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

PurposeTo 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.MethodsWe 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.ResultsWhen 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.ConclusionThe 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.

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of the molecular origin of amide proton transfer–weighted imaging

Sun,

Zhao,

2023

“…In contrast, the NOE(−3.5) signal, which has a relatively narrower line width compared to MT, is attributed to the dipolar interactions between mobile macromolecules, including mobile proteins and membrane lipid aliphatic chains and water protons. 31,46 The NOE(−1.6), which has a much narrower line width compared to NOE(−3.5), originates from dipolar interactions between protons of the choline (Cho) head group in phospholipids and water protons. Unlike free Cho with less restricted mobility, the Cho head group in phospholipids is in a more restricted state that modulates the dipolar interactions with water.…”

Section: Introductionmentioning

confidence: 99%

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

Viswanathan,

Kurmi,

2023

PurposeA 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.MethodsRats 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.ResultsThe 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.ConclusionThis 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.

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

Viswanathan,

Yin,

Kurmi

et al. 2023

Self Cite

PurposeMachine 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.MethodsPartially 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.ResultsExperiments 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.ConclusionPartially synthetic CEST data can address the challenges in conventional ML methods.