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.

Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging.

Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.

Tumor therapy by targeting extracellular hydroxyapatite using novel drugs: A paradigm shift.

BACKGROUND: It has been shown that tumor microenvironment (TME) hydroxyapatite (HAP) is typically associated with many malignancies and plays a role in tumor progression and growth. Additionally, acidosis in the TME has been reported to play a key role in selecting for a more aggressive tumor phenotype, drug resistance and desensitization to immunotherapy for many types of cancers. TME-HAP is an attractive target for tumor detection and treatment development since HAP is generally absent from normal soft tissue. We provide strong evidence that dissolution of hydroxyapatite (HAP) within the tumor microenvironment (TME-HAP) using a novel therapeutic can be used to kill cancer cells both in vitro and in vivo with minimal adverse effects. METHODS: We developed an injectable cation exchange nano particulate sulfonated polystyrene solution (NSPS) that we engineered to dissolve TME-HAP, inducing localized acute alkalosis and inhibition of tumor growth and glucose metabolism. This was evaluated in cell culture using 4T1, MDA-MB-231 triple negative breast cancer cells, MCF10 normal breast cells, and H292 lung cancer cells, and in vivo using orthotopic mouse models of cancer that contained detectable microenvironment HAP including breast (MMTV-Neu, 4T1, and MDA-MB-231), prostate (PC3) and colon (HCA7) cancer using 18 F-NaF for HAP and 18 F-FDG for glucose metabolism with PET imaging. On the other hand, H292 lung tumor cells that lacked detectable microenvironment HAP and MCF10a normal breast cells that do not produce HAP served as negative controls. Tumor microenvironment pH levels following injection of NSPS were evaluated via Chemical Exchange Saturation (CEST) MRI and via ex vivo methods. RESULTS: Within 24 h of adding the small concentration of 1X of NSPS (~7 µM), we observed significant tumor cell death (~ 10%, p < 0.05) in 4T1 and MDA-MB-231 cell cultures that contain HAP but ⟨2% in H292 and MCF10a cells that lack detectable HAP and in controls. Using CEST MRI, we found extracellular pH (pHe) in the 4T1 breast tumors, located in the mammary fat pad, to increase by nearly 10% from baseline before gradually receding back to baseline during the first hour post NSPS administration. in the tumors that contained TME-HAP in mouse models, MMTV-Neu, 4T1, and MDA-MB-231, PC3, and HCA7, there was a significant reduction (p<0.05) in 18 F-Na Fuptake post NSPS treatment as expected; 18 F- uptake in the tumor = 3.8 ± 0.5 %ID/g (percent of the injected dose per gram) at baseline compared to 1.8 ±0.5 %ID/g following one-time treatment with 100 mg/kg NSPS. Of similar importance, is that 18 F-FDG uptake in the tumors was reduced by more than 75% compared to baseline within 24 h of treatment with one-time NSPS which persisted for at least one week. Additionally, tumor growth was significantly slower (p < 0.05) in the mice treated with one-time NSPS. Toxicity showed no evidence of any adverse effects, a finding attributed to the absence of HAP in normal soft tissue and to our therapeutic NSPS having limited penetration to access HAP within skeletal bone. CONCLUSION: Dissolution of TME-HAP using our novel NSPS has the potential to provide a new treatment paradigm to enhance the management of cancer patients with poor prognosis.

The missing third dimension-Functional correlations of BOLD signals incorporating white matter.

Correlations between magnetic resonance imaging (MRI) blood oxygenation level-dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.

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.

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.

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.

Nuclear Overhauser enhancement imaging at -1.6 ppm in rat brain at 4.7T.

PURPOSE: A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer signal at around -1.6 ppm, termed NOE(-1.6), has been reported at high fields of 7T and 9.4T previously. This study aims to validate the presence of this signal at a relatively low field of 4.7T and evaluate its variations in different brain regions and tumors. METHODS: Rats were injected with monocrystalline iron oxide nanoparticles to reduce the NOE(-1.6) signal. CEST signals were measured using different saturation powers before and after injection to assess the presence of this signal. Multiple-pool Lorentzian fits, with/without inclusion of the NOE(-1.6) pool, were performed on CEST Z-spectra obtained from healthy rat brains and rats with 9L tumors. These fits aimed to further validate the presence of the NOE(-1.6) signal and quantify its amplitude. RESULTS: The NOE(-1.6) signal exhibited a dramatic change following the injection of monocrystalline iron oxide nanoparticles, confirming its presence at 4.7T. The NOE(-1.6) signal reached its peak at a saturation power of ∼0.75 µT, indicating an optimized power level. The multiple-pool Lorentzian fit without the NOE(-1.6) pool showed higher residuals around -1.6 ppm compared to the fit with this pool, further supporting the presence of this signal. The NOE(-1.6) signal did not exhibit significant variation in the corpus callosum and caudate putamen regions, but it showed a significant decrease in tumors, which aligns with previous findings at 9.4T. CONCLUSION: This study successfully demonstrated the presence of the NOE(-1.6) signal at 4.7T, which provides valuable insights into its potential applications at lower field strengths.

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.

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.

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 on ω 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.

Correction of errors in estimates of T1ρ at low spin-lock amplitudes in the presence of B0 and B1 inhomogeneities.

Relaxation rates R1ρ in the rotating frame measured by spin-lock methods at very low locking amplitudes (≤ 100 Hz) are sensitive to the effects of water diffusion in intrinsic gradients and may provide information on tissue microvasculature, but accurate estimates are challenging in the presence of B0 and B1 inhomogeneities. Although composite pulse preparations have been developed to compensate for nonuniform fields, the transverse magnetization comprises different components and the spin-lock signals measured do not decay exponentially as a function of locking interval at low locking amplitudes. For example, during a typical preparation sequence, some of the magnetization in the transverse plane is nutated to the Z-axis and later tipped back, and so does not experience R1ρ relaxation. As a result, if the spin-lock signals are fit to a monoexponential decay with locking interval, there are residual errors in quantitative estimates of relaxation rates R1ρ and their dispersion with weak locking fields. We developed an approximate theoretical analysis to model the behaviors of the different components of the magnetization, which provides a means to correct these errors. The performance of this correction approach was evaluated both through numerical simulations and on human brain images at 3 T, and compared with a previous correction method using matrix multiplication. Our correction approach has better performance than the previous method at low locking amplitudes. Through careful shimming, the correction approach can be applied in studies using low spin-lock amplitudes to assess the contribution of diffusion to R1ρ dispersion and to derive estimates of microvascular sizes and spacings. The results of imaging eight healthy subjects suggest that R1ρ dispersion in human brain at low locking fields arises from diffusion among inhomogeneities that generate intrinsic gradients on a scale of capillaries (~7.4 ± 0.5 µm).

Severity of polycystic kidney disease revealed by multiparametric MRI.

PURPOSE: We aimed to compare multiple MRI parameters, including relaxation rates ( R 1 $$ {R}_1 $$ , R 2 $$ {R}_2 $$ , and R 1 ρ $$ {R}_{1\rho } $$ ), ADC from diffusion weighted imaging, pool size ratio (PSR) from quantitative magnetization transfer, and measures of exchange from spin-lock imaging ( S ρ $$ {S}_{\rho } $$ ), for assessing and predicting the severity of polycystic kidney disease (PKD) over time. METHODS: Pcy/Pcy mice with CD1 strain, a mouse model of autosomal dominant PKD, were imaged at 5, 9, and 26 wk of age using a 7T MRI system. Twelve-week normal CD1 mice were used as controls. Post-mortem paraffin tissue sections were stained using hematoxylin and eosin and picrosirius red to identify histological changes. RESULTS: Histology detected segmental cyst formation in the early stage (week 5) and progression of PKD over time in Pcy kidneys. In T 2 $$ {T}_2 $$ -weighted images, small cysts appeared locally in cystic kidneys in week 5 and gradually extended to the whole cortex and outer stripe of outer medulla region from week 5 to week 26. Regional PSR, R 1 $$ {R}_1 $$ , R 2 $$ {R}_2 $$ , and R 1 ρ $$ {R}_{1\rho } $$ decreased consistently over time compared to normal kidneys, with significant changes detected in week 5. Among all the MRI measures, R 2 $$ {R}_2 $$ and R 1 ρ $$ {R}_{1\rho } $$ allow highest detectability to PKD, while PSR and R 1 $$ {R}_1 $$ have highest correlation with pathological indices of PKD. Using optimum MRI parameters as regressors, multiple linear regression provides reliable prediction of PKD progression. CONCLUSION: R 2 $$ {R}_2 $$ , R 1 $$ {R}_1 $$ , and PSR are sensitive indicators of the presence of PKD. Multiparametric MRI allows a comprehensive analysis of renal changes caused by cyst formation and expansion.

R1ρ dispersion in white matter correlates with quantitative metrics of cognitive impairment.

Much previous neuroimaging research in Alzheimer's disease has focused on the roles of amyloid and tau proteins, but recent studies have implicated microvascular changes in white matter as early indicators of damage related to later dementia. We used MRI to derive novel, non-invasive measurements of R1ρ dispersion using different locking fields to characterize variations of microvascular structure and integrity in brain tissues. We developed a non-invasive 3D R1ρ dispersion imaging technique using different locking fields at 3T. We acquired MR images and cognitive assessments of participants with mild cognitive impairment (MCI) and compared them to age-matched healthy controls in a cross-sectional study. After providing informed consent, 40 adults aged 62 to 82 years (n = 17 MCI) were included in this study. White matter ΔR1ρ-fraction measured by R1ρ dispersion imaging showed a strong correlation with the cognitive status of older adults (ßstd = -0.4, p-value < 0.01) independent of age, in contrast to other conventional MRI markers such as T2, R1ρ, and white matter hyperintense lesion volume (WMHs) measured with T2-FLAIR. The correlation of WMHs with cognitive status was no longer significant after adjusting for age and sex in linear regression analysis, and the size of the regression coefficient was substantially decreased (53% lower). This work establishes a new non-invasive method that potentially characterizes impairment of the microvascular structure of white matter in MCI patients compared to healthy controls. The application of this method in longitudinal studies would improve our fundamental understanding of the pathophysiologic changes that accompany abnormal cognitive decline with aging and help identify potential targets for treatment of Alzheimer's disease.

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.

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.

Towards differentiation of brain tumor from radiation necrosis using multi-parametric MRI: Preliminary results at 4.7 T using rodent models.

BACKGROUND: It remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting. METHODS: Multi-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T2 (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow). RESULTS: For single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions. CONCLUSION: A general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis.