Low-field MRI has recently gained interest due to its potential for increased accessibility, reduced cost, and improved safety. However, high-quality anatomical imaging and robust tissue characterization remains an active area of research, particularly when aiming for a simple, one-click scan that captures all relevant information in a single acquisition. Bright-blood imaging is widely used for visualizing cardiac structures and coronary arteries, whereas black-blood is optimal for delineating the myocardium, atrial and vessel walls. High-resolution imaging is required for the accurate detection and segmentation of small anatomical structures, such as the coronary arteries, to enable assessment of narrowing or blockages. Co-registered T 1 / T 2 $$ {T}_1/{T}_2 $$ mapping enables quantitative myocardial tissue characterization, offering valuable clinical information for the detection of myocardial abnormalities. In this study, we sought to develop a novel free-breathing, motion-compensated 3D multi-contrast high-resolution cardiac MR sequence for simultaneous assessment of whole-heart cardiovascular anatomy via bright- and black-blood imaging and myocardial tissue quantification by joint T 1 $$ {T}_1 $$ and T 2 $$ {T}_2 $$ mapping at 0.55 T in a single scan. Data were acquired over six interleaved contrasts with various preparation modules using a variable flip angle bSSFP spiral-like readout with 2D image-based navigation for translational motion correction, resulting in a predictable acquisition time of ≈ 12 $$ \approx 12 $$ min. Images were reconstructed using non-rigid motion corrected iterative sensitivity encoding followed by high-dimensional patch-based low-rank denoising, resulting in the acquisition, reconstruction and quantitative mapping time of ≈ 31 $$ \approx 31 $$ min. In the phantom study, sequence performance was evaluated using correlation and Bland-Altman analysis against reference gold-standard and clinical mapping methods. In vivo, 3D bright- and black-blood volumes were assessed in multiple views, and vessel sharpness was quantified from multiplanar images. For joint T 1 / T 2 $$ {T}_1/{T}_2 $$ mapping, bull's-eye plots were generated to evaluate the mean, standard deviation, and coefficient of variation for apical, mid-cavity, and basal segments, and results were summarized using violin plots. Differences between the proposed 3D sequence and established 2D methods were analyzed with a two-tailed t $$ t $$ -test. In the phantom study, a small positive bias in T 1 $$ {T}_1 $$ of 6 . 3 ms $$ 6.3\kern0.3em \mathrm{ms} $$ was observed compared with inversion recovery spin-echo and 23 . 5 ms $$ 23.5\kern0.3em \mathrm{ms} $$ with MOLLI, while for T 2 $$ {T}_2 $$ biases of 7 . 3 ms $$ 7.3\kern0.3em \mathrm{ms} $$ compared with spin-echo and 0 . 8 ms $$ 0.8\kern0.3em \mathrm{ms} $$ with T 2 $$ {T}_2 $$ prep bSSFP were found. In vivo, statistically similar T 1 $$ {T}_1 $$ values of ( 648 ± 26 ) ms $$ \left(648\pm 26\right)\kern0.3em \mathrm{ms} $$ and T 2 $$ {T}_2 $$ values of ( 56 . 9 ± 3 . 2 ) ms $$ \left(56.9\pm 3.2\right)\kern0.3em \mathrm{ms} $$ were obtained, with differences versus MOLLI of - 3 ms ± 15 ms $$ -3\kern0.3em \mathrm{ms}\pm 15\kern0.3em \mathrm{ms} $$ ( p = 0 . 75 $$ p=0.75 $$ ) and versus T 2 $$ {T}_2 $$ prep bSSFP of - 0 . 5 ms ± 2 . 1 ms $$ -0.5\kern0.3em \mathrm{ms}\pm 2.1\kern0.3em \mathrm{ms} $$ ( p = 0 . 63 $$ p=0.63 $$ ). The proposed sequence demonstrated high image quality and accurate mapping despite the inherent limitations of low-field strength, suggesting its feasibility for comprehensive cardiac assessment in resource-limited environments.
Growth retardation, defined by impaired anthropometric development relative to age-based population standards, may be accompanied by metabolic alterations. This study aimed to characterize serum and urine metabolomic profiles in children with growth retardation using exploratory 1H-NMR spectroscopy. Thirty children with growth retardation and 24 controls were included in this cross-sectional study. Serum and urine samples were analyzed by 1H-NMR spectroscopy, and annotated metabolites were evaluated using Chenomx, MetaboAnalyst, and R software. Clinical and laboratory variables were compared between groups. Metabolomic analysis included univariate comparisons, volcano plots, PCA, OPLS-DA, Spearman correlation analysis, age-adjusted regression, exploratory ROC analysis, and metabolite set enrichment analysis. The growth retardation group had lower age and BMI than controls, while serum iron and AST were higher. Volcano plot analysis showed a more distinct pattern of nominal metabolite alterations in serum than in urine. In serum, 3-hydroxybutyrate and glucuronate showed relative increases, whereas N-acetylgalactosamine, alanine, glutamate, and glucose showed relative decreases. In urine, metabolite differences were less pronounced. Multivariate analysis demonstrated a significant overall group effect in serum but not in urine. Within the growth retardation group, age was negatively correlated with serum 3-hydroxybutyrate and positively correlated with N-acetyl-L-aspartic acid, whereas several urinary metabolites showed negative correlations with age. After age adjustment, only 4-ethylbenzoic acid remained significant in serum after multiple testing correction. Exploratory ROC analysis identified N-acetylgalactosamine and 4-ethylbenzoic acid in serum and beta-alanine and creatinine in urine as the metabolites with the highest within cohort classification tendency. Children with growth retardation exhibited modest metabolic differences, with more consistent group-related alterations observed in serum than in urine. These findings mainly suggest changes related to energy metabolism, amino acid turnover, and intermediary metabolism. However, the results remain exploratory and require confirmation in larger, age-stratified and independently validated cohorts.
Nuclear magnetic resonance (NMR) metabolomics offers a robust platform for the analysis of complex biological samples, but its application is often constrained by signal overlap and limited sensitivity. In this study, we developed an optimized two-dimensional NMR approach (probe-induced sensitivity enhancement with nonuniform sampling and band-selective 1H-13C HSQC [PRISE-NUS-bs-HSQC]) to enable high-throughput, high-resolution semiquantitative profiling of 22 amino acids in plasma from a rat model of heart failure (HF). When combined with spatially resolved metabolomics of cardiac tissue using AFADESI-MSI, plasma levels of valine, leucine, isoleucine, phenylalanine, methionine, and glutamine showed positive correlations with metabolic disturbances in the infarct (I) area of the heart. A composite biomarker panel derived from these amino acids distinguished HF rats from controls with an area under the curve (AUC) of 0.926. Using multiple therapeutic strategies, including traditional Chinese medicine (TCM), Western medicine, combined therapy with TCM and Western medicine, and active ingredients from Chinese medicine, we observed that all interventions reversed HF-associated amino acid metabolic dysregulation to varying extents. Notably, combined treatment with Qishen Yiqi dropping pills and sacubitril/valsartan produced broader metabolic normalization and more pronounced synergistic effects than either monotherapy. Collectively, this study establishes a noninvasive and efficient 2D NMR-based framework for exploring the association between systemic amino acid metabolism and regional cardiac injury in HF. The integrated analytical strategy provides mechanistic insight into amino acid metabolic remodeling during HF progression and underscores its translational potential for noninvasive diagnosis and therapeutic evaluation.
Rotator cuff (RC) tears are a prevalent source of shoulder pain that restrict everyday activities and diminish quality of life. While MRI reveals structural damage in the tendon, it is uncertain if RC illness is also indicated in the body's circulating metabolites. The establishment of systemic signals may guide the future creation of blood-based biomarker panels; however, existing evidence is currently insufficient. This exploratory, hypothesis-generating case-control study investigated serum metabolites via proton nuclear magnetic resonance (1H-NMR) in persons with full-thickness RC injuries and age- and sex-matched healthy controls (50 cases, 50 controls). Spectra underwent meticulous quality assessments, and predetermined statistical analyses were conducted with management of false discoveries. We also examined how well different metabolites discriminated RC tears from controls and explored which metabolic pathways were most represented. Numerous metabolites exhibited variations between the groups. Concentrations of N-carbamoyl-β-alanine, taurine, erythritol, and N-acetylaspartate were elevated in RC tears, but levels of 3-methylhistidine and proline were diminished. Pathway enrichment revealed pyrimidine metabolism, bile-acid biosynthesis, taurine/hypotaurine metabolism, β-alanine metabolism, and histidine metabolism, indicating coordinated perturbations at the pathway level rather than isolated alterations. These data must be regarded as preliminary and hypothesis-generating, lacking the establishment of causation or immediate clinical applicability. Although preliminary, these findings are scientifically significant because they suggest that full-thickness RC tears may be associated with a coordinated systemic metabolic signature rather than only a focal tendon abnormality. This systems-level perspective adds to the current understanding of RC pathology by generating biologically plausible hypotheses related to tissue remodeling, oxidative stress adaptation, and energy metabolism. Prospective longitudinal investigations with targeted quantification, external validation, and integration with imaging and clinical outcomes are required before clinical translation.
Proton magnetic resonance spectroscopy (1H-MRS) was used to detect the levels of N-acetylaspartate (NAA)/total creatinine (tCr) and choline (Cho)/tCr in bilateral hippocampus and posterior cingulate gyrus of postmenopausal patients with noncognitive impairment and cognitive impairment and to evaluate the cutoff point of related brain metabolites. To enhance the early recognition of postmenopausal cognitive impairment, this study included 62 postmenopausal patients with subjective cognitive decline (SCD), 62 postmenopausal patients with mild cognitive impairment (MCI), and 56 postmenopausal healthy controls (HC) without cognitive impairment. The relationship between brain metabolites and cognitive function was analyzed using Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), and 1H-MRS. The cutoff point for related brain metabolites were predicted by the receiver operating characteristic curve and the Youden index (YDI). The scores of MMSE and MOCA in the SCD group and the MCI group were lower than those in the HC group, and the scores of MMSE and MOCA in the MCI group were lower than those in the SCD group. The differences of NAA/tCr in the bilateral hippocampus, bilateral posterior cingulate gyrus, and Cho/tCr in the right hippocampus among the three groups were statistically significant. In comparison with the HC group, NAA/tCr in the bilateral hippocampus of the SCD group was decreased, and Cho/tCr in the right hippocampus of the MCI group decreased. Compared with the HC group and the SCD group, NAA/tCr in the bilateral hippocampus and bilateral posterior cingulate gyrus of the MCI group significantly decreased. Among them, the correlation coefficient between NAA/tCr in bilateral hippocampus regions and MMSE and MOCA scale scores were the highest. When distinguishing the SCD group from the HC group, the area under the curve (AUC) of NAA/tCr in the left hippocampus was the highest at 0.687 (95%CI: 0.591-0.784, p < 0.001), with a standard error (SE) of 0.049, acceptable sensitivity (71.0%) and specificity (60.7%), a maximum YDI of 0.317, and a corresponding cutoff point of 1.445; when distinguishing the MCI group from the HC group, the AUC of left hippocampal NAA/tCr was the highest at 0.876 (95%CI: 0.815-0.938, p < 0.001), with an SE of 0.031, high sensitivity (77.4%) and specificity (82.1%), a maximum YDI of 0.595, and a corresponding cutoff point of 1.265. Additionally, for distinguishing between the SCD group and the MCI group, the AUC of left hippocampal NAA/tCr was 0.752 (95%CI: 0.665-0.839, p < 0.001), with moderate sensitivity (59.7%) but high specificity (82.3%), a YDI of 0.420, and a cutoff point of 1.155. Postmenopausal SCD patients, postmenopausal MCI patients, and postmenopausal healthy subjects exhibit significant differences in brain metabolite levels. According to the cutoff point for related brain metabolites, early screening for cognitive impairment in postmenopausal middle-aged and elderly women can serve as a reference for clinical diagnosis.
Brain metabolism is vital to healthy brain function and is often altered in disease; yet direct investigation in patients is challenging. Although animal models are commonly used for studying brain metabolism, their use is under increasing scrutiny due to concerns of animal welfare and model validity. Human pluripotent stem cell (hPSC)-derived cerebral organoids (COs) present a unique opportunity to model human brain developmental and neuropathological processes, allowing for detailed metabolic characterization via multiple approaches. Here, we applied high-resolution magic-angle spinning (HR-MAS) proton nuclear magnetic resonance (1H-NMR) spectroscopy to analyze metabolite levels in hPSC-derived COs, establishing a pipeline to study neurometabolic pathways in these engineered human brain tissues. We identified and quantified 17 metabolites in hPSC-derived COs at different stages of maturity. The high spectral quality (linewidth < 4 Hz, SNR > 65) allowed detection of metabolite levels in 85- to 312-day-old hPSC-derived COs, which exhibited a metabolic profile similar to human fetal brain, with key distinguishing features relative to human adult brain, including: elevated lactate levels; approximately equimolar glutamate and glutamine levels; low N-acetylaspartate levels; and an abundance of hypotaurine. In summary, this study presents direct metabolic assessment in intact COs via HR-MAS 1H-NMR spectroscopy. Our approach provides a platform for investigating human brain metabolism and its alteration in human brain models of neurodegeneration.
Magnetic resonance spectroscopy provides novel noninvasive tools for postmortem forensic investigations. While postmortem temperature assessment using 1H-MRS has recently been established, noninvasive determination of brain tissue pH remains largely unexplored despite evidence linking pH to the cause of death. In this study, we investigated the feasibility of in situ brain pH determination using postmortem 1H-MRS based on the chemical shift difference between myo-inositol (mI, reference metabolite) and acetate (Ace, indicator metabolite) in 58 decedents. LCModel-based fitting strategies were evaluated using simulations that mimic postmortem brain 1H-MRS spectra in order to identify the most accurate and precise approach and to assess factors affecting chemical shift determination. The in situ MRS data were subsequently analyzed using the optimal strategy. Brain pH values derived from the mI-Ace chemical shift difference were validated against pH measurements obtained from homogenized brain tissue samples. Our results demonstrate that the optimal fitting is achieved by allowing increased flexibility in the chemical shift determination of Ace. Using this approach, robust detection of low pH values in postmortem brain tissue is achieved. These findings demonstrate the feasibility of Ace-based 1H-MRS for noninvasive in situ assessment of postmortem brain pH and indicate its potential for forensic applications.
Phosphorus magnetic resonance spectroscopy (31P-MRS) enables noninvasive measurement of brain metabolism, yet its reproducibility in clinical settings remains unclear. We systematically assessed intrasession and intersession variability as well as interindividual differences of key phosphorus metabolites at 3 T in healthy individuals and persons with Parkinson's disease under various experimental conditions. Intersession variability, as measured by coefficients of variation (CoVs) increased notably for longer scan intervals (~1 year), and metabolite ratios from well-resolved spectral signals (i.e., adenosine triphosphate [ATP], phosphocreatine [PCr], and intracellular inorganic phosphate [Pi]) exhibited consistently higher stability compared with ratios calculated from metabolite signals overlapping on the spectrum (e.g., total nicotinamide adenine dinucleotide [tNAD], as well as phosphate monoesters [PMEs] and phosphate diesters [PDEs]). Test-retest variability ranged from ~5 to 25 CoV%, where PCr, ATP-α, and ATP-γ were the most stable while glycerophosphocholine (GPC), glycerophosphoethanolamine (GPE), phosphoethanolamine (PE), and tNAD varied considerably. Interindividual variability was found to be higher than intraindividual variability for all metabolite ratios, ranging from ~9 to 33 CoV%. By systematically quantifying intraindividual and interindividual variability, as well as providing explicit sample size recommendations, this study facilitates more reliable longitudinal and cross-sectional clinical trials and translational studies of brain metabolism featuring 31P-MRS.
This study aimed to investigate longitudinal changes in intramyocellular (IMCL) and extramyocellular lipids (EMCL) in skeletal muscle during hindlimb unloading using in vivo proton magnetic resonance spectroscopy (1H-MRS). Hindlimb unloading was performed in male Wistar rats (n = 11) for 14 days. 1H-MRS measurements were acquired in the tibialis anterior (TA) and plantaris muscles at baseline and after unloading. In addition, control animals (n = 5) were analyzed over the same period under standard housing conditions. IMCL and EMCL were quantified using LCModel. IMCL levels decreased in both the TA (2.88 ± 2.08 to 1.57 ± 0.50 × 10-3 mmol/L, p = 0.0295) and plantaris muscles (2.70 ± 0.99 to 1.32 ± 0.83 × 10-3 mmol/L, p = 0.0019) following hindlimb unloading. EMCL levels showed an increase in the plantaris muscle (1.80 ± 1.44 to 3.78 ± 2.22 × 10-3 mmol/L, p = 0.0234), whereas no significant change was observed in the TA. In control animals, no consistent directional changes were observed in either IMCL or EMCL over the same interval. Hindlimb unloading was associated with a reduction in IMCL content in skeletal muscle. These findings provide longitudinal in vivo evidence of IMCL alterations under disuse conditions. Changes in EMCL were observed in the plantaris muscle; however, these should be interpreted with caution. 1H-MRS may serve as a useful noninvasive tool for assessing muscle lipid content.
Tissue electrical properties are required by electromagnetic simulation software to conduct radiofrequency (RF) safety studies. Values are commonly taken from existing databases of tissue properties, where brain conductivity was measured ex vivo. We hypothesize that using in vivo brain conductivity values, as reported in the recent literature on in vivo MRI measurements, can improve the accuracy of such simulations. Sixteen subjects were scanned at 3T to obtain experimental maps of the transmit RF field, B 1 + $$ {B}_1^{+} $$ , of the head. Electromagnetic simulations were performed using biomodels with varying morphologies, using both a conventional ( σ ex vivo = 0.46 S / m $$ {\sigma}_{\mathrm{ex}\ \mathrm{vivo}}=0.46\ \mathrm{S}/\mathrm{m} $$ ) and a modified brain conductivity ( σ in vivo = 0.70 S / m $$ {\sigma}_{\mathrm{in}\ \mathrm{vivo}}=0.70\ \mathrm{S}/\mathrm{m} $$ ). A framework was developed to process the simulated B 1 + $$ {B}_1^{+} $$ fields, including a systematic method to combine the two excitation ports of the transmit coil model (accounting for different load impedances between simulation and experiment), geometric alignment, and scaling of the simulated fields, allowing a quantitative comparison of complex B 1 + $$ {B}_1^{+} $$ maps of the brain with experimental maps. Specific absorption rate (SAR) maps were also estimated by different methods, including a novel B 1 + $$ {B}_1^{+} $$ -derived formula. Normalized root-mean-squared errors between simulation and experiment, in the brain, were approximately 6% for B 1 + $$ {B}_1^{+} $$ magnitude. While head geometry mainly impacted the accuracy of B 1 + $$ {B}_1^{+} $$ magnitude, with errors up to 11% (p = 0.007) between the best fitting human model and the worst one, brain electrical conductivity mainly impacted B 1 + $$ {B}_1^{+} $$ phase, with errors reduced by 48% when using σ in vivo $$ {\sigma}_{\mathrm{in}\ \mathrm{vivo}} $$ instead of σ ex vivo $$ {\sigma}_{\mathrm{ex}\ \mathrm{vivo}} $$ (p = 0.0004). The agreement in average brain SAR, between simulation and experiment, was also improved, with differences reduced from 62% ( σ ex vivo $$ {\sigma}_{\mathrm{ex}\ \mathrm{vivo}} $$ ) to 22% ( σ in vivo $$ {\sigma}_{\mathrm{in}\ \mathrm{vivo}} $$ ). Using brain conductivity values from recent in vivo studies improves the accuracy of RF safety modelling. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT04645628.
Parotid tumors exhibit diverse biomechanical and microstructural characteristics that remain challenging to characterize noninvasively. This study aimed to investigate these properties using ex vivo tabletop magnetic resonance elastography (MRE) combined with multiparametric MRI, including relaxometry, diffusion-weighted imaging, and fat quantification. Twelve patients (six males, six females; mean age 60.3 ± 11.2 years) undergoing parotid tumor resection were included. Resected specimens were assessed histopathologically according to the WHO Classification of Head and Neck Tumors (5th edition, 2022) and imaged within 36 h postresection using two-dimensional Bessel-MRE alongside T1 and T2 relaxometry, diffusion imaging, and fat fraction mapping. MRE-derived parameters included shear wave speed (SWS), penetration rate (PR), elasticity (μ), and viscosity (η). Compared with adjacent normal tissue, pathological tissue demonstrated significantly higher SWS (p = 0.014), elasticity (p = 0.027), viscosity (p = 0.037), and T1 relaxation time (p < 0.001), along with reduced fat fraction (p < 0.001). Penetration rate showed a near-significant increase (p = 0.065), whereas T2 exhibited no overall difference. Subtype analysis revealed that pleomorphic adenomas had significantly higher apparent diffusion coefficient (ADC) (p = 0.008), T1 (p = 0.008), and T2 (p = 0.008) values than Warthin tumors, with greater deviation from normal tissue. Correlation analysis showed that in normal tissue, fat fraction strongly influenced variability in SWS, T1, and ADC, with ADC demonstrating a strong negative correlation with fat fraction (r = -0.95, p < 0.001), limiting its interpretability without fat correction. In contrast, pathological tissue demonstrated a tightly coupled cluster of ADC, T1, and T2 (r = 0.95-0.96, all p < 0.001) reflecting water content, independent of mechanical parameters. These findings indicate that ex vivo MRE combined with multiparametric MRI enables quantitative assessment of parotid tumor properties through complementary biomarkers reflecting distinct physical mechanisms. Although viscoelastic parameters alone were insufficient to differentiate all tumor subtypes, their integration with diffusion and relaxometry metrics provides a more comprehensive characterization of tumