NMR in Biomedicine最新文献

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.

The study aimed to investigate the feasibility of dynamic glucose-enhanced (DGE) MRI technology in the clinical application of glioma. Twenty patients with glioma were examined using a preoperative DGE-MRI protocol before clinical intervention. A brief hyperglycemic state was achieved by injecting 50 mL of 50% w/w D-glucose intravenously during the DGE imaging. The total acquisition time for the DGE was 15 min. Area-under-the-curve (AUC) images were calculated using the DGE images. AUC_2-7min values of the glioma core, margin area, edema area, and contralateral brain parenchyma were compared using Mann-Whitney U tests. Overall, gray and white matter areas in the AUC images showed relatively low DGE signal change and bilateral symmetry. However, the tumor cores displayed a significant hyperintensity. A high DGE signal change was also seen in the necrotic, cystic, and cerebrospinal areas. These results show that DGE MRI is a feasible technique for the study of brain tumors as part of a clinical exam. Importantly, DGE MRI showed enhancement in areas confirmed histopathologically as tumors, whereas Gd T1w MRI did not show any enhancement in this area. Since the D-glucose molecule is smaller than Gd-based contrast agents, DGE MRI may be more sensitive to subtle blood-brain barrier disruptions, thus potentially providing early information about possible malignancy. These findings provide a new perspective for the further exploration and analysis of D-glucose uptake in brain tumors.

With the increasing adoption of ultra-high-field (UHF) systems, sodium (²³Na) imaging is undergoing clinical translation. However, existing commercially available dual-tuned coils, while demonstrating adequate sodium signal-to-noise ratio (SNR), fail to meet the requirements for high-resolution proton (¹H) structural imaging, resulting in suboptimal workflow efficiency during integrated multi-nucleus examinations. In this work, we present a novel double-layer ¹H/²³Na transceiver array with eight channels per nucleus for human brain MRI at 7T. The proposed array achieved 64% of the ¹H SNR of the 32-channel receive array commonly used in clinical practice. By integrating dual-nucleus imaging capabilities within a single platform, this design eliminates coil-switching requirements and post-acquisition registration, thereby streamlining clinical workflows and advancing sodium MRI translational feasibility.

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.

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.

In vivo fluorine-19 MRI using F-based tracer media has shown utility and versatility for a wide range of biomedical uses, particularly immune and stem cell detection, as well as biosensing. As with many advanced MRI acquisition techniques, the sensitivity and limit of detection (LOD) in vivo is a key consideration for a successful study outcome. In this review, we analyze the primary factors that limit cell LOD. The achievable sensitivity is strongly dependent on the specific composition of tracer, cell type of interest, cell activity, data acquisition and reconstruction methods, and MRI hardware design. Recent innovations in molecular ¹⁹F tracer design and image acquisition-reconstruction methods have achieved significant leaps in ¹⁹F MRI sensitivity, and integration of these new materials and methods into studies can result in > 10-fold improvement in LOD. These developments will help unlock the full potential of clinical ¹⁹F MRI for biomedical applications.

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.