A review of optimization and quantification techniques for chemical exchange saturation transfer MRI toward sensitive in vivo imaging

B1 inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T

PURPOSE

CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model.

METHODS

Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 μT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/B 1 2 $$ {B}_1^2 $$ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach.

RESULTS

B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices.

CONCLUSION

The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.

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.

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.

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.

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.

A generalized QUCESOP method with evaluating CEST peak overlap

The overlapping peaks of the target chemical exchange saturation transfer (CEST) solutes and other unknown CEST solutes affect the quantification results and accuracy of the chemical exchange parameters-the fractional concentration, f b , exchange rate, k b , and transverse relaxation rate, R 2 b -for the target solutes. However, to date, no method has been established for assessing the overlapping peaks. This study aimed to develop a method for quantifying the f b , k b , and R 2 b values of a specific CEST solute, as well as assessing the overlap between the CEST peaks of the specific solute(s) and other unknown solutes. A simplified R 1 ρ model was proposed, assuming linear approximation of the other solutes' contributions to R 1 ρ . A CEST data acquisition scheme was applied with various saturation offsets and saturation powers. In addition to fitting the f b , k b , and R 2 b values of the specific solute, the overlapping condition was evaluated based on the root mean square error (RMSE) between the trajectories of the acquired and synthesized data. Single-solute and multi-solute phantoms with various phosphocreatine (PCr) concentrations and pH values were used to calculate the f b and k b of PCr and the corresponding RMSE. The feasibility of RMSE for evaluating the overlapping condition, and the accurate fitting of f b and k b in weak overlapping conditions, were verified. Furthermore, the method was employed to quantify the nuclear Overhauser effect signal in rat brains and the PCr signal in rat skeletal muscles, providing results that were consistent with those reported in previous studies. In summary, the proposed approach can be applied to evaluate the overlapping condition of CEST peaks and quantify the f b , k b , and R 2 b values of specific solutes, if the weak overlapping condition is satisfied.

Machine learning-based amide proton transfer imaging using partially synthetic training data

PURPOSE

Machine learning (ML) has been increasingly used to quantify CEST effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, whereas training with fully simulated data may introduce bias because of limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect.

METHODS

Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9 L tumors.

RESULTS

Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data.

CONCLUSION

Partially synthetic CEST data can address the challenges in conventional ML methods.

Demonstration of magnetic resonance Z-spectral imaging for fatty acid characterization of bone marrow at 3 T

Magnetic resonance Z-spectral imaging (ZSI) has emerged as a new approach to measure fat fraction (FF). However, its feasibility for fat spectral imaging remains to be elucidated. In this study, a single-slice ZSI sequence dedicated to fat spectral imaging was designed, and its capability for fatty acid characterization was investigated on peanut oil samples, a multiple-vial fat-water phantom with varied oil volumes, and vertebral body marrow in healthy volunteers and osteoporosis patients at 3 T. The peanut oil spectrum was also recorded with a 400-MHz NMR spectrometer. A Gaussian-Lorentzian sum model was used to resolve water and six fat signals of the pure oil sample or four fat signals of the fat-water phantom or vertebral bone marrow from Z spectra. Fat peak amplitudes were normalized to the total peak amplitude of water and all fat signals. Normalized fat peak amplitudes and FF were quantified and compared among vials of the fat-water phantom or between healthy volunteers and osteoporosis patients. An unpaired student's t-test and Pearson's correlation were conducted, with p less than 0.05 considered statistically significant. The results showed that the peanut oil spectra measured with the ZSI technique were in line with respective NMR spectra, with amplitudes of the six fat signal peaks significantly correlated between the two methods (y = x + 0.001, r = 0.996, p < 0.001 under a repetition time of 1.6 s; and y = 1.026x - 0.003, r = 0.996, p < 0.001 under a repetition time of 3.1 s). Moreover, ZSI-measured FF exhibited a significant correlation with prepared oil volumes (y = 0.876x + 1.290, r = 0.996, p < 0.001). The osteoporosis patients showed significantly higher normalized fat peak amplitudes and FF in the L4 vertebral body marrow than the healthy volunteers (all p < 0.01). In summary, the designed ZSI sequence is feasible for fatty acid characterization, and has the potential to facilitate the diagnosis and evaluation of diseases associated with fat alterations at 3 T.

Transmembrane pH gradient imaging in rodent glioma models

A unique feature of the tumor microenvironment is extracellular acidosis in relation to intracellular milieu. Metabolic reprogramming in tumors results in overproduction of H+ ions (and lactate), which are extruded from the cells to support tumor survival and progression. As a result, the transmembrane pH gradient (ΔpH), representing the difference between intracellular pH (pHi) and extracellular pH (pHe), is posited to be larger in tumors compared with normal tissue. Controlling the transmembrane pH difference has promise as a potential therapeutic target in cancer as it plays an important role in regulating drug delivery into cells. The current study shows successful development of an MRI/MRSI-based technique that provides ΔpH imaging at submillimeter resolution. We applied this technique to image ΔpH in rat brains with RG2 and U87 gliomas, as well as in mouse brains with GL261 gliomas. pHi was measured with Amine and Amide Concentration-Independent Detection (AACID), while pHe was measured with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS). The results indicate that pHi was slightly higher in tumors (7.40-7.43 in rats, 7.39-7.47 in mice) compared with normal brain (7.30-7.38 in rats, 7.32-7.36 in mice), while pHe was significantly lower in tumors (6.62-6.76 in rats, 6.74-6.84 in mice) compared with normal tissue (7.17-7.22 in rats, 7.20-7.21 in mice). As a result, ΔpH was higher in tumors (0.64-0.81 in rats, 0.62-0.65 in mice) compared with normal brain (0.13-0.16 in rats, 0.13-0.16 in mice). This work establishes an MRI/MRSI-based platform for ΔpH imaging at submillimeter resolution in gliomas.

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.

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.

A rapid method for phosphocreatine-weighted imaging in muscle using double saturation power-chemical exchange saturation transfer

Monitoring the variation in phosphocreatine (PCr) levels following exercise provides valuable insights into muscle function. Chemical exchange saturation transfer (CEST) has emerged as a sensitive method with which to measure PCr levels in muscle, surpassing conventional MR spectroscopy. However, existing approaches for quantifying PCr CEST signals rely on time-consuming fitting methods that require the acquisition of the entire or a section of the CEST Z-spectrum. Additionally, traditional fitting methods often necessitate clear CEST peaks, which may be challenging to obtain at low magnetic fields. This paper evaluated the application of a new model-free method using double saturation power (DSP), termed DSP-CEST, to estimate the PCr CEST signal in muscle. The DSP-CEST method requires the acquisition of only two or a few CEST signals at the PCr frequency offset with two different saturation powers, enabling rapid dynamic imaging. Additionally, the DSP-CEST approach inherently eliminates confounding signals, offering enhanced robustness compared with fitting methods. Furthermore, DSP-CEST does not demand clear CEST peaks, making it suitable for low-field applications. We evaluated the capability of DSP-CEST to enhance the specificity of PCr CEST imaging through simulations and experiments on muscle tissue phantoms at 4.7 T. Furthermore, we applied DSP-CEST to animal leg muscle both before and after euthanasia and observed successful reduction of confounding signals. The DSP-CEST signal still has contaminations from a residual magnetization transfer (MT) effect and an aromatic nuclear Overhauser enhancement effect, and thus only provides a PCr-weighted imaging. The residual MT effect can be reduced by a subtraction of DSP-CEST signals at 2.6 and 5 ppm. Results show that the residual MT-corrected DSP-CEST signal at 2.6 ppm has significant variation in postmortem tissues. By contrast, both the CEST signal at 2.6 ppm and a conventional Lorentzian difference analysis of CEST signal at 2.6 ppm demonstrate no significant variation in postmortem tissues.

A Magnetic Resonance Imaging-Chemical Exchange Saturation Transfer (MRI-CEST) Method for the Detection of Water Cycling across Cellular Membranes

Water cycling across the membrane transporters is considered a hallmark of cellular metabolism and it could be of high diagnostic relevance in the characterization of tumors and other diseases. The method relies on the response of intracellular proton exchanging molecules to the presence of extracellular Gd-based contrast agents (GBCAs). Paramagnetic GBCAs enhances the relaxation rate of water molecules in the extracellular compartment and, through membrane exchange, the relaxation enhancement is transferred to intracellular molecules. The effect is detected at the MRI-CEST (Magnetic Resonance Imaging - Chemical Exchange Saturation Transfer) signal of intracellular proton exchanging molecules. The magnitude of the change in the CEST response reports on water cycling across the membrane. The method has been tested on Red Blood Cells and on orthotopic murine models of breast cancer with different degree of malignancy (4T1, TS/A and 168FARN). The distribution of voxels reporting on membrane permeability fits well with the cells' aggressiveness and acts as an early reporter to monitor therapeutic treatments.

Amide proton transfer-weighted MRI for renal tumors: Comparison with diffusion-weighted imaging

OBJECTIVE

To investigate the potential of amide proton transfer-weighted (APTw) MRI in identifying benign and malignant renal tumors and to evaluate whether APTw MRI can add diagnostic value to diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Participants with renal tumor underwent preoperative multiparametric MRI, including APTw MRI and DWI. The APTw and apparent diffusion coefficient (ADC) of malignant tumors and benign tumors were calculated independently by two radiologists and compared. The value of the mean APTw and the mean ADC for differentiating malignant and benign tumors was evaluated by receiver operating characteristic analysis.

RESULTS

In total, 65 participants (mean age, 59 years ±14; 41 men) were evaluated: 54 with malignant and 11 with benign renal tumors. Malignant renal tumors showed higher mean APTw values [2.03% (1.63) vs 1.00% (1.60); P < 0.01] and lower mean ADC values (1.22 × 10-3 mm2/s ± 0.37 vs 1.51 × 10-3 mm2/s ± 0.37; P < 0.05) than benign renal tumors. The area under the receiver operating characteristic curve (AUC) of APTw, ADC and the combination of them for the identification of benign and malignant renal tumors was 0.78(95% CI: 0.66, 0.87; P < 0.001),0.70(95% CI: 0.54, 0.86; P < 0.05) and 0.79 (95% CI: 0.67, 0.88; P < 0.001). The optimal cutoff value for mean APTw was 2.14% (sensitivity, 74%; specificity, 73%). There was no difference between these three parameters for differentiating malignant from benign renal tumors (P > 0.05).

CONCLUSION

The APTw MRI has the potential use as an imaging biomarker for renal malignant and benign tumors.

Image quality assessment and feasibility of three-dimensional amide proton transfer-weighted imaging for hepatocellular carcinoma

Background

With the continuous innovation of magnetic resonance imaging (MRI) hardware and software technology, amide proton transfer-weighted (APTw) imaging has been applied in liver cancer. However, to our knowledge, no study has evaluated the feasibility of a three-dimensional amide proton transfer-weighted (3D-APTw) imaging sequence for hepatocellular carcinoma (HCC). This study thus aimed to conduct an image quality assessment of 3D-APTw for HCC and to explore its feasibility.

Methods

3D-APTw MRI examinations were completed in 134 patients with clinically suspected HCC. According to the uniformity of APTw signal in the liver and within the lesion and the proportion of artifact and missing signal regions, APTw images were subjectively scored using a 5-point scale. The scanning success rate of liver APTw imaging was calculated as the ratio of the number of cases with a quality assurance measurement of more than 3 to the total number of HCC cases. The intra- and interobserver quality assurance measurements for APTw images were compared via the Kappa consistency test. Within the HCC cases with a minimum image quality threshold of 3 points, the APT values of HCC and the liver parenchyma, signal-to-noise ratio of APT-weighted images (SNRAPTw), and contrast-to-noise ratio of HCC (CNRHCC) were measured by two observers. The intra- and interobserver agreement was assessed using the intraclass correlation coefficient (ICC). The differences in APT values between HCC and liver parenchyma was determined using the Mann-Whitney test.

Results

Sixty-six HCC cases with a quality assurance measurement of APTw imaging were included in the final analysis, and the calculated success rate was 70.21% (66/94). The subjective APT image quality scores of the two observers were consistent (3.66±1.18, 3.50±1.19, and 3.68±1.18), and no intergroup or intragroup statistical differences were found (P=0.594, and P=0.091), but the consistency of inter- and intraobserver was not as satisfactory (κ=0.594 and κ=0.580). The APT values in HCC lesion were significantly higher than those in liver parenchyma (2.73%±0.91% vs. 1.62%±0.55%; P<0.001). The APT values in HCC showed favorable intra- and interobserver consistency between the two observers (ICC =0.808 and ICC =0.853); the APT values in liver parenchyma, SNRAPTw, and CNRHCC values had moderate intraobserver consistency (ICC =0.578, ICC =0.568, and ICC =0.508) and interobserver consistency (ICC =0.599, ICC =0.199, and ICC =0.650). The coefficients of variation of the APTw values in the HCC lesion and in liver parenchyma were 33.4% and 34.4%, respectively. The SNRAPTw and CNRHCC were 30.75±18.74 and 3.56±3.19, with a coefficient of variation of 60.9% and 74.9%, respectively.

