a lipopolysaccharide (LPS)-induced rat model, we focused on hippocampal changes associated with neuronal inflammation. Materials and Methods: Twenty-one Sprague–Dawley rats (8 weeks old, male) were divided into three groups: control (CTRL, n = 7), LPS-induced sepsis at 5 mg/kg (LPS05, n = 7), and 10 mg/kg (LPS10, n = 7). Sepsis was induced via a single intraperitoneal injection of LPS. APTw imaging was performed using a 7 T preclinical MRI system, and signal quantification in the hippocampus was conducted using the magnetization transfer ratio asymmetry analysis. Results and Discussion: APTw imaging at 7 T demonstrated significantly elevated hippocampal APTw signals in SAE model rats (LPS05 and LPS10) compared to the control (CTRL) group: CTRL (−1.940 ± 0.207%) vs. LPS05 (−0.472 ± 0.485%) (p < 0.001) and CTRL vs. LPS10 (−0.491 ± 0.279%) (p < 0.001). However, no statistically significant difference was observed between the LPS05 and LPS10 groups (p = 0.994). These results suggest that APTw imaging can effectively detect neuroinflammation-related metabolic alterations in the hippocampus. Conclusion: Our findings support the feasibility of APTw CEST imaging as a non-invasive molecular MRI technique for SAE, with potential applications in diagnosis, disease monitoring, and therapeutic evaluation.

MC-RED: A deep learning network for motion correction in 3D CEST imaging.

Chemical exchange saturation transfer (CEST) imaging is highly sensitive to patient motion, which can compromise the reliability of quantitative molecular analysis. This study aims to develop and validate a deep learning-based motion correction method for enhancing image quality in 3D CEST imaging. We introduce MC-RED, a motion correction approach using a residual encoding-decoding network that incorporates frequency-specific information via a 2D Gaussian distribution with static reference images. This network generates motion-free reference frames for registering and correcting motion-corrupted CEST images. The method was evaluated on simulated data from healthy volunteers and clinical data from encephalitis patients, with performance measured by peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE) from Lorentzian difference (LD) analysis. MC-RED significantly enhances image quality by reducing motion artifacts, particularly in low-contrast regions near the water resonance. The corrected images show improved signal fidelity and spatial alignment, leading to more accurate quantitative maps. The deep learning-based MC-RED effectively corrects motion artifacts in 3D CEST imaging, thereby holding potentials for increasing the reliability of quantitative CEST analysis.

Investigating the mechanism of inositol against paclitaxel chemoresistance on triple-negative breast cancer by using 7T multiparametric MRI and mitochondrial changes

BackgroundThe emerging triple-negative breast cancer (TNBC) treatments target mitochondrial fission to combat paclitaxel (PTX) resistance. Inositol’s inhibition of this process makes it a potential therapy. Multiparametric MRI provides an early and effective assessment of these innovations.ObjectiveTo monitor the efficacy of Inositol on PTX-resistant TNBC mice using 7T multiparametric MRI, and to further explore the mechanism of inositol inhibiting PTX chemoresistance in combination with the morphological changes of isolated mitochondria.Materials and methodsBALB/c mice aged 6–8 weeks were subcutaneously inoculated with PTX-resistant 4T1 cells and divided into three groups: PTX-treated mice (n = 24), “PTX + Inositol”-treated mice (n = 24) and untreated mice (n = 24). Six mice in each group underwent diffused weighted imaging (DWI) and diffusion kurtosis imaging (DKI) every 7 days after administration. To observe the dynamic changes of inositol within the tumor tissue post-treatment, chemical exchange saturation transfer (CEST) imaging was performed. Six mice in each group were sacrificed on day 0, 7, and 14 respectively for histopathological examination. After a 3-week scanning cycle, the remaining mice in each group were euthanized for histopathological analysis. The therapeutic response of inositol was assessed via Hematoxylin & Eosin (H&E) staining and Ki-67 immunohistochemistry. The effects of inositol on mitochondrial structure and PTX resistance were studied by Western Blot and electron microscopy. One-way analysis of variance, independent samples t-test, paired samples t-test, Kruskal-Wallis, and Spearman rank correlation were used.ResultsThe CEST signal of inositol in tumor tissue was significantly higher after 1 h of inositol administration than before (2.75 ± 0.71% vs. 1.80 ± 0.33%, p < 0.05). On day 21 after treatment, the tumor volume in the PTX + Ins group was smaller than that in the PTX group (191.52 ± 27.98 mm3 vs. 388.98 ± 32.62 mm3, p < 0.001). The MD, MK, and ADC values were correlated significantly with tumor cell density (MD, r = -0.872; MK, r = 0.723; ADC, r = -0.858) and Ki-67 level (MD, r = -0.975; MK, r = 0.680; ADC, r = -0.860). The p-AMPK levels of PTX + Ins group were lower than that of PTX group (0.50 ± 0.06 vs. 0.60 ± 0.05, p = 0.04), and the mitochondrial length was longer than that of PTX group (0.86 ± 0.10 vs. 0.44 ± 0.09, p < 0.001), with a significant correlation to Ki-67 levels (r = -0.853, p < 0.001).ConclusionInositol may counteract PTX resistance in TNBC by disrupting mitochondrial fission, and DWI combined with DKI effectively tracked this effect.

Specific APT and NOE Imaging Using DSP-CEST in Humans at 3 T.

Specific chemical exchange saturation transfer (CEST) imaging, especially at low-field clinical 3-T scanners where multiple pools overlap, is challenging. Recently, a double saturation power (DSP)-CEST method has been developed to improve the specificity in quantifying two major CEST variations: amide proton transfer (APT) and nuclear Overhauser enhancement (NOE). This method avoids the mutual interference between APT and NOE, unlike the conventional asymmetry analysis method. It is also model-free, making it more robust compared to various Lorentzian fitting methods. Initially, this method was developed for continuous-wave saturation and tested in animals on preclinical MRI. This paper aims to further develop the method for pulsed-CEST saturation and apply it to human imaging at 3 T. Simulations and phantom experiments were first conducted to validate its specificity. Subsequently, invivo experiments were performed on six healthy human brains to demonstrate its applications. Two CEST quantification metrics, including the magnetization transfer ratio (MTR) and apparent exchange-dependent relaxation (AREX), were evaluated. The DSP-CEST successfully eliminated all confounding components and specifically quantified the APT and NOE effects. It demonstrated significantly lower MTR-quantified APT effects and higher AREX-quantified APT effects in white matter (WM) than in gray matter (GM). Moreover, it revealed no significant difference in the MTR-quantified NOE effect between WM and GM, but a significantly higher AREX-quantified NOE effect in WM than in GM. These results, differing from those obtained using the asymmetry analysis, confirmed their different origins. In contrast, they align with those obtained using a Lorentzian difference (LD) analysis, suggesting both methods have improved specificity. Yet, without the need to assume the fitting models and set fitting parameters, the DSP-CEST method proves to be more robust than the LD method.

The Application of Quasi-Steady-State Chemical Exchange Saturation Transfer Imaging in the Visualization of Glioma Infiltration and the Optimal Extent of Resection Establishment.

The quasi-steady-state (QUASS) algorithm improves chemical exchange saturation transfer (CEST) reliability, but its efficacy in detecting glioma infiltration is unclear. To assess apparent and QUASS CEST in visualizing glioma infiltration and predicting optimal extent-of-resection (EOR). Prospective. 72 adult-type diffuse glioma patients (37 males, 49.57 ± 14.76 years) and 24 healthy volunteers (12 males, 48.71 ± 14.23 years). 3 T, fast spin-echo CEST. Apparent and QUASS CEST effects (amide proton transfer [APT], combined magnetization transfer and nuclear overhauser enhancement and the 2-ppm chemical exchange saturation transfer peak) were calculated in solid tumor, edema, contralateral normal apparent white matter (CNAWM) in glioma patients, and white matter in healthy individuals (WMH). Comparisons were made between these four regions, high-/low-grade gliomas (HGGs/LGGs), and isocitrate dehydrogenase (IDH)-mutant/wild-type gliomas. Twenty-seven biopsy samples from glioblastomas and peritumoral regions were selected and traced back to the original images. Then, correlations between CEST effects, cellularity, and Ki-67 labeling index (LI) were assessed. An optimal cutoff value for the normalized ratios of QUASS APT (QUASS_rAPT) was generated. Linear mixed models, t-test, receiver-operating characteristic analysis, and Pearson's correlation tests were used. The statistical significance was set at p ≤ 0.05. QUASS_APT value decreased significantly from tumor solid area, edema, and CNAWM to WMH (3.887 ± 1.489, 2.556 ± 0.985, and 1.584 ± 0.462, respectively). QUASS_rAPT of solid tumor area differentiated LGGs from HGGs (1.878 ± 0.515 vs. 2.857 ± 1.026) and IDH-mutant gliomas from IDH wild-type gliomas (2.195 ± 0.769 vs. 2.875 ± 1.092). QUASS_rAPT strongly correlated with cell density and Ki-67 LI (r = 0.801 and 0.776). The optimal cutoff value of QUASS_rAPT was 1.300. Compared to other CEST effects and apparent methods, QUASS_rAPT enhances glioma stratification and better reflects cell density and proliferative potential. A QUASS_rAPT > 1.30 optimized EOR prediction in glioblastomas. 2 TECHNICAL EFFICACY: Stage 2.

When CEST meets diffusion: Multi‐echo diffusion‐encoded CEST (dCEST) MRI to measure intracellular and extracellular CEST signal distributions

PurposeTo develop a multi‐echo, diffusion‐encoded chemical exchange saturation transfer (dCEST) imaging technique for estimating the intracellular and extracellular/intravascular contributions to the conventional CEST signal.MethodsA dCEST pulse sequence was developed to quantify the signal fractions, transverse relaxation times (T2), and apparent diffusion coefficient (ADC) of the intracellular and extracellular/intravascular water compartments. dCEST images were acquired across a wide range of TE, b‐values, RF saturation strengths, and frequency offsets. The data were analyzed using a two‐compartment model with distinct diffusivities and T2 values. Intracellular and extracellular fractions of conventional water‐saturation spectra (Z‐spectra) and corresponding amide proton transfer (APT) signals were estimated from human brain scans of healthy volunteers at 3 T.ResultsThe multi‐echo diffusion results showed that the intracellular water fractions were significantly higher than the extracellular water fractions, whereas the intracellular T2 values were shorter than those of the extracellular/intravascular compartments. The ADC for the intracellular compartment was significantly lower than that of the extracellular compartment. The dCEST analysis showed that the average intracellular and extracellular fractions of the Z‐spectra were 85 ± 7% and 15 ± 4%, respectively. The overall intracellular APT‐weighted values were higher than the total (i.e., intracellular + extracellular) APT‐weighted values.ConclusionsThe dCEST imaging technique provides valuable insight into the source of signals in conventional CEST MRI, offering potential utility for clinical applications.

Quantitative molecular imaging using deep magnetic resonance fingerprinting.

Deep learning-based saturation transfer magnetic resonance fingerprinting (MRF) is an emerging approach for noninvasive in vivo imaging of proteins, metabolites and pH. It involves a series of steps, including sample/participant preparation, image acquisition schedule design, biophysical model formulation and artificial intelligence and computational model training, followed by image acquisition, deep reconstruction and analysis. Saturation transfer-based molecular MRI has been slow to reach clinical maturity and adoption for clinical practice due to its technical complexity, semi-quantitative contrast-weighted nature and long scan times needed for the extraction of quantitative molecular biomarkers. Deep MRF provides solutions to these challenges by providing a quantitative and rapid framework for extracting biologically and clinically meaningful molecular information. Here we define a complete protocol for quantitative molecular MRI using deep MRF. We describe in vitro sample preparation and animal and human scan considerations, and provide intuition behind the acquisition protocol design and optimization of chemical exchange saturation transfer (CEST) and semi-solid magnetization transfer (MT) quantitative imaging. We then extensively describe the building blocks for several artificial intelligence models and demonstrate their performance for different applications, including cancer monitoring, brain myelin imaging and pH quantification. Finally, we provide guidelines to further modify and expand the pipeline for imaging a variety of other pathologies (such as neurodegeneration, stroke and cardiac disease), accompanied by the related open-source code and sample data. The procedure takes between 48 min (for two proton pools or in vitro imaging) and 57 h (for complex multi-proton pool in vivo imaging) to complete and is suitable for graduate student-level users.

Quantitative multislice and jointly optimized rapid CEST for in vivo whole‐brain imaging

PurposeTo develop a quantitative multislice chemical exchange saturation transfer (CEST) schedule optimization and pulse sequence that reduces the loss of sensitivity inherent to multislice sequences.MethodsA deep learning framework was developed for simultaneous optimization of scan parameters and slice order. The optimized sequence was tested in numerical simulations against a random schedule and an optimized single‐slice schedule. The scan efficiency of each schedule was quantified. Three healthy subjects were scanned with the proposed sequence. Regions of interest in white matter (WM) and gray matter (GM) were defined. The sequence was compared with the single‐slice sequence in vivo and differences quantified using Bland–Altman plots. Test–retest reproducibility was assessed, and the Lin's concordance correlation coefficient (CCC) was calculated for WM and GM. Intersubject variability was also measured with the CCC. Feasibility of whole‐brain clinical imaging was tested using a multislab acquisition in 1 subject.ResultsThe optimized multislice sequence yielded a lower mean error than the random schedule for all tissue parameters and a lower error than the optimized single‐slice schedule for four of six parameters. The optimized multislice sequence provided the highest scan efficiency. In vivo tissue‐parameter values obtained with the proposed sequence agreed well with those of the optimized single‐slice sequence and prior studies. The average WM/GM CCC was 0.8151/0.7779 for the test–retest scans and 0.7792/0.7191 for the intersubject variability experiment.ConclusionA multislice schedule optimization framework and pulse sequence were demonstrated for quantitative CEST. The proposed approach enables accurate and reproducible whole‐brain quantitative CEST imaging in clinically relevant scan times.

Validation of qMT and CEST MRI as Biomarkers of Response to Treatment After Lumbar Spinal Cord Injury in Rats.

The progression and repair of a traumatically injured spinal cord (SCI) involves multifactored processes. Noninvasive, mechanism-informative objective biomarkers could greatly facilitate the translation of findings from preclinical animal models to patient applications. We aimed to develop and validate multiparametric chemical exchange saturation transfer (CEST) and quantitative magnetization transfer (qMT) magnetic resonance imaging (MRI) biomarkers for assessing SCI severity, demyelination, and neuroinflammation, as well as the response to neuroprotective drug treatment riluzole. Changes in CEST and qMT MRI metrics before and after a moderate contusion injury at the L1 level of the lumbar spinal cord were compared between two groups of rats that received either the riluzole or a vehicle treatment over 8 weeks. The specificity of these MRI biomarkers was validated by postmortem immunohistology. The functional relevance of these biomarkers was evaluated by correlation with hindlimb sensorimotor and pain behavior. The pool size ratio (PSR) maps from qMT acquisitions of the SCI region in riluzole-treated rats showed increased white matter macromolecular content compared to the HBC vehicle-treated group, suggesting increased myelin levels and possible remyelination of the injured spinal cord. CEST APT pool (3.5 ppm) amplitude decreased at the region rostral to the injury in riluzole-treated rats compared to the vehicle group, indicating potentially reduced neuroinflammatory activity. MRI metrics correlated temporally with behavioral measures of injury severity and recovery. Histological analysis spatially validated MRI-revealed myelination and neuroinflammation status and confirmed differences between the drug and vehicle treatment groups. Quantitative MRI is well suited for monitoring and quantifying the efficacy of pharmacological treatments in preclinical spinal cord injury models. Multiparametric MRI changes in white matter myelination (qMT PSR) and neuroinflammation (CEST APT) in the injured spinal cord were related to injury severity, behavioral deficits, and recovery progression over time. Both imaging metrics captured enhanced recovery from the neuroprotective drug riluzole, supporting the practical utility of these MRI biomarkers.

CEST imaging combined with 1H-MRS reveal the neuroprotective effects of riluzole by improving neurotransmitter imbalances in Alzheimer’s disease mice

BackgroundThe imbalance of glutamate (Glu) and gamma-aminobutyric acid (GABA) neurotransmitter system plays a crucial role in the pathogenesis of Alzheimer’s disease (AD). Riluzole is a Glu modulator originally approved for amyotrophic lateral sclerosis that has shown potential neuroprotective effects in various neurodegenerative disorders. However, whether riluzole can improve Glu and GABA homeostasis in AD brain and its related mechanism of action remain unknown. This study utilized chemical exchange saturation transfer (CEST) imaging combined with proton magnetic resonance spectroscopy (1H-MRS) to monitor the dynamic changes of Glu and GABA in riluzole-treated AD mice, aiming to evaluate the efficacy and mechanism of riluzole in AD treatment.MethodsGluCEST, GABACEST and 1H-MRS were used to longitudinally monitor Glu and GABA levels in 3xTg AD mice treated with riluzole (12.5 mg/kg/day) or vehicle for 20 weeks. Magnetic resonance measurements were performed at baseline, 6, 12, and 20 weeks post-treatment. Cognitive performance was assessed using the Morris Water Maze (MWM) at baseline, 10, and 20 weeks. At the study endpoint, immunohistochemistry, Nissl staining, and Western blot were used to evaluate the brain pathology, neuronal survival, and protein expression.ResultsGluCEST, GABACEST and 1H-MRS consistently revealed higher levels of Glu and GABA in the brain of riluzole-treated AD mice compared to untreated controls, which were associated with improvements in spatial learning and memory. The cognitive improvements significantly correlated with the increased GluCEST signals and Glu levels. Immunohistochemistry and Nissl staining demonstrated that riluzole treatment reduced amyloid-beta (Aβ) deposition, tau hyperphosphorylation, GFAP-positive astrocyte activation, and prevented neuronal loss. Moreover, riluzole upregulated the expression of excitatory amino acid transporter 2 (EAAT2), glutamic acid decarboxylase 65/67 (GAD65/67), and glutamine synthetase (GS), suggesting enhanced neurotransmitter metabolism.ConclusionsCEST imaging combined with 1H-MRS demonstrated the effectiveness of riluzole in modulating Glu- and GABA-related changes and improving cognitive function in 3xTg AD mice, potentially through regulating key proteins involved in neurotransmitter metabolism. These findings suggest riluzole as a therapeutic agent for Alzheimer’s disease and highlight the utility of multimodal MR imaging in monitoring treatment response and exploring disease mechanisms.

DEISM: Deep Reconstruction Framework with Self-calibration Mechanisms for Accelerated Chemical Exchange Saturation Transfer Imaging.

The prolonged scan time of chemical exchange saturation transfer (CEST) imaging, caused by multiple data acquisitions over the varying saturation offset frequencies, necessitates accelerated imaging techniques. In this work, the artifact information is exploited as an important prior for CEST image reconstruction by exploring the spatial-frequential redundancy in the artifact field. Specifically, we proposed a novel deep reconstruction framework with self-calibration mechanisms (DEISM) for highly accelerated CEST imaging. DEISM features two successively concatenated structures: i) a model-based network responsible for initial image reconstruction from undersampled multi-coil k-space data, and ii) a data-driven artifact suppression (AS) network that estimates and corrects the residual artifacts in a self-calibrated manner. In addition, a novel encoder-decoder architecture with a multi-scale feature fusion mechanism is developed and utilized for robust artifact estimation and artifact correction. We trained the DEISM framework end-to-end using simulated data, and evaluated its performance on both healthy volunteers and brain tumor patients, using retrospectively or prospectively undersampled data at various acceleration factors. Experimental results demonstrated the feasibility of the data-driven AS concept and the effectiveness of exploiting the spatial-frequential correlation in the artifact field. By integrating the image artifact priors into the learning-based CEST image reconstruction process, DEISM can provide high-quality source images, molecular maps, and CEST spectra, outperforming the other conventional and state-of-the-art reconstruction techniques.

High-Quality CEST Mapping With Lorentzian-Model Informed Neural Representation.

Chemical Exchange Saturation Transfer (CEST) MRI has demonstrated its remarkable ability to enhance the detection of macromolecules and metabolites with low concentrations. While CEST mapping is essential for quantifying molecular information, conventional methods face critical limitations: model-based approaches are constrained by limited sensitivity and robustness depending heavily on parameter setups, while data-driven deep learning methods lack generalizability across heterogeneous datasets and acquisition protocols. To overcome these challenges, we propose a Lorentzian-model Informed Neural Representation (LINR) framework for high-quality CEST mapping. LINR employs a self-supervised neural architecture embedding the Lorentzian equation - the fundamental biophysical model of CEST signal evolution - to directly reconstruct high-sensitivity parameter maps from raw z-spectra, eliminating dependency on labeled training data. Convergence of the self-supervised training strategy is guaranteed theoretically, ensuring LINR's mathematical validity. The superior performance of LINR in capturing CEST contrasts is revealed through comprehensive evaluations based on synthetic phantoms and in-vivo experiments (including tumor and Alzheimer's disease models). The intuitive parameter-free design enables adaptive integration into diverse CEST imaging workflows, positioning LINR as a versatile tool for non-invasive molecular diagnostics and pathophysiological discovery.

Amide proton transfer weighted MRI measurements yield consistent and repeatable results in patients with gliomas: a prospective test-retest study.

Chemical exchange saturation transfer (CEST) imaging has emerged as a promising imaging biomarker, but its reliability for clinical practice remains uncertain. This study aimed to investigate the robustness of CEST parameters in healthy volunteers and patients with brain tumours. A total of n = 52 healthy volunteers and n = 52 patients with histologically confirmed glioma underwent two consecutive 3-T MRI scans separated by a 1-min break. The CEST measurements were reconstructed using two models: with and without fluid suppression and included the evaluation of both amide (amidePTw) and amine (aminePTw) offsets. Mean intensity values in healthy volunteers were compared from volumetric segmentations (VOI) of grey matter, white matter, and the whole brain. Mean intensity values in brain tumour patients were assessed from VOI of the contrast-enhancing, non-enhancing and whole tumour, as well as from the normal-appearing white matter. Test-retest reliability was assessed using ICC and Bland-Altman plots. The amidePTw/aminePTw signal intensity distribution was significantly affected by fluid suppression (p < 0.001 for each VOI). Test-retest reliability in healthy volunteers showed fair to excellent agreement (ICC = 0.53-0.74), with the highest signal intensity values observed by amidePTw (ICC = 0.73-0.74). In patients, an excellent agreement of both amidePTw and aminePTw measurements was observed across different tumour regions (ICC = 0.76-0.89), with the highest ICC for contrast-enhancing tumour measurements. Bland-Altman analysis indicated negligible systematic bias and no proportional bias in measurement errors. Measurements from amide/aminePTw imaging obtained from an adequately powered test-retest study yield consistent and reproducible results in glioma patients, as a prerequisite for robust imaging biomarker discovery in neuro-oncology. Question The clinical reliability of chemical exchange saturation transfer imaging remains uncertain, necessitating further investigation to establish its robustness as a biomarker in neuro-oncology. Findings This study demonstrates that amide/amine proton transfer imaging provides repeatable, high-agreement measurements in glioma patients, particularly in contrast-enhancing tumour regions. Clinical relevance This test-retest study demonstrates that chemical exchange saturation transfer imaging using two models and assessing amide and amine offsets yield consistent and repeatable results in glioma patients, as a prerequisite for robust imaging biomarker discovery for neuro-oncology studies and clinical practice.

Visualization of Unconjugated Bilirubin In Vivo with a Novel Approach Using Chemical Exchange Saturation Transfer Magnetic Resonance Imaging in a Rat Model.

Unconjugated bilirubin (UCB) visualization is valuable for early bilirubin encephalopathy (BE) diagnosis and management. UCB neurotoxicity is a challenge, necessitating improved imaging modalities for precise localization and characterization. This study developed a noninvasive method for UCB imaging in the brain using chemical exchange saturation transfer (CEST) magnetic resonance imaging, which visualizes UCB distribution through amide-bulk water proton exchange, a process termed bilirubin CEST (Bil-CEST) imaging. Bil-CEST imaging parameters were initially optimized; the exchange rate of the amide protons of UCB was calculated. Bil-CEST imaging characteristics and specificity were assessed using in vitro images of UCB solutions under different conditions and images of other brain metabolites. Bil-CEST maps of the rat brain were collected at the baseline and dynamically, postinjection of the UCB solution or vehicle into lateral ventricles of Sprague-Dawley rats. The model was validated using a water maze and pathological staining. In vitro, the Bil-CEST effect was observed at approximately 5.5 ppm downfield from bulk water. This effect was proportional to the UCB concentration and B1 amplitude. In vivo, Bil-CEST imaging revealed a progressive enhancement following a lateral ventricular UCB injection. Conversely, no significant imaging changes were observed in the vehicle group. Compared with the vehicle group, the UCB group had more hippocampal neuronal apoptosis and worse cognitive function. These findings highlight the utility of Bil-CEST in direct UCB imaging, indicating its potential as a clinically valuable biomarker for BE diagnosis and management.

Complexes of Iron(II), Cobalt(II), and Nickel(II) with DOTA-Tetraglycinate for pH and Temperature Imaging Using Hyperfine Shifts of an Amide Moiety.

Paramagnetic complexes of 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetate (DOTA4-) derivatives have shown potential for molecular imaging with magnetic resonance. DOTA-tetraglycinate (DOTA-4AmC4-) coordinated with lanthanide metal ions (Ln3+) demonstrates pH/temperature sensing with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) and Chemical Exchange Saturation Transfer (CEST), respectively, detecting nonexchangeable (e.g., -CHy, where 3 ≥ y ≥ 1) and exchangeable (e.g., -OH or -NHx, where 2 ≥ x ≥ 1) protons. Herein, we report paramagnetic complexes of divalent transition-metal ions (M2+ = Fe2+, Co2+, Ni2+) with DOTA-4AmC4- that endow a unique amide proton (-NH) moiety for pH/temperature sensing. Crystallographic data reveal that DOTA-4AmC4- coordinates with M2+ through oxygen and nitrogen donor atoms, ranging in coordination numbers from 8-coordinate in Fe(II)DOTA-4AmC2-, 7-coordinate in Co(II)DOTA-4AmC2-, and 6-coordinate in Ni(II)DOTA-4AmC2-. The -CHy protons in M(II)DOTA-4AmC2- displayed modest pH/temperature sensitivities, but -NH protons exhibited higher intensity, suggesting prominent BIRDS properties. The pH sensitivity was the highest for Ni(II)DOTA-4AmC2- (1.42 ppm/pH), followed by Co(II)DOTA-4AmC2- (0.21 ppm/pH) and Fe(II)DOTA-4AmC2- (0.16 ppm/pH), whereas temperature sensitivities were comparable (i.e., 0.22, 0.13, and 0.17 ppm/°C, respectively). The CEST image contrast for -NH in M(II)DOTA-4AmC2- was much weaker compared to that of Ln(III)DOTA-4AmC-. Given its high pH sensitivity and low cytotoxicity, Ni(II)DOTA-4AmC2- shows promise for use in preclinical BIRDS-based pH imaging.

Glucose chemical exchange saturation transfer MRI for predicting the histological grade of rectal cancer: a comparative study with amide proton transfer-weighted and diffusion-weighted imaging

BackgroundTo evaluate the utility of glucose chemical exchange saturation transfer (glucoCEST) MRI with non-contrast injection in predicting the histological grade of rectal cancer.MethodsThis prospective analysis included 60 patients with preoperative rectal cancer who underwent pelvic glucoCEST, amide proton transfer-weighted imaging (APTWI), and diffusion-weighted imaging (DWI). In total, 21 low-grade and 39 high-grade cases were confirmed by postoperative pathology. The MTRasym (1.2 ppm), MTRasym (3.5 ppm), and apparent diffusion coefficient (ADC) values of lesions between the low-grade and high-grade groups were compared. The area under the receiver operating characteristic curve (AUC) was generated to evaluate the diagnostic performance of each technique. Logistic regression (LR) analysis was applied to determine independent predictors and for multi-parameter combined diagnosis.ResultsElevated MTRasym (1.2 ppm), MTRasym (3.5 ppm) values and lower ADC values were observed in the high-grade group compared with low-grade cases (all p < 0.01). The AUCs of MTRasym (1.2 ppm), MTRasym (3.5 ppm), and ADC for differentiating between low- and high-grade rectal cancer cases were 0.792, 0.839, and 0.855, respectively. The diagnostic performance of the combination of the three indexes was improved (AUC, 0.969; sensitivity, 95.24%; specificity, 87.18%). The good consistency and reliability of the combination of independent predictors were demonstrated by calibration curve analysis and DCA.ConclusionThe glucoCEST MRI without contrast injection, APTWI, and DWI all facilitate the assessment of histological grade in rectal cancer, and the combination of the three can effectively discriminate between high- and low-grade rectal cancer, which is expected to be a promising imaging marker.Critical relevance statementThe glucose chemical exchange saturation transfer MRI method facilitates the assessment of histological grade in rectal cancer and offers additional information to improve the diagnostic performance of amide proton transfer-weighted imaging, and diffusion-weighted imaging.Key PointsGlucose chemical exchange saturation transfer imaging could differentiate histological grade.Amide proton transfer-weighted and diffusion-weighted were associated with histological grade.The combination of different parameters showed the best diagnostic performance.Graphical

Magnetic resonance imaging techniques for monitoring glioma response to chemoradiotherapy.

Treatment response assessment for gliomas currently uses changes in tumour size as measured with T1- and T2-weighted MRI. However, changes in tumour size may occur many weeks after therapy completion and are confounded by radiation treatment effects. Advanced MRI techniques sensitive to tumour physiology may provide complementary information to evaluate tumour response at early timepoints during therapy. The objective of this review is to provide a summary of the history and current knowledge regarding advanced MRI techniques for early treatment response evaluation in glioma. The literature survey included perfusion MRI, diffusion-weighted imaging, quantitative magnetization transfer imaging, and chemical exchange transfer MRI. Select articles spanning the history of each technique as applied to treatment response evaluation in glioma were chosen. This report is a narrative review, not formally systematic. Chemical exchange saturation transfer imaging potentially offers the earliest method to detect tumour response due to changes in metabolism. Diffusion-weighted imaging is sensitive to changes in tumour cellularity later during radiotherapy and is prognostic for progression-free and overall survival. Substantial evidence suggests that perfusion MRI can differentiate between tumour recurrence and treatment effect, but consensus regarding acquisition, processing, and interpretation is still lacking. Magnetization transfer imaging shows promise for detecting subtle white matter damage which could indicate tumour invasion, but more research in this area is needed. Advanced MRI techniques show potential for early treatment response assessment, but each technique alone lacks specificity. Multiparametric imaging may be necessary to aid biological interpretation and enable treatment guidance.

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

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

Scan-Specific Unsupervised Highly Accelerated Non-Cartesian CEST Imaging Using Implicit Neural Representation and Explicit Sparse Prior.

Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique. CEST imaging usually requires a long scan time, and reducing acquisition time is highly desirable for clinical applications. A novel scan-specific unsupervised deep learning algorithm is proposed to accelerate steady-state pulsed CEST imaging with golden-angle stack-of-stars trajectory using hybrid-feature hash encoding implicit neural representation. Additionally, imaging quality is further improved by using the explicit prior knowledge of low rank and weighted joint sparsity in the spatial and Z-spectral domain of CEST data. In the retrospective acceleration experiment, the proposed method outperforms other state-of-the-art algorithms (TDDIP, LRTES, kt-SLR, NeRP, CRNN, and PBCS) for the in vivo human brain dataset under various acceleration rates. In the prospective acceleration experiment, the proposed algorithm can still obtain results close to the fully-sampled images. The hybrid-feature hash encoding implicit neural representation combined with explicit sparse prior (INRESP) can efficiently accelerate CEST imaging. The proposed algorithm achieves reduced error and improved image quality compared to several state-of-the-art algorithms at relatively high acceleration factors. The superior performance and the training database-free characteristic make the proposed algorithm promising for accelerating CEST imaging in various applications.

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.