Bloch-McConnell Equations Research Articles

Synthesis of higher-B0 CEST Z-spectra from lower-B0 data via deep learning and singular value decomposition.

Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B0) allow for better separation of Z-spectral "peaks," aiding signal interpretation and quantification. However, data acquisition at higher B0 is restricted by equipment access, field inhomogeneity and safety issues. Herein, we aim to synthesize higher-B0 Z-spectra from readily available data acquired with 3 T clinical scanners using a deep learning framework. Trained with simulation data using models based on Bloch-McConnell equations, this framework comprised two deep neural networks (DNNs) and a singular value decomposition (SVD) module. The first DNN identified B0 shifts in Z-spectra and aligned them to correct frequencies. After B0 correction, the lower-B0 Z-spectra were streamlined to the second DNN, casting into the key feature representations of higher-B0 Z-spectra, obtained through SVD truncation. Finally, the complete higher-B0 Z-spectra were recovered from inverse SVD, given the low-rank property of Z-spectra. This study constructed and validated two models, a phosphocreatine (PCr) model and a pseudo-in-vivo one. Each experimental dataset, including PCr phantoms, egg white phantoms, and in vivo rat brains, was sequentially acquired on a 3 T human and a 9.4 T animal scanner. Results demonstrated that the synthetic 9.4 T Z-spectra were almost identical to the experimental ground truth, showing low RMSE (0.11% ± 0.0013% for seven PCr tubes, 1.8% ± 0.2% for three egg white tubes, and 0.79% ± 0.54% for three rat slices) and high R2 (>0.99). The synthesized amide and NOE contrast maps, calculated using the Lorentzian difference, were also well matched with the experiments. Additionally, the synthesis model exhibited robustness to B0 inhomogeneities, noise, and other acquisition imperfections. In conclusion, the proposed framework enables synthesis of higher-B0 Z-spectra from lower-B0 ones, which may facilitate CEST MRI quantification and applications.

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

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

Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging.

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.

Comparison of model-free Lorentzian and spinlock model-based fittings in quantitative CEST imaging ofacutestroke.

CEST MRI detects complex tissue changes following acute stroke. Our study aimed to test if spinlock model-based fitting of the quasi-steady-state (QUASS)-reconstructed equilibrium CEST MRI improves the determination of multi-pool signal changes over the commonly-used model-free Lorentzian fitting in acute stroke. Multiple three-pool CEST Z-spectra were simulated using Bloch-McConnell equations for a range of T1 , relaxation delay, and saturation times. The multi-pool CEST signals were solved from the simulated Z-spectra to test the accuracy of routine Lorentzian (model-free) and spinlock (model-based) fittings without and with QUASS reconstruction. In addition, multiparametric MRI scans were obtained in rat models of acute stroke, including relaxation, diffusion, and CEST Z-spectrum. Finally, we compared model-free and model-based per-pixel CEST quantification in vivo. The spinlock model-based fitting of QUASS CEST MRI provided a nearly T1 -independent determination of multi-pool CEST signals, advantageous over the fittings of apparent CEST MRI (model-free and model-based). In vivo data also demonstrated that the spinlock model-based QUASS fitting captured significantly different changes in semisolid magnetization transfer (-0.9 ± 0.8 vs. 0.3 ± 0.8%), amide (-1.1 ± 0.4 vs. -0.5 ± 0.2%), and guanidyl (1.0 ± 0.4 vs. 0.7 ± 0.3%) signals over the model-free Lorentzian analysis. Our study demonstrated that spinlock model-based fitting of QUASS CEST MRI improved the determination of the underlying tissue changes following acute stroke, promising further clinical translation of quantitative CEST imaging.

Numerical fitting of Extrapolated semisolid Magnetization transfer Reference (NEMR) signals: Improved detection of ischemic stroke.

To propose a novel Numerical fitting method of the Extrapolated semisolid Magnetization transfer Reference (NEMR) signal for quantifying the CEST effect. Modified two-pool Bloch-McConnell equations were used to numerically fit the magnetization transfer (MT) and direct water saturation (DS) signals at far off-resonance frequencies, which was subsequently extrapolated into the frequency range of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) pools. Then the subtraction of the fitted two-pool z-spectrum and the experimentally acquired z-spectrum yielded APT# and NOE# signals mostly free of MT and DS contamination. Several strategies were used to accelerate the NEMR fitting. Furthermore, the proposed NEMR method was compared with the conventional extrapolated semisolid magnetization transfer reference (EMR) and magnetization transfer ratio asymmetry (MTRasym ) methods in simulations and stroke patients. The combination of RF downsampling, MT lineshape look-up table, and conversion of MATLAB code to C code accelerated the NEMR fitting by over 2700-fold. Monte-Carlo simulations showed that NEMR had higher accuracy than EMR and eliminated the requirement of the steady-state condition. In ischemic stroke patients, the NEMR maps at 1 μT removed hypointense artifacts seen on EMR and MTRasym images, and better depicted stroke lesions than EMR. For NEMR, NOE# yielded significantly (p < 0.05) stronger signal contrast between stroke and normal tissues than APT# at 1 μT. The proposed NEMR method is suitable for arbitrary saturation settings and can remove MT and DS contamination from the CEST signal for improved detection of ischemic stroke.

Adiabatically prepared spin-lock could reduce the R1ρ dispersion.

R1ρ (or spin-lock) imaging is prone to artifacts arising from field inhomogeneities that may impact the R1ρ quantification. Previous research has proposed two types of method to manage the artifacts in continuous-wave constant amplitude spin-lock, one is based on the composite block pulses to compensate for the field imperfections, another category uses adiabatic pulses in the R1ρ pre-pulse to excite and reverse the magnetization (named adiabatic prepared approach). Although both methods have proved their efficiency in alleviating artifacts, we observed that the adiabatic pulse approach could produce much lower R1ρ dispersion in human knee cartilage than the block pulse method (characterized by the R1ρ difference ∆R1ρ =11.4 Hz (from spin-lock field 50 to 500 Hz) for the block pulse method vs. ∆R1ρ =4.5 Hz for the adiabatic pulse approach). Prompted by this observation, the purpose of this study was to investigate the underlying factors that may affect the R1ρ dispersion through numerical simulations based on the two-pool exchanging Bloch-McConnell equations. The effects of free water pool size Pa (from 0.80 to 0.95), chemical exchange rate kb (from the bound to free water pool, ranged from 500 to 3,000 Hz), adiabatic pulse duration Tp (from 5.0 to 25 ms), and the chemical shift of the bound pool ppmb (from 1.0 to 5.0 ppm) were examined on the degree of the R1ρ dispersion for the two R1ρ imaging methods. In general, the greater the ppmb, kb, Tp, and the smaller Pa, the more significant difference in R1ρ dispersion between the block and adiabatic approaches, with the dispersion curve of the adiabatic method becoming flatter. The adiabatic prepared approach may compromise the R1ρ dispersion, the effect is determined by the combination of the tissue and radiofrequency (RF) pulse properties. It is suggested that care should be taken when using the adiabatically prepared approach to study R1ρ dispersion.

Site-based description of R1ρ relaxation in local reference frames

Nuclear magnetic spin relaxation in the presence of an applied radiofrequency field depends critically on chemical exchange processes that transfer nuclear spins between chemical or conformational environments with distinct resonance frequencies. Characterization of chemical exchange processes in R1ρ relaxation dispersion, CEST, and DEST experiments provides powerful insights into chemical and conformational kinetics of biological macromolecules. The present work reformulates expressions for magnetization evolution and the R1ρ relaxation rate constant by focussing on the orientations of the tilted rotating frames of reference for magnetization components in different sites, by treating the spin-locking field strength as a perturbation to free-precession evolution, and by applying the Homotopy Analysis and Laplace transform methods to approximate solutions to the Bloch-McConnell equations. The results provide an expression for R1ρ that is invariant to the topology of the kinetic scheme, an approximate equation for evolution of spin-locked z-magnetization, and an approach for effective simplification of chemical exchange topologies for 2- and N-site chemical exchange processes. The theoretical approach also provides an accurate approximation for relaxation during a constant-amplitude radiofrequency field in the absence of exchange.

Theory of chemical exchange saturation transfer MRI in the context of different magnetic fields.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a versatile MRI method that provides contrast based on the level of molecular and metabolic activity. This contrast arises from indirect measurement of protons in low concentration molecules that are exchanging with the abundant water proton pool. The indirect measurement is based on magnetization transfer of radio frequency (rf)-prepared magnetization from the small pool to the water pool. The signal can be modeled by the Bloch-McConnell equations combining standard magnetization dynamics and chemical exchange processes. In this article, we review analytical solutions of the Bloch-McConnell equations and especially the derived CEST signal equations and their implications. The analytical solutions give direct insight into the dependency of measurable CEST effects on underlying parameters such as the exchange rate and concentration of the solute pools, but also on the system parameters such as the rf irradiation field B1 , as well as the static magnetic field B0 . These theoretical field-strength dependencies and their influence on sequence design are highlighted herein. In vivo results of different groups making use of these field-strength benefits/dependencies are reviewed and discussed.

Myocardium Assessment by Relaxation along Fictitious Field, Extracellular Volume, Feature Tracking, and Myocardial Strain in Hypertensive Patients with Left Ventricular Hypertrophy.

Background Previous research has shown impaired global longitudinal strain (GLS) and slightly elevated extracellular volume fraction (ECV) in hypertensive patients with left ventricular hypertrophy (HTN LVH). Up to now, only little attention has been paid to interactions between macromolecules and free water in hypertrophied myocardium. Purpose To evaluate the feasibility of relaxation along a fictitious field with rank 2 (RAFF2) in HTN LVH patients. Study Type. Single institutional case control. Subjects 9 HTN LVH (age, 69 ± 10 years) and 11 control subjects (age, 54 ± 12 years). Field Strength/Sequence. Relaxation time mapping (T1, T1ρ, and TRAFF2 with 11.8 μT maximum radio frequency field amplitude) was performed at 1.5 T using a Siemens Aera (Erlangen, Germany) scanner equipped with an 18-channel body array coil. Assessment. ECV was calculated using pre- and postcontrast T1, and global strains parameters were assessed by Segment CMR (Medviso AB Co, Sweden). The parametric maps of T1ρ and TRAFF2 were computed using a monoexponential model, while the Bloch-McConnell equations were solved numerically to model effect of the chemical exchange during radio frequency pulses. Statistical Tests. Parametric maps were averaged over myocardium for each subject to be used in statistical analysis. Kolmogorov-Smirnov was used as the normality test followed by Student's t-test and Pearson's correlation to determine the difference between the HTN LVH patients and controls along with Hedges' g effect size and the association between variables, respectively. Results T RAFF2 decreased statistically (83 ± 2 ms vs 88 ± 6 ms, P < 0.031), and global longitudinal strain was impaired (GLS, −14 ± 3 vs − 18 ± 2, P < 0.002) in HTN LVH patients compared to the controls, respectively. Also, significant negative correlation was found between TRAFF2 and GLS (r = −0.53, P < 0.05). Data Conclusion. Our results suggest that TRAFF2 decrease in HTN LVH patients may be explained by gradual collagen accumulation which can be reflected in GLS changes. Most likely, it increases the water proton interactions and consequently decreases TRAFF2 before myocardial scarring.

Chemical Exchange Saturation Transfer for Lactate-Weighted Imaging at 3 T MRI: Comprehensive In Silico, In Vitro, In Situ, and In Vivo Evaluations.

Based on in silico, in vitro, in situ, and in vivo evaluations, this study aims to establish and optimize the chemical exchange saturation transfer (CEST) imaging of lactate (Lactate-CEST—LATEST). To this end, we optimized LATEST sequences using Bloch–McConnell simulations for optimal detection of lactate with a clinical 3 T MRI scanner. The optimized sequences were used to image variable lactate concentrations in vitro (using phantom measurements), in situ (using nine human cadaveric lower leg specimens), and in vivo (using four healthy volunteers after exertional exercise) that were then statistically analyzed using the non-parametric Friedman test and Kendall Tau-b rank correlation. Within the simulated Bloch–McConnell equations framework, the magnetization transfer ratio asymmetry (MTRasym) value was quantified as 0.4% in the lactate-specific range of 0.5–1 ppm, both in vitro and in situ, and served as the imaging surrogate of the lactate level. In situ, significant differences (p < 0.001) and strong correlations (τ = 0.67) were observed between the MTRasym values and standardized intra-muscular lactate concentrations. In vivo, a temporary increase in the MTRasym values was detected after exertional exercise. In this bench-to-bedside comprehensive feasibility study, different lactate concentrations were detected using an optimized LATEST imaging protocol in vitro, in situ, and in vivo at 3 T, which prospectively paves the way towards non-invasive quantification and monitoring of lactate levels across a broad spectrum of diseases.

Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady-state free precession MRI.

PurposeQuantitative magnetization transfer (qMT) imaging can be used to quantify the proportion of protons in a voxel attached to macromolecules. Here, we show that the original qMT balanced steady‐state free precession (bSSFP) model is biased due to over‐simplistic assumptions made in its derivation.Theory and MethodsWe present an improved model for qMT bSSFP, which incorporates finite radiofrequency (RF) pulse effects as well as simultaneous exchange and relaxation. Furthermore, a correction relating to finite RF pulse effects for sinc‐shaped excitations is derived. The new model is compared to the original one in numerical simulations of the Bloch‐McConnell equations and in previously acquired in vivo data.ResultsOur numerical simulations show that the original signal equation is significantly biased in typical brain tissue structures (by 7%‐20%), whereas the new signal equation outperforms the original one with minimal bias (<1%). It is further shown that the bias of the original model strongly affects the acquired qMT parameters in human brain structures, with differences in the clinically relevant parameter of pool‐size‐ratio of up to 31%. Particularly high biases of the original signal equation are expected in an MS lesion within diseased brain tissue (due to a low T2/T1‐ratio), demanding a more accurate model for clinical applications.ConclusionThe improved model for qMT bSSFP is recommended for accurate qMT parameter mapping in healthy and diseased brain tissue structures.

Quasi-steady-state chemical exchange saturation transfer (QUASS CEST) MRI analysis enables T1 normalized CEST quantification – Insight into T1 contribution to CEST measurement

Chemical exchange saturation transfer (CEST) MRI depends not only on the labile proton concentration and exchange rate but also on relaxation rates, particularly T1 relaxation time. However, T1 normalization has shown to be not straightforward under non-steady-state conditions and in the presence of radiofrequency spillover effect. Our study aimed to test if the combined use of the new quasi-steady-state (QUASS) analysis and inverse CEST calculation facilitates T1 normalization for improved CEST quantification. The CEST signal was simulated with Bloch-McConnell equations, and the apparent CEST, QUASS CEST, and the inverse CEST effects were calculated. T1-normalized CEST effects were tested for their specificity to the underlying CEST system (i.e., labile proton ratio and exchange rate). CEST experiments were performed from a 9-vial phantom of independently varied concentrations of creatine (20, 40, and 60 mM) and manganese chloride (20, 30, and 40 µM) under a range of RF saturation amplitudes (0.5–4 µT) and durations (1–4 s). The simulation showed that while T1 normalization of the apparent CEST effect was subject to noticeable T1 contamination, the T1-normalized inverse QUASS CEST effect had little T1 dependence. The experimental data were analyzed using a multiple linear regression model, showing that T1-normalized inverse QUASS analysis significantly depended on creatine concentration and saturation power (P < 0.05), not on manganese chloride concentration and saturation duration, advantageous over other CEST indices. The QUASS CEST algorithm reconstructs the steady-state CEST effect, enabling T1-normalized inverse CEST effect calculation for improved quantification of the underlying CEST system.

Accuracy and performance analysis for Bloch and Bloch-McConnell simulation methods

PurposeTo introduce new solution methods for the Bloch and Bloch-McConnell equations and compare them quantitatively to different known approaches. Theory and MethodsA new exact solution per time step is derived by means of eigenvalues and generalized eigenvectors. Fast numerical solution methods based on asymmetric and symmetric operator splitting, which are already known for the Bloch equations, are extended to the Bloch-McConnell equations. Those methods are compared to other numerical methods including spin domain, one-step and multi-step methods, and matrix exponential. Error metrics are introduced based on the exact solution method, which allows to assess the accuracy of each solution method quantitatively for arbitrary example data. ResultsAccuracy and performance properties for nine different solution methods are analyzed and compared in extensive numerical experiments including various examples for non-selective and slice-selective MR imaging applications. The accuracy of the methods heavily varies, in particular for short relaxation times and long pulse durations. ConclusionIn absence of relaxation effects, the numerical results confirm the rotation matrices approach as accurate and computationally efficient Bloch solution method. Otherwise, as well as for the Bloch-McConnell equations, symmetric operator splitting methods are recommended due to their excellent numerical accuracy paired with efficient run time.

Theoretical classification of exchange geometries from the perspective of NMR relaxation dispersion

NMR relaxation dispersion experiments have been widely applied to probe important conformational exchange of macro-molecules in many biological systems. The current improvements in computational techniques as well as the theoretical breakthroughs make the quantitative data analysis of complex exchange models possible. However, the topology of a given exchange model is also one of the main factors affecting the solution of Bloch-McConnell equation. The lack of a theoretical analysis of the exchange topologies at n-site exchange hinders further progress of such data analysis. Here, using graph theory, we reveal the topological complexity of n-site exchange and present all exchange models when n is less than 6. Furthermore, we introduce an alternative way, using machine learning, to select an exchange model based on a set of relaxation dispersion data without fitting them with every individual exchange model.

AcidoCEST MRI Evaluates the Bone Microenvironment in Multiple Myeloma.

Multiple myeloma (MM) is an incurable disease of malignant plasma cells in the bone marrow (BM). Adaptive responses to hypoxia may be an essential element in MM progression and drug resistance. This metabolic adaptation involves a decrease in extracellular pH (pHe), and it depends on the upregulation of glucose transporters (GLUTs) that is common in hypoxia and in cancer cells. CEST MRI is an imaging technique that assesses pHe indirectly by the exchange rate of magnetic saturation transfer between labile protons on a solute and water. Thus, this study aimed to determine the feasibility of acidoCEST MRI for pHe measurement using an orthotopic mouse model of MM compared with GLUT1 immunofluorescence staining as a reference. Orthotopic BM engrafted MM xenografts were established in NSG/NOD mice using the human RPMI8226 myeloma cell line. AcidoCEST MRI was performed approximately 6 weeks after intravenous challenge, before and after intravenous administration of iopamidol. BM pHe values were generated via fitting the CEST spectrum with the Bloch-McConnell equations. Samples were decalcified, sectioned, and immunostained for GLUT1 expression. Pearson's correlation was used to assess the relationship between pHe and [H3O+] versus GLUT1 expression. Ten mice underwent acidoCEST MRI followed by immunofluorescent histologic analysis. A strong negative correlation was seen between pHe versus GLUT1 expression (r = - 0.75, p < 0.001). After transformation of pH to [H3O+], a strong positive correlation between [H3O+] and GLUT1 expression was observed (r = 0.8, p < 0.001). AcidoCEST MRI can measure the extracellular pH of bone marrow affected by multiple myeloma. In this MM orthotopic mouse model, pHe measured by acidoCEST MRI showed strong correlations with the metabolic phenotype of BM tumor assessed by immunofluorescent histological assessment of GLUT1 overexpression.

Influence of phosphate concentration on amine, amide, and hydroxyl CEST contrast.

To evaluate the influence of phosphate on amine, amide, and hydroxyl CEST contrast using Bloch-McConnell simulations applied to physical phantom data. Phantom solutions of 4 representative metabolites with exchangeable protons-glycine (α-amine protons), Cr (η-amine protons), egg white protein (amide protons), and glucose (hydroxyl protons)-were prepared at different pH levels (5.6 to 8.9) and phosphate concentrations (5 to 80 mM). CEST images of the phantom were collected with CEST-EPI sequence at 3 tesla. The CEST data were then fitted to full Bloch-McConnell equation simulations to estimate the exchange rate constants. With the fitted parameters, simulations were performed to evaluate the intracellular and extracellular contributions of CEST signals in normal brain tissue and brain tumors, as well as in dynamic glucose-enhanced experiments. The exchange rates of α-amine and hydroxyl protons were found to be highly dependent on both pH and phosphate concentrations, whereas the exchange rates of η-amine and amide protons were pH-dependent, albeit not catalyzed by phosphate. With phosphate being predominantly intracellular, CEST contrast of α-amine exhibited a higher sensitivity to changes in the extracellular microenvironment. Simulations of dynamic glucose-enhanced signals demonstrated that the contrast between normal and tumor tissue was mostly due to the extracellular CEST effect. The proton exchange rates in some metabolites can be greatly catalyzed by the presence of phosphate at physiological concentrations, which substantially alters the CEST contrast. Catalytic agents should be considered as confounding factors in future CEST-MRI research. This new dimension may also benefit the development of novel phosphate-sensitive imaging methods.

Chemical Exchange Saturation Transfer MRI Optimal Continuous Wave RF Irradiation Parameters for Glycogen (glycoCEST) Detection

Chemical exchange saturation transfer (CEST) enables detection of molecules such as glycogen, whose concentrations are too low to impact the signal intensity of standard MR imaging. Detection of these molecules is achieved by selectively saturating a molecule of interest and by measuring the reduction in water signal due to saturation transfer. CEST effects are dependent on parameters such as CEST agent concentration, pH, temperature, relaxation rate, magnetic field strength as well as on experimental parameters such as repetition time, RF irradiation amplitude, and the imaging readout scheme. Measurement of molecules with exchangeable protons that resonate very close to water, e.g., hydroxyl groups in glycogen, is challenging especially at lower magnetic field strengths, mainly due to the effect of direct water saturation. Therefore, optimal RF irradiation parameters that maximize the CEST signal and reduce the competing factors are important for better quantification of glycogen-weighted CEST effects. In this study, analytical solution of the Bloch-McConnell equations was used to find optimal continuous wave RF irradiation parameters for detection of glycogen. In vivo tests were performed on a human calf muscle at different saturation powers to validate the optimal saturation parameters determined via simulation. The selected parameters were applied in vitro to CEST measurements in a phantom with varying glycogen concentrations and also in vivo in a human calf muscle. Our results show the possibility of detecting glycogen using CEST MRI at 3 T. It is further shown that the glycoCEST signal can be maximized by optimizing the RF pulse irradiation parameters (duration and power) and that different glycogen concentrations can be identified when applying the optimized saturation pulse.

Improved MR fingerprinting for relaxation measurement in the presence of semisolid magnetization transfer.