Conclusions

Liver 3D-APTw imaging was preliminarily demonstrated to be clinically feasible for evaluating HCC.

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.

Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage.

Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data

Purpose

Machine learning (ML) has been increasingly used to quantify chemical exchange saturation transfer (CEST) effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, while training with fully simulated data may introduce bias due to limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect.

Methods

Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9L tumors.

Results

Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data.

Conclusion

Partially synthetic CEST data can address the challenges in conventional ML methods.

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.

Assessing the predictability of the H3K27M status in diffuse glioma patients using frequency importance analysis on chemical exchange saturation transfer MRI

BACKGROUND AND OBJECTIVES

In diffuse glioma patients, Lys-27-Met mutations in histone 3 genes (H3K27M) are associated with an aggravated prognosis and further decreased overall survival. By using frequency importance analysis on chemical exchange saturation transfer (CEST) MRI, this study aimed to assess the predictability of the H3K27M status in diffuse glioma patients.

METHODS

Twenty-two patients diagnosed with diffuse glioma, with a known H3K27M status, were included in the present study. All patients underwent CEST MRI scans. The previously proposed frequency importance analysis was performed to determine the relative contribution of the amide and aliphatic protons for the differentiation between normal tissues and tumors. For this comparison, the conventional MTRasym analysis of amide protons at 3.5 ppm, i.e., the amide proton transfer-weighted (APTw) signal, was employed. Statistical analysis was performed using the Mann-Whitney U test, and the receiver operating characteristic (ROC) and area under the curve (AUC) analyses.

RESULTS

The mean and 90th percentile of the ΔAPTw intensities, amide and aliphatic frequency importance values revealed statistically significant differences between the wildtype and the H3K27M-altered patient groups (p < 0.05). For the prediction of the H3K27M status, amide frequency importance achieved highest AUCs of 0.97, with a specificity of 0.93. In contrast, the ΔAPTw intensities and aliphatic frequency importance showed relatively lower AUCs (<0.35) in predicting the H3K27M status.

CONCLUSIONS

Amide frequency importance exhibited satisfactory performance in the prediction of the H3K27M status. As such, it may be considered as a non-invasive MRI biomarker for the diagnosis of diffuse gliomas.

Validation of the presence of fast exchanging amine CEST effect at low saturation powers and its influence on the quantification of APT

PURPOSE

Accurately quantifying the amide proton transfer (APT) effect and the underlying exchange parameters is crucial for its applications, but previous studies have reported conflicting results. In these quantifications, the CEST effect from the fast exchange amine was always ignored because it was considered weak with low saturation powers. This paper aims to evaluate the influence of the fast exchange amine CEST on the quantification of APT at low saturation powers.

METHODS

A quantification method with low and high saturation powers was used to distinguish APT from the fast exchange amine CEST effect. Simulations were conducted to assess the method's capability to separate APT from the fast exchange amine CEST effect. Animal experiments were performed to assess the relative contributions from the fast exchange amine and amide to CEST signals at 3.5 ppm. Three APT quantification methods, each with varying degrees of contamination from the fast exchange amine, were employed to process the animal data to assess the influence of the amine on the quantification of APT effect and the exchange parameters.

RESULTS

The relative size of the fast exchange amine CEST effect to APT effect gradually increases with increasing saturation power. At 9.4 T, it increases from approximately 20% to 40% of APT effect with a saturation power increase from 0.25 to 1 μT.

CONCLUSION

The fast exchange amine CEST effect leads overestimation of APT effect, fitted amide concentration, and amide-water exchange rate, potentially contributing to the conflicting results reported in previous studies.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Evaluation of contributors to amide proton transfer-weighted imaging and nuclear Overhauser enhancement-weighted imaging contrast in tumors at a high magnetic field

PURPOSE

The purpose is to evaluate the relative contribution from confounding factors (T1 weighting and magnetization transfer) to the CEST ratio (CESTR)-quantified amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) (-3.5) in tumors as well as whether the CESTR can reflect the distribution of the solute concentration (fs ).

METHODS

We first provided a signal model that shows the separate dependence of CESTR on these confounding factors and the clean CEST/NOE effects quantified by an apparent exchange-dependent relaxation (AREX) method. We then measured the change in these effects in the 9-L tumor model in rats, through which we calculated the relative contribution of each confounding factor. fs was also fitted, and its correlations with the CESTR and AREX were assessed to evaluate their capabilities to reflect fs .

RESULTS

The CESTR-quantified APT shows "positive" contrast in tumors, which arises primarily from R1w at low powers and both R1w and magnetization transfer at high powers. CESTR-quantified NOE (-3.5) shows no or weak contrast in tumors, which is due to the cancelation of R1w and NOE (-3.5), which have opposite contributions. CESTR-quantified APT has a stronger correlation with APT fs than AREX-quantified APT. CESTR-quantified NOE (-3.5) has a weaker correlation with NOE (-3.5) fs than AREX-quantified NOE (-3.5).

CONCLUSION

CESTR reflects a combined effect of T1 weighting and CEST/NOE. Both factors depend on fs , which contributes positively to the dependence of CESTR on fs in APT imaging and enhances its correlation with fs . In contrast, these factors have opposite contributions to its dependence on fs in NOE (-3.5) imaging, thereby weakening the correlation.

Assignment of molecular origins of NOE signal at -3.5 ppm in the brain

PURPOSE

Nuclear Overhauser enhancemen mediated saturation transfer effect, termed NOE (-3.5 ppm), is a major source of CEST MRI contrasts at 3.5 ppm in the brain. Previous phantom experiments have demonstrated that both proteins and lipids, two major components in tissues, have substantial contributions to NOE (-3.5 ppm) signals. Their relative contributions in tissues are informative for the interpretation of NOE (-3.5 ppm) contrasts that could provide potential imaging biomarkers for relevant diseases, which remain incompletely understood.

METHODS

Experiments on homogenates and supernatants of brain tissues collected from healthy rats, that could isolate proteins from lipids, were performed to evaluate the relative contribution of lipids to NOE (-3.5 ppm) signals. On the other hand, experiments on ghost membranes with varied pH, and reconstituted phospholipids with different chemical compositions were conducted to study the dependence of NOE (-3.5 ppm) on physiological conditions. Besides, CEST imaging on rat brains bearing 9 L tumors and healthy rat brains was performed to analyze the causes of the NOE (-3.5 ppm) contrast variations between tumors and normal tissues, and between gray matter and white matter.

RESULTS

Our experiments reveal that lipids have dominant contributions to the NOE (-3.5 ppm) signals. Further analysis suggests that decreased NOE (-3.5 ppm) signals in tumors and higher NOE (-3.5 ppm) signals in white matter than in gray matter are mainly explained by changes in membrane lipids, rather than proteins.

CONCLUSION

NOE (-3.5 ppm) could be exploited as a highly sensitive MRI contrast for imaging membrane lipids in the brain.

Detection of tissue pH with quantitative chemical exchange saturation transfer magnetic resonance imaging

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a novel means for sensitive detection of dilute labile protons and chemical exchange rates. By sensitizing to pH-dependent chemical exchange, CEST MRI has shown promising results in monitoring tissue statuses such as pH changes in disorders like acute stroke, tumor, and acute kidney injury. This article briefly reviews the basic principles for CEST imaging and quantitative measures, from the simplistic asymmetry analysis to multipool Lorentzian decoupling and quasi-steady-state reconstruction. In particular, the advantages and limitations of commonly used quantitative approaches for CEST applications are discussed.

Frequency importance analysis for chemical exchange saturation transfer magnetic resonance imaging using permuted random forest

Chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) is a promising molecular imaging tool that allows sensitive detection of endogenous metabolic changes. However, because the CEST spectrum does not display a clear peak like MR spectroscopy, its signal interpretation is challenging, especially under 3-T field strength or with a large saturation B₁ . Herein, as an alternative to conventional Z-spectral fitting approaches, a permuted random forest (PRF) method is developed to determine featured saturation frequencies for lesion identification, so-called CEST frequency importance analysis. Briefly, voxels in the CEST dataset were labeled as lesion and control according to multicontrast MR images. Then, by considering each voxel's saturation signal series as a sample, a permutation importance algorithm was employed to rank the contribution of saturation frequency offsets in the differentiation of lesion and normal tissue. Simulations demonstrated that PRF could correctly determine the frequency offsets (3.5 or -3.5 ppm) for classifying two groups of Z-spectra, under a range of B₀ , B₁ conditions and sample sizes. For ischemic rat brains, PRF only displayed high feature importance around amide frequency at 2 h postischemia, reflecting that the pH changes occurred at an early stage. By contrast, the data acquired at 24 h postischemia exhibited high feature importance at multiple frequencies (amide, water, and lipids), which suggested the complex tissue changes that occur during the later stages. Finally, PRF was assessed using 3-T CEST data from four brain tumor patients. By defining the tumor region on amide proton transfer-weighted images, PRF analysis identified different CEST frequency importance for two types of tumors (glioblastoma and metastatic tumor) (p < 0.05, with each image slice as a subject). In conclusion, the PRF method was able to rank and interpret the contribution of all acquired saturation offsets to lesion identification; this may facilitate CEST analysis in clinical applications, and open up new doors for comprehensive CEST analysis tools other than model-based approaches.

Generalization of quasi-steady-state reconstruction to CEST MRI with two-tiered RF saturation and gradient-echo readout-Synergistic nuclear Overhauser enhancement contribution to brain tumor amide proton transfer-weighted MRI

PURPOSE

While amide proton transfer-weighted (APTw) MRI has been adopted in tumor imaging, there are concurrent APT, magnetization transfer, and nuclear Overhauser enhancement changes. Also, the APTw image is confounded by relaxation changes, particularly when the relaxation delay and saturation time are not sufficiently long. Our study aimed to extend a quasi-steady-state (QUASS) solution to determine the contribution of the multipool CEST signals to the observed tumor APTw contrast.

METHODS

Our study derived the QUASS solution for a multislice CEST-MRI sequence with an interleaved RF saturation and gradient-echo readout between signal averaging. Multiparametric MRI scans were obtained in rat brain tumor models, including T₁ , T₂ , diffusion, and CEST scans. Finally, we performed spinlock model-based multipool fitting to determine multiple concurrent CEST signal changes in the tumor.

RESULTS

The QUASS APTw MRI showed small but significant differences in normal and tumor tissues and their contrast from the acquired asymmetry calculation. The spinlock model-based fitting showed significant differences in semisolid magnetization transfer, amide, and nuclear Overhauser enhancement effects between the apparent and QUASS CEST MRI. In addition, we determined that the tumor APTw contrast is due to synergistic APT increase (+3.5 ppm) and NOE decrease (-3.5 ppm), with their relative contribution being about one third and two thirds under a moderate B₁ of 0.75 μT at 4.7 T.

CONCLUSION

Our study generalized QUASS analysis to gradient-echo image readout and quantified the underlying tumor CEST signal changes, providing an improved elucidation of the commonly used APTw MRI.

Demonstration of accurate multi-pool chemical exchange saturation transfer MRI quantification - Quasi-steady-state reconstruction empowered quantitative CEST analysis

Chemical exchange saturation transfer (CEST) MRI is sensitive to dilute labile protons and microenvironment properties, yet CEST quantification has been challenging. This difficulty is because the CEST measurement depends not only on the underlying CEST system but also on the scan protocols, including RF saturation amplitude, duration, and repetition time. In addition, T1 normalization is not straightforward under non-equilibrium conditions. Recently, a quasi-steady-state (QUASS) algorithm was established to reconstruct the desired equilibrium state from experimental measurements. Our study aimed to determine the accuracy of spinlock-model-based multi-pool CEST quantification using numerical simulations and phantom experiments. In short, CEST Z-spectra were simulated for a representative 3-pool model, and CEST amplitudes were solved with spinlock model-based multi-pool fitting and assessed as a function of RF saturation time (Ts), repetition time (TR), and T1. Although the apparent CEST signals showed significant T1 dependence, such relationships were not observed following QUASS reconstruction. To test the accuracy of T1 correction, a multi-vial phantom of nicotinamide and creatine was doped with manganese chloride, resulting in T1 ranging from 1 s to beyond 2 s. The multi-labile signals determined from the routine measurements showed significant dependence on Ts, TR, and T1. In contrast, CEST signals from the QUASS reconstruction showed consistent quantification independent of such variables. To summarize, our study demonstrated that accurate CEST quantification is feasible in multi-pool CEST systems with the spinlock-model-based fitting of QUASS CEST MRI.

Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response

Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.

Assignment of molecular origins of NOE signal at -3.5 ppm in the brain

Purpose

Nuclear Overhauser Enhancement mediated saturation transfer effect, termed NOE(-3.5 ppm), is a major source of chemical exchange saturation transfer (CEST) MRI contrasts at 3.5 ppm in the brain. Previous phantom experiments have demonstrated that both proteins and lipids, two major components in tissues, have substantial contributions to NOE(-3.5 ppm) signals. Their relative contributions in tissues are informative for the interpretation of NOE(-3.5 ppm) contrasts that could provide potential imaging biomarkers for relevant diseases, which remain incompletely understood.

Methods

Experiments on homogenates and supernatants of brain tissues collected from healthy rats, that could isolate proteins from lipids, were performed to evaluate the relative contribution of lipids to NOE(-3.5 ppm) signals. On the other hand, experiments on ghost membranes with varied pH, and reconstituted phospholipids with different chemical compositions were conducted to study the dependence of NOE(-3.5 ppm) on physiological conditions. Besides, CEST imaging on rat brains bearing 9L tumors and healthy rat brains was performed to analyze the causes of the NOE(-3.5 ppm) contrast variations between tumors and normal tissues, and between gray matter and white matter.

Results

Our experiments reveal that lipids have dominant contributions to the NOE (-3.5 ppm) signals. Further analysis suggests that decreased NOE(-3.5 ppm) signals in tumors and higher NOE(-3.5 ppm) signals in white matter than in gray matter are mainly explained by changes in membrane lipids, rather than proteins.

Conclusion

NOE(-3.5 ppm) could be exploited as a highly sensitive MRI contrast for imaging membrane lipids in the brain.

NOE-weighted imaging in tumors using low-duty-cycle 2π-CEST

PURPOSE

Nuclear Overhauser enhancement (NOE)-mediated CEST imaging at -3.5 ppm has shown clinical interest in diagnosing tumors. Multiple-pool Lorentzian fit has been used to quantify NOE, which, however, requires a long scan time. Asymmetric analysis of CEST signals could be a simple and fast method to quantify this NOE, but it has contamination from the amide proton transfer (APT) at 3.5 ppm. This work proposes a new method using an asymmetric analysis of a low-duty-cycle pulsed-CEST sequence with a flip angle of 360°, termed 2π-CEST, to reduce the contribution from APT.

METHODS

Simulations were used to evaluate the capability of the 2π-CEST to reduce APT. Experiments on animal tumor models were performed to show its advantages compared with the conventional asymmetric analysis. Samples of reconstituted phospholipids and proteins were used to evaluate the molecular origin of this NOE.

RESULTS

The 2π-CEST has reduced contribution from APT. In tumors where we show that the NOE is comparable to the APT effect, reducing the contamination from APT is crucial. The results show that the NOE signal obtained with 2π-CEST in tumor regions appears more homogeneous than that obtained with the conventional method. The phantom study showed that both phospholipids and proteins contribute to the NOE at -3.5 ppm.

CONCLUSION

The NOE at -3.5 ppm has a different contrast mechanism from APT and other CEST/NOE effects. The proposed 2π-CEST is more accurate than the conventional asymmetric analysis in detecting NOE, and requires much less scan time than the multiple-pool Lorentzian fit.

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

PURPOSE

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.

METHODS

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.

RESULTS

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 < 0.05) and ischemic tissue (-5.53% ± 0.68% (apparent) vs. -5.94% ± 0.73% (QUASS), P < 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 (-1.6 parts per million) effects.

CONCLUSION

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.

Dynamic alteration in myocardium creatine during acute infarction using MR CEST imaging

Creatine (Cr) is an essential metabolite in the creatine kinase reaction, which plays a critical role in maintaining normal cardiac function. Chemical exchange saturation transfer (CEST) MRI offers a novel way to map myocardium Cr. This study aims to investigate the dynamic alteration in myocardium Cr during acute infarction using CEST MRI, which may facilitate understanding of the heart remodeling mechanism at the molecular level. Seven adult Bama pigs underwent cardiac cine, Cr CEST, and late gadolinium-enhanced (LGE) T₁ -weighted (T₁ w) imaging three and 14 days after myocardial infarction induction on a 3 T scanner. Cardiac structural and functional indices, including myocardium mass (MM), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF), were measured from cines. Infarct angle was determined from LGE T₁ w images, based on which myocardium was classified into infarct, adjacent, and remote regions. Cr-weighted CEST signal was quantified from a three-pool Lorentzian fitting model and measured within each region and the entire myocardium. Student's t-test was conducted to evaluate any significant differences in measurements between the two time points. Correlation was assessed with Pearson correlation. P values less than 0.05 were considered statistically significant. Over the studied period, MM, EDV, and ESV did not alter significantly (P > 0.05), whereas significant increases of SV and EF and decrease of infarct angle were observed (P < 0.05). Meanwhile, the Cr-weighted CEST signal elevated significantly on Day 14 compared with Day 3 in the infarct (10.00 ± 1.28% versus 6.91 ± 1.54%, P < 0.01), adjacent (11.17 ± 2.00% versus 8.01 ± 1.58%, P = 0.01), and entire myocardium (11.03 ± 1.36% versus 8.19 ± 1.28%, P < 0.01). Moderate negative correlations were shown between the infarct angle and Cr-weighted CEST signals in the infarct (r = -0.80, P < 0.001), adjacent (r = -0.58, P = 0.03), and entire myocardium (r = -0.76, P < 0.01). In conclusion, the dynamic increase of myocardium Cr during acute infarction may interact with cardiac structural and functional recovery. The study provides supplementary insights into the heart remodeling process from the metabolic viewpoint.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Demonstration of fast and equilibrium human muscle creatine CEST imaging at 3 T

PURPOSE

Creatine chemical exchange saturation transfer (CrCEST) MRI is used increasingly in muscle imaging. However, the CrCEST measurement depends on the RF saturation duration (Ts) and relaxation delay (Td), and it is challenging to compare the results of different scan parameters. Therefore, this study aims to evaluate the quasi-steady-state (QUASS) CrCEST MRI on clinical 3T scanners.

METHODS

T₁ and CEST MRI scans of Ts/Td of 1 s/1 s and 2 s/2 s were obtained from a multi-compartment creatine phantom and 5 healthy volunteers. The CrCEST effect was quantified with asymmetry analysis in the phantom, whereas 5-pool Lorentzian fitting was applied to isolate creatine from phosphocreatine, amide proton transfer, combined magnetization transfer and nuclear Overhauser enhancement effects, and direct water saturation in four major muscle groups of the lower leg. The routine and QUASS CrCEST measurements were compared under two different imaging conditions. Paired Student's t-test was performed with p-values less than 0.05 considered statistically significant.

RESULTS

The phantom study showed a substantial influence of Ts/Td on the routine CrCEST quantification (p = 0.02), and such impact was mitigated with the QUASS algorithm (p = 0.20). The volunteer experiment showed that the routine CrCEST, amide proton transfer, and combined magnetization transfer and nuclear Overhauser enhancement effects increased significantly with Ts and Td (p < 0.05) and were significantly smaller than the corresponding QUASS indices (p < 0.01). In comparison, the QUASS CrCEST MRI showed little dependence on Ts and Td, indicating its robustness and accuracy.

CONCLUSION

The QUASS CrCEST MRI is feasible to provide fast and accurate muscle creatine imaging.

MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification

Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data.

Stable and inert macrocyclic cobalt(II) and nickel(II) complexes with paraCEST response

We report the synthesis of the macrocyclic ligands 3,9-PC2AMH (2,2'-(3,6,9-triaza-1(2,6)-pyridinacyclodecaphane-3,9-diyl)diacetamide) and 3,9-PC2AMtBu (2,2'-(3,6,9-triaza-1(2,6)-pyridinacyclodecaphane-3,9-diyl)bis(N-tert-butyl)acetamide) which contain a pyclen platform functionalized with acetamide or tert-butylacetamide pendant arms at positions 3 and 9 of the macrocyclic unit. The corresponding Co(II) and Ni(II) complexes were prepared, isolated and characterised as potential paramagnetic chemical exchange saturation transfer (paraCEST) agents. The X-ray structures of the Ni(II) complexes reveal six-coordination of the ligands to the metal ion. The Co(II) complex with 3,9-PC2AMtBu shows a similar six-coordinate structure in the solid state, while the Co(II) complex with 3,9-PC2AMH contains a seven-coordinate metal ion, seventh coordination being completed by the presence of an inner-sphere water molecule. The structure of the Co(II) complexes was investigated using ¹H NMR spectroscopy and computational methods. The complexes present a seven-coordinate structure in solution, as demonstrated by the analysis of the paramagnetic shifts using density functional theory. Ligand protonation constants and stability constants of the complexes with 3,9-PC2AMH were determined using potentiometric titrations (I = 0,15 M NaCl). The Co(II) complex was found to be more stable than the Ni(II) analogue (log K_CoL = 14.46(5) and log K_NiL = 13.15(3)). However, the Ni(II) and Co(II) complexes display similar rate constants characterizing the proton-assisted dissociation mechanism. The presence of highly shifted ¹H NMR signals due to the amide protons in slow exchange with bulk water results in sizeable CEST signals, which are observed at +67 and +15 ppm for the Co(II) complex with 3,9-PC2AMH and +42 and +7 ppm for the Ni(II) analogue at 25 °C.

Fast and equilibrium CEST imaging of brain tumor patients at 3T

Chemical exchange saturation transfer (CEST) MRI, versatile for detecting endogenous mobile proteins and tissue pH, has proved valuable in tumor imaging. However, CEST MRI scans are often performed under non-equilibrium conditions, which confound tissue characterization. This study proposed a quasi-steady-state (QUASS) CEST MRI algorithm to standardize fast and accurate tumor imaging at 3 T. The CEST signal evolution was modeled by longitudinal relaxation rate during relaxation delay (Td) and spinlock relaxation during RF saturation time (Ts), from which the QUASS CEST effect is derived. Numerical simulation and human MR imaging experiments (7 healthy volunteers and 19 tumor patients) were conducted at 3 T to compare the CEST measurements obtained under two representative experimental conditions. In addition, amide proton transfer (APT), combined magnetization transfer (MT) and nuclear overhauser enhancement (NOE) effects, and direct water saturation were isolated using a 3-pool Lorentzian fitting in white matter and gray matter of healthy volunteers and for patients in the contralateral normal-appearing white matter and tumor regions. Finally, the student's t-test was performed between conventional and QUASS CEST measurements. The routine APT and combined MT & NOE measures significantly varied with Ts and Td (P < .001) and were significantly smaller than the corresponding QUASS indices (P < .001). In contrast, the results from the QUASS reconstruction showed little dependence on the scan protocol (P > .05), indicating the accuracy and robustness of QUASS CEST MRI for tumor imaging. To summarize, the QUASS CEST reconstruction algorithm enables fast and accurate tumor CEST imaging at 3 T, promising to expedite and standardize clinical CEST MRI.

Demonstration of fast multi-slice quasi-steady-state chemical exchange saturation transfer (QUASS CEST) human brain imaging at 3T

PURPOSE

To combine multi-slice chemical exchange saturation transfer (CEST) imaging with quasi-steady-state (QUASS) processing and demonstrate the feasibility of fast QUASS CEST MRI at 3T.

METHODS

Fast multi-slice echo planar imaging (EPI) CEST imaging was developed with concatenated slice acquisition after single radiofrequency irradiation. The multi-slice CEST signal evolution was described by the spin-lock relaxation during saturation duration (T_s ) and longitudinal relaxation during the relaxation delay time (T_d ) and post-label delay (PLD), from which the QUASS CEST was generalized to fast multi-slice acquisition. In addition, numerical simulations, phantom, and normal human subjects scans were performed to compare the conventional apparent and QUASS CEST measurements with different T_s , T_d, and PLD.

RESULTS

The numerical simulation showed that the apparent CEST effect strongly depends on T_s , T_d , and PLD, while the QUASS CEST algorithm minimizes such dependences. In the L-carnosine gel phantom, the proposed QUASS CEST effects (2.68 ± 0.12% [mean ± SD]) were higher than the apparent CEST effects (1.85 ± 0.26%, p < 5e-4). In the human brain imaging, Bland-Altman analysis bias of the proposed QUASS CEST effects was much smaller than the PLD-corrected apparent CEST effects (0.03% vs. -0.54%), indicating the proposed fast multi-slice CEST imaging is robust and accurate.

CONCLUSIONS

The QUASS processing enables fast multi-slice CEST imaging with minimal loss in the measurement of the CEST effect.

Metabolic Magnetic Resonance Imaging in Neuroimaging: Magnetic Resonance Spectroscopy, Sodium Magnetic Resonance Imaging and Chemical Exchange Saturation Transfer

Magnetic resonance (MR) is a powerful and versatile technique that offers much more beyond conventional anatomic imaging and has the potential of probing in vivo metabolism. Although MR spectroscopy (MRS) predates clinical MR imaging (MRI), its clinical application has been limited by technical and practical challenges. Other MR techniques actively being developed for in vivo metabolic imaging include sodium concentration imaging and chemical exchange saturation transfer. This article will review some of the practical aspects of MRS in neuroimaging, introduce sodium MRI and chemical exchange saturation transfer MRI, and highlight some of their emerging clinical applications.

Chemical exchange saturation transfer imaging for epilepsy secondary to tuberous sclerosis complex at 3 T: Optimization and analysis

The homeostasis of various metabolites is impaired in epilepsy secondary to the tuberous sclerosis complex (TSC). Chemical exchange saturation transfer (CEST) imaging is an emerging molecular MRI technique that can detect various metabolites and proteins in vivo. However, the role of CEST imaging for TSC-associated epilepsy has not been assessed. Here, we aim to investigate the feasibility of applying CEST imaging to TSC-associated epilepsy, optimize the CEST acquisition parameters, and provide an analysis method for exploring the dominant molecular contributors to the CEST signal measured. Nine TSC epilepsy patients were scanned on a 3-T MRI system. The CEST saturation frequencies were swept from -6 to 6 ppm with 12 different combinations of saturation power (4, 3, 2 and 1 μT) and duration (1000, 700 and 400 ms). Furthermore, a two-stage simulation method based on the seven-pool Bloch-McConnell model was proposed to assess the contribution of each exchangeable pool to the CEST signal in normal-appearing white matter and cortical tubers, which avoided the complexity and uncertainty of full Bloch-McConnell fitting. The results showed that under the optimal saturation duration of 1000 ms, the greatest contrast between tubers and normal tissues occurred around 3, 2.5, 1.75 and 3.5 ppm for B₁ of 4, 3, 2 and 1 μT, respectively. At the optimal frequency offsets, the CEST values of tubers were significantly higher than those in the normal brain tissues (P < 0.01). Furthermore, the two-stage analysis suggested that the amine pool played a dominant role in yielding the contrast between cortical tubers and normal tissues. These results indicate that CEST MRI may serve as a potentially useful tool for identifying tubers in TSC, and the two-stage analysis method may provide a route for investigating the molecular contributions to the CEST contrast in biological tissues.

Chemical exchange saturation transfer for detection of antiretroviral drugs in brain tissue

OBJECTIVE

Antiretroviral drug theranostics facilitates the monitoring of biodistribution and efficacy of therapies designed to target HIV type-1 (HIV-1) reservoirs. To this end, we have now deployed intrinsic drug chemical exchange saturation transfer (CEST) contrasts to detect antiretroviral drugs within the central nervous system (CNS).

DESIGN AND METHODS

CEST effects for lamivudine (3TC) and emtricitabine (FTC) were measured by asymmetric magnetization transfer ratio analyses. The biodistribution of 3TC in different brain sub-regions of C57BL/6 mice treated with lipopolysaccharides was determined using MRI. CEST effects of 3TC protons were quantitated by Lorentzian fitting analysis. 3TC levels in plasma and brain regions were measured using ultraperformance liquid chromatography tandem mass spectrometry to affirm the CEST test results.

RESULTS

CEST effects of the hydroxyl and amino protons in 3TC and FTC linearly correlated to drug concentrations. 3TC was successfully detected in vivo in brain sub-regions by MRI. The imaging results were validated by measurements of CNS drug concentrations.

CONCLUSION

CEST contrasts can be used to detect antiretroviral drugs using MRI. Such detection can be used to assess spatial--temporal drug biodistribution. This is most notable within the CNS where drug biodistribution may be more limited with the final goal of better understanding antiretroviral drug-associated efficacy and potential toxicity.

Analysis Protocol for the Quantification of Renal pH Using Chemical Exchange Saturation Transfer (CEST) MRI

The kidney plays a major role in maintaining body pH homeostasis. Renal pH, in particular, changes immediately following injuries such as intoxication and ischemia, making pH an early biomarker for kidney injury before the symptom onset and complementary to well-established laboratory tests. Because of this, it is imperative to develop minimally invasive renal pH imaging exams and test pH as a new diagnostic biomarker in animal models of kidney injury before clinical translation. Briefly, iodinated contrast agents approved by the US Food and Drug Administration (FDA) for computed tomography (CT) have demonstrated promise as novel chemical exchange saturation transfer (CEST) MRI agents for pH-sensitive imaging. The generalized ratiometric iopamidol CEST MRI analysis enables concentration-independent pH measurement, which simplifies in vivo renal pH mapping. This chapter describes quantitative CEST MRI analysis for preclinical renal pH mapping, and their application in rodents, including normal conditions and acute kidney injury.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.

Preliminary demonstration of in vivo quasi-steady-state CEST postprocessing-Correction of saturation time and relaxation delay for robust quantification of tumor MT and APT effects

PURPOSE

Chemical exchange saturation transfer (CEST) MRI is versatile for measuring the dilute labile protons and microenvironment properties. However, the use of insufficiently long RF saturation duration (Ts) and relaxation delay (Td) may underestimate the CEST measurement. This study proposed a quasi-steady-state (QUASS) CEST analysis for robust CEST quantification.

METHODS

The CEST signal evolution was modeled as a function of the longitudinal relaxation rate during Td and spin-lock relaxation rate during Ts, from which the QUASS-CEST effect is derived. Numerical simulation and in vivo rat glioma MRI experiments were conducted at 11.7 T to compare the apparent and QUASS-CEST results obtained under different Ts/Td of 2 seconds/2 seconds and 4 seconds/4 seconds. Magnetization transfer and amide proton transfer effects were resolved using a multipool Lorentzian fitting and evaluated in contralateral normal tissue and tumor regions.

RESULTS

The simulation showed the dependence of the apparent CEST effect on Ts and Td, and such reliance was mitigated with the QUASS algorithm. Animal experiment results showed that the apparent magnetization transfer and amide proton transfer effects and their contrast between contralateral normal tissue and tumor regions increased substantially with Ts and Td. In comparison, the QUASS magnetization transfer and amide proton transfer effects and their difference between contralateral normal tissue and tumor exhibited little dependence on Ts and Td. In addition, the apparent magnetization transfer and amide proton transfer were significantly smaller than the corresponding QUASS indices (P < .05).

CONCLUSION

The QUASS-CEST algorithm enables robust CEST quantification and offers a straightforward approach to standardize CEST experiments.

Quasi-steady-state CEST (QUASS CEST) solution improves the accuracy of CEST quantification: QUASS CEST MRI-based omega plot analysis

PURPOSE

CEST MRI omega plot quantifies the labile proton fraction ratio (f_r ) and exchange rate (k_sw ), yet it assumes long RF saturation time (Ts) and relaxation delay (Td). Our study aimed to test if a quasi-steady-state (QUASS) CEST analysis that accounts for the effect of finite Ts and Td could improve the accuracy of CEST MRI quantification.

METHODS

We modeled the MRI signal evolution using a typical CEST EPI sequence. The signal relaxes toward its thermal equilibrium following the bulk water relaxation rate during Td, and then toward its CEST steady state following the spin-lock relaxation rate during Ts from which the QUASS CEST effect is derived. Both f_r and k_sw were solved from simulated conventional apparent CEST and QUASS CEST MRI. We also performed MRI experiments from a Cr-gel phantom under serially varied Ts and Td times from 1.5 to 7.5 s.

RESULTS

Simulation showed that, although k_sw could be slightly overestimated (3%-15%) for the range of Ts and Td, f_r could be substantially underestimated by as much as 67%. In contrast, the QUASS solution provided accurate k_sw and f_r determination within 2%. The CEST MRI experiments confirmed that the QUASS solution enabled robust quantification of k_sw and f_r , superior over the omega plot analysis based on the conventional apparent CEST MRI measurements.

CONCLUSIONS

The QUASS CEST MRI algorithm corrects the effect of finite Ts and Td times on CEST measurements, thereby allowing robust and accurate CEST quantification.

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

OBJECT

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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

Evaluation of a similarity anisotropic diffusion denoising approach for improving in vivo CEST-MRI tumor pH imaging

PURPOSE

Chemical exchange saturation transfer MRI provides new approaches for investigating tumor microenvironment, including tumor acidosis that plays a key role in tumor progression and resistance to therapy. Following iopamidol injection, the detection of the contrast agent inside the tumor tissue allows measurements of tumor extracellular pH. However, accurate tumor pH quantifications are hampered by the low contrast efficiency of the CEST technique and by the low SNR of the acquired CEST images, hence in a reduced detectability of the injected agent. This work aims to investigate a novel denoising method for improving both tumor pH quantification and accuracy of CEST-MRI pH imaging.

METHODS

An hybrid denoising approach was investigated for CEST-MRI pH imaging based on the combination of the nonlocal mean filter and the anisotropic diffusion tensor method. The denoising approach was tested in simulated and in vitro data and compared with previously reported methods for CEST imaging and with established denoising approaches. Finally, it was validated with in vivo data to improve the accuracy of tumor pH maps.

RESULTS

The proposed method outperforms current denoising methods in CEST contrast quantification and detection of the administered contrast agent at several increasing noise levels with simulated data. In addition, it achieved a better pH quantification in in vitro data and demonstrated a marked improvement in contrast detection and a substantial improvement in tumor pH accuracy in in vivo data.

CONCLUSION

The proposed approach effectively reduces the noise in CEST images and increases the sensitivity detection in CEST-MRI pH imaging.

Quasi-steady state chemical exchange saturation transfer (QUASS CEST) analysis-correction of the finite relaxation delay and saturation time for robust CEST measurement

PURPOSE

CEST provides a MR contrast mechanism sensitizing to the exchange between dilute labile and bulk water protons. However, the CEST effect depends on the RF saturation duration and relaxation delay, which need to be long to reach its steady state. Our study aims to estimate the QUAsi-Steady State (QUASS) CEST signal from experiments with shorter saturation and relaxation delay times.

METHODS

The evolution of the CEST signal was modeled as a function of the bulk water longitudinal relaxation rate during the relaxation delay (Td) and spin-lock relaxation rate during the RF saturation (Ts), from which the QUASS CEST effect is solved. Numeric simulations were programmed to compare the apparent CEST and QUASS CEST effects as a function of Ts and Td times. We also performed CEST MRI experiments from a creatine-gel pH phantom under serially varied Ts and Td times.

RESULTS

The numeric simulation showed that although the apparent CEST effect depends on Td and Ts, the QUASS CEST solution has little dependence. Phantom results showed that the routine CEST pH contrast could be described by a nonlinear regression model (ie, Δ C E S T R = Δ C E S T R eq app 1 - e - R 1 ρ app · t ). We had Δ C E S T R eq app = 3.90 ± 0.03 % (P < 5e-8) and R 1 ρ app = 0.62 ± 0.02 s - 1 (P < 5e-6). For the QUASS CEST analysis, we modeled the pH contrast as Δ C E S T R = Δ C E S T R eq QUASS + s · t , using a linear regression model. We had Δ C E S T R eq QUASS = 3.63 ± 0.01 % (P < 5e-9) and s = - 0.02 ± 0.00 % / s (P < 0.01), the slope of which is minimal.

CONCLUSIONS

The QUASS CEST algorithm provides a post-processing solution that facilitates robust CEST measurement.

Brain pH Imaging and its Applications

Acid-base homeostasis and pH regulation are critical for normal tissue metabolism and physiology, and brain tissue pH alters in many diseased states. Several noninvasive tissue pH Magnetic Resonance (MR) techniques have been developed over the past few decades to shed light on pH change during tissue function and dysfunction. Nevertheless, there are still challenges for mapping brain pH noninvasively at high spatiotemporal resolution. To address this unmet biomedical need, chemical exchange saturation transfer (CEST) MR techniques have been developed as a sensitive means for non-invasive pH mapping. This article briefly reviews the basic principles of different pH measurement techniques with a focus on CEST imaging of pH. Emerging pH imaging applications in the tumor are provided as examples throughout the narrative, and CEST pH imaging in acute stroke is discussed in the final section.

Investigating the origin of pH-sensitive magnetization transfer ratio asymmetry MRI contrast during the acute stroke: Correction of T1 change reveals the dominant amide proton transfer MRI signal

PURPOSE

Amide proton transfer (APT) MRI is promising to serve as a surrogate metabolic imaging biomarker of acute stroke. Although the magnetization transfer ratio asymmetry (MTRasym ) has been used commonly, the origin of pH-weighted MRI effect remains an area of investigation, including contributions from APT, semisolid MT contrast asymmetry, and nuclear Overhauser enhancement effects. Our study aimed to determine the origin of pH-weighted MTRasym contrast following acute stroke.

METHODS

Multiparametric MRI, including T1 , T2 , diffusion and Z-spectrum, were performed in rats after middle cerebral artery occlusion. We analyzed the conventional Z-spectrum I Δ ω I 0 and the apparent exchange spectrum R ex Δ ω , being the difference between the relaxation-scaled inverse Z-spectrum and the intrinsic spinlock relaxation rate R 1 · cos 2 θ · I 0 I Δ ω - R 1 ρ Δ ω . The ischemia-induced change was calculated as the spectral difference between the diffusion lesion and the contralateral normal area.

RESULTS

The conventional Z-spectrum signal change at -3.5 ppm dominates that at +3.5 ppm (-1.16 ± 0.39% vs. 0.76 ± 0.26%, P < .01) following acute stroke. In comparison, the magnitude of ΔRex change at 3.5 ppm becomes significantly larger than that at -3.5 ppm (-2.80 ± 0.40% vs. -0.94 ± 0.80%, P < .001), with their SNR being 7.0 and 1.2, respectively. We extended the magnetization transfer and relaxation normalized APT concept to the apparent exchange-dependent relaxation image, documenting an enhanced pH contrast between the ischemic lesion and the intact tissue, over that of MTRasym .

CONCLUSION

Our study shows that after the relaxation-effect correction, the APT effect is the dominant contributing factor to pH-weighted MTRasym following acute stroke.