NMR in Biomedicine最新文献

³¹P magnetic resonance spectroscopy (MRS) can spectrally resolve metabolites involved in phospholipid metabolism whose levels are altered in many cancers. Ultra-high field facilitates the detection of phosphomonoesters (PMEs) and phosphodiesters (PDEs) with increased SNR and spectral resolution. Utilizing multi-echo MR spectroscopic imaging (MRSI) further enhances SNR and enables T₂ information estimation per metabolite. To address the specific absorption rate (SAR) challenges associated with high-power demanding adiabatic or composite block pulses in multi-echo phosphorus imaging, we present a dual-band refocusing RF pulse designed for operation at B₁ amplitudes of 14.8 μT which holds potential for integration into multi-echo sequences. Phantom and in vivo experiments conducted in the brain at 7 Tesla validated the effectiveness of this low-power dual-band RF pulse. Furthermore, we implemented the dual-band RF pulse into a multi-echo MRSI sequence where it offered the potential to increase the number of echo pulses within the same acquisition time compared to high-power adiabatic implementation, demonstrating its feasibility and practicality.

Tissue-mimicking reference phantoms are indispensable for the development and optimization of magnetic resonance (MR) measurement sequences. Phantoms have greatest utility when they mimic the MR signals arising from tissue physiology; however, many of the properties underlying these signals, including tissue relaxation characteristics, can vary as a function of magnetic field strength. There has been renewed interest in magnetic resonance imaging (MRI) at field strengths less than 1 T, and phantoms developed for higher field strengths may not be physiologically relevant at these lower fields. This work focuses on developing materials with specific relaxation properties for lower magnetic field strengths. Specifically, we developed recipes that can be used to create synthetic samples for target nuclear magnetic resonance relaxation values for fields between 0.0065 and 0.55 T. $T_1$ and $T_2$ mixing models for agarose-based gels doped with a paramagnetic salt (one of CuSO₄, GdCl₃, MnCl₂, or NiCl₂) were created using relaxation measurements of synthetic gel samples at 0.0065, 0.064, and 0.55 T. Measurements were evaluated for variability with respect to measurement repeatability and changing synthesis protocol or laboratory temperature. The mixing models were used to identify formulations of agarose and salt composition to approximately mimic the relaxation times of five neurological tissues (blood, cerebrospinal fluid, fat, gray matter, and white matter) at 0.0065, 0.0475, 0.05, 0.064, and 0.55 T. These mimic sample formulations were measured at each field strength. Of these samples, the GdCl₃ and NiCl₂ measurements were closest to the target tissue relaxation times. The GdCl₃ or NiCl₂ mixing model recipes are recommended for creating target relaxation samples below 0.55 T. This work can help development of MRI methods and applications for low-field systems and applications.

Alterations in tricarboxylic acid (TCA) cycle metabolism are associated with hepatic metabolic disorders. Elevated hepatic acetate concentrations, often attributed to high caloric intake, are recognized as a pivotal factor in the etiology of obesity and metabolic syndrome. Therefore, the assessment of acetate breakdown and TCA cycle activity plays a central role in understanding the impact of diet-induced alterations on liver metabolism. Magnetic resonance-based deuterium metabolic imaging (DMI) could help to unravel the underlying mechanisms involved in disease development and progression, however, the application of conventional deuterated glucose does not lead to substantial enrichment in hepatic glutamine and glutamate. This study aimed to demonstrate the feasibility of DMI for tracking deuterated acetate breakdown via the TCA cycle in lean and diet-induced fatty liver (FL) rats using 3D DMI after an intraperitoneal infusion of sodium acetate-d3 at 9.4T. Localized and nonlocalized liver spectra acquired at 10 time points post-injection over a 130-min study revealed similar intrahepatic acetate uptake in both animal groups (AUC_FL = 717.9 ± 131.1 mM▯min^-1, AUC_lean = 605.1 ± 119.9 mM▯min^-1, p = 0.62). Metabolic breakdown could be observed in both groups with an emerging glutamine/glutamate (Glx) peak as a downstream metabolic product (AUC_FL = 113.6 ± 23.8 mM▯min^-1, AUC_lean = 136.7 ± 41.7 mM▯min^-1, p = 0.68). This study showed the viability of DMI for tracking substrate flux through the TCA cycle, underscoring its methodological potential for imaging metabolic processes in the body.

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain ¹H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single-component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared with those other methods was investigated. The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. Although the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p ≤ 0.0001). Minimally detectable changes are in the range 0.5-1.9 mM, and the percentage coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Here, the PRaMM model, a method for an improved quantification of metabolites, was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Ageing is a complex phenomenon affecting a wide range of coexisting biological processes. The homogeneity of the studied population is an essential parameter for valid interpretations of outcomes. The presented study capitalises on the MRI data available in the Human Connectome Project-Aging (HCP-A) and, within individuals over 55 years of age who passed the HCP-A section criteria, compares a subgroup of 37 apparently neurocognitively healthy individuals selected based on stringent criteria with 37 age and sex-matched individuals still representative of typical ageing but who did not pass the stringent definition of neurocognitively healthy. Specifically, structural scans, diffusion weighted imaging and T1w/T2w ratio were utilised. Furthermore, data of 26 HCP-A participants older than 90 years as notional 'super-agers' were analysed. The relationship of age and several microstructural MRI metrics (T1w/T2w ratio, mean diffusivity, intracellular volume fraction and free water volume fraction) differed significantly between typical and healthy ageing cohort in areas highly relevant for ageing such as hippocampus, prefrontal and temporal cortex and cerebellum. However, the trajectories of the healthy ageing population did not show substantially better overlap with the findings in people older than 90 than those of the typical population. Therefore, caution must be exercised in the choice of adequate study group characteristics relevant for respective ageing-related hypotheses. Contrary to typical ageing group, the healthy ageing cohort may show generally stable levels of several MRI metrics of interest.

Since the overall glioma mass and its subcomponents-enhancing region (malignant part of the tumor), non-enhancing (less aggressive tumor cells), necrotic core (dead cells), and edema (water deposition)-are complex and irregular structures, non-Euclidean geometric measures such as fractal dimension (FD or "fractality") and lacunarity are needed to quantify their structural complexity. Fractality measures the extent of structural irregularity, while lacunarity measures the spatial distribution or gaps. The complex geometric patterns of the glioma subcomponents may be closely associated with the grade and molecular landscape. Therefore, we measured FD and lacunarity in the glioma subcomponents and developed machine learning models to discriminate between tumor grades and isocitrate dehydrogenase (IDH) gene status. 3D fractal dimension (FD3D) and lacunarity (Lac3D) were measured for the enhancing, non-enhancing plus necrotic core, and edema-subcomponents using preoperative structural-MRI obtained from the The Cancer Genome Atlas (TCGA) and University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) glioma cohorts. The FD3D and Lac3D measures of the tumor-subcomponents were then compared across glioma grades (HGGs: high-grade gliomas vs. LGGs: low-grade gliomas) and IDH status (mutant vs. wild type). Using these measures, machine learning platforms discriminative of glioma grade and IDH status were developed. Kaplan-Meier survival analysis was used to assess the prognostic significance of FD3D and Lac3D measurements. HGG exhibited significantly higher fractality and lower lacunarity in the enhancing subcomponent, along with lower fractality in the non-enhancing subcomponent compared to LGG. This suggests that a highly irregular and complex geometry in the enhancing-subcomponent is a characteristic feature of HGGs. A comparison of FD3D and Lac3D between IDH-wild type and IDH-mutant gliomas revealed that mutant gliomas had ~2.5-fold lower FD3D in the enhancing-subcomponent and higher FD3D with lower Lac3D in the non-enhancing subcomponent, indicating a less complex and smooth enhancing subcomponent, and a more continuous non-enhancing subcomponent as features of IDH-mutant gliomas. Supervised ML models using FD3D from both the enhancing and non-enhancing subcomponents together demonstrated high-sensitivity in discriminating glioma grades (~97.9%) and IDH status (~94.4%). A combined fractal estimation of the enhancing and non-enhancing subcomponents using MR images could serve as a non-invasive, precise, and quantitative measure for discriminating glioma grade and IDH status. The combination of 2-hydroxyglutarate-magnetic resonance spectroscopy (2HG-MRS) with FD3D and Lac3D quantification may be established as a robust imaging signature for glioma subtyping.

Native T1 mapping is a non-invasive technique used for early detection of diffused myocardial abnormalities, and it provides baseline tissue characterization. Post-contrast T1 mapping enhances tissue differentiation, enables extracellular volume (ECV) calculation, and improves myocardial viability assessment. Accurate and precise segmenting of the left ventricular (LV) myocardium on T1 maps is crucial for assessing myocardial tissue characteristics and diagnosing cardiovascular diseases (CVD). This study presents a deep learning (DL)-based pipeline for automatically segmenting LV myocardium on T1 maps and automatic computation of radial T1 and ECV values. The study employs a multicentric dataset consisting of retrospective multiparametric MRI data of 332 subjects to develop and assess the performance of the proposed method. The study compared DL architectures U-Net and Deep Res U-Net for LV myocardium segmentation, which achieved a dice similarity coefficient of 0.84 ± 0.43 and 0.85 ± 0.03, respectively. The dice similarity coefficients computed for radial sub-segmentation of the LV myocardium on basal, mid-cavity, and apical slices were 0.77 ± 0.21, 0.81 ± 0.17, and 0.61 ± 0.14, respectively. The t-test performed between ground truth vs. predicted values of native T1, post-contrast T1, and ECV showed no statistically significant difference (p > 0.05) for any of the radial sub-segments. The proposed DL method leverages the use of quantitative T1 maps for automatic LV myocardium segmentation and accurately computing radial T1 and ECV values, highlighting its potential for assisting radiologists in objective cardiac assessment and, hence, in CVD diagnostics.

Dimethyl sulfoxide (DMSO) has wide biomedical applications such as cryoprotectant and hydrophobic drug carrier. Here, we report for the first time that DMSO can generate a distinctive chemical exchange saturation transfer (CEST) signal at around -2 ppm. Structural analogs of DMSO, including aprotic and protic solvents, also demonstrated CEST signals from -1.4 to -3.8 ppm. When CEST detectable barbituric acid (BA) was dissolved in DMSO solution and was co-loaded to liposome, two obvious peaks at 5 and -2 ppm were observed, indicating that DMSO and related solvent system can be monitored in a label-free manner via CEST, which can be further applied to imaging drug nanocarriers. With reference to previous studies, there could be molecular interactions or magnetization transfer pathways, such as the relayed nuclear Overhauser enhancement (rNOE), that lead to this detectable CEST contrast at negative offset frequencies of the Z-spectrum. Our findings suggest that small molecules of organic solvents could be involved in magnetization transfer processes with water and readily detected by CEST magnetic resonance imaging (MRI), providing a new avenue for detecting solvent-water and solvent-drug interactions.

Although the information obtained from in vivo proton magnetic resonance spectroscopy (¹H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Conventional $B_0$ gradient systems have several weaknesses including high cost and bulk. As a step towards addressing these while providing new degrees of freedom for spatial encoding and system design in Magnetic Resonance Imaging (MRI), a radio frequency (RF) gradient encoding system and pulse sequence for phase encoding using the Bloch-Siegert (BS) shift were developed. Optimized BS spatial encoding coils with bucking windings (counter-wound loops) were designed and constructed, along with compatible homogeneous imaging coils for excitation and signal reception. Two coil systems were developed: one for phantom imaging and a second for human wrist imaging. BS phase-encoded imaging and BS RF pulse simulations were performed. Pulse sequences were designed for linear stepping in k-space and implemented on a 47.5-mT scanner to image resolution phantoms in both coil setups. Reconstructions were performed using both the full $B_1^{+}$ -based encoding fields for each BS pulse amplitude and using inverse discrete Fourier transforms. A $B_0$ gradient was used for frequency encoding during signal readout, and the third axis was projected. Specific absorption ratio (SAR) calculations were performed for the wrist coil to determine the safety of BS-based RF encoding for $B_0$ fields in the low field MRI regime. The optimized RF spatial encoding coils resulted in higher linearity ( $R^2=0.9981$ and 0.9921 in the phantom and wrist coils, respectively) than coils used in previous work. The phantom and wrist imaging coils were validated in simulations and experimentally to produce a peak $B_1^{+}=1.35$ G and 0.8 G with 12-W input power, respectively, in the field-of-view (length = 11 cm) used for imaging. Nominal imaging resolutions of 5.22 and 7.21 mm were, respectively, achieved by the two-coil systems in the RF phase-encoded dimension. Coil systems, pulse sequences, and image reconstructions were developed for linear RF phase encoding using the BS shift and validated using a 47.5-mT open low field scanner, establishing a key component required for $B_0$  gradient-free imaging at low $B_0$  field strengths.