To characterize and minimize the magnetization transfer (MT) effect in MR fingerprinting (MRF) relaxation measurements with a 2-pool (2P) MT model of multiple tissue types. Semisolid MT effect in MRF was modeled using 2P Bloch-McConnell equations. The combinations of MT parameters of multiple tissues (white [WM] and gray matter [GM]) were used to build the MRF dictionary. Both 1-pool (1P) and 2P models were simulated to characterize the dependence on MT. Relaxations measured using MRF with spin-echo saturation-recovery (SR) or inversion-recovery preparations were compared with conventional SR-prepared T1 and multiple spin-echo T2 measurements. The simulations results were validated with phantoms and brain tissue samples. The MRF signal was different from the 1P and 2P models. 1P MRF produced significantly (P < .05) underestimated T1 in WM (20-30%) and GM (7-10%), while 2P MRF measured consistent T1 and T2 in both WM and GM with conventional measurements (pairwise test P > .1; correlated P < .05). Simulations showed that SR-prepared MRF measuring T1 had much less errors against the variation of the macromolecular fraction. Compared with inversion-recovery preparation, SR-prepared MRF produced higher relaxation correlations (R > 0.9) with conventional measurements in both WM and GM across samples, suggesting that SR-prepared MRF was less sensitive to the compositive effect of multiple MT parameters variations. 2P MRF using a combination of MT parameters for multiple tissue types can measure consistent relaxations with conventional methods. With the 2P models, SR-prepared MRF would provide an option for robust relaxation measurement under heterogeneous MT.

CEST MR-Fingerprinting: Practical considerations and insights for acquisition schedule design and improved reconstruction.

To understand the influence of various acquisition parameters on the ability of CEST MR-Fingerprinting (MRF) to discriminate different chemical exchange parameters and to provide tools for optimal acquisition schedule design and parameter map reconstruction. Numerical simulations were conducted using a parallel computing implementation of the Bloch-McConnell equations, examining the effect of TR, TE, flip-angle, water and , saturation-pulse duration, power, and frequency on the discrimination ability of CEST-MRF. A modified Euclidean distance matching metric was evaluated and compared to traditional dot product matching. L-Arginine phantoms of various concentrations and pH were scanned at 4.7T and the results compared to numerical findings. Simulations for dot product matching demonstrated that the optimal flip-angle and saturation times are and 1100 ms, respectively. The optimal maximal saturation power was 3.4μT for concentrated solutes with a slow exchange rate, and 5.2μT for dilute solutes with medium-to-fast exchange rates. Using the Euclidean distance matching metric, much lower maximum saturation powers were required (1.6 and 2.4μT, respectively), with a slightly longer saturation time (1500ms) and flip-angle. For both matching metrics, the discrimination ability increased with the repetition time. The experimental results were in agreement with simulations, demonstrating that more than a 50% reduction in scan-time can be achieved by Euclidean distance-based matching. Optimization of the CEST-MRF acquisition schedule is critical for obtaining the best exchange parameter accuracy. The use of Euclidean distance-based matching of signal trajectories simultaneously improved the discrimination ability and reduced the scan time and maximal saturation power required.

Approximated analytical characterization of the steady-state chemical exchange saturation transfer (CEST) signals.

CEST MRI can indirectly detect low-concentrated molecules via their proton exchange with the bulk water and is widely measured by a sensitivity index, the asymmetry of magnetization transfer ratio (MTRasym ). Because CEST applications are often limited by their low sensitivity or specificity, it is important to characterize MTRasym analytically to optimize its sensitivity or specifity. Approximated analytical solutions of the MTRasym spectrum were derived based on a 2-pool chemical exchange model for slow-to-intermediate exchanges. The optimal saturation pulse power for maximizing the MTRasym or tuning MTRasym to a specific exchange rate and the peak position and linewidth of a MTRasym spectrum were also derived. These approximated analytical solutions were compared with the solutions from the Bloch-McConnell equations using computer simulations. The approximated analytical solutions of the MTRasym spectra, the optimizing parameters, and the peak and linewidth of MTRasym matched well with the solutions of Bloch-McConnell equations in the slow or slow-to-intermediate exchange regimes. These approximate analytical solutions can provide insights to the understanding of CEST signal property and help the optimization of saturation parameters and the interpretation of CEST data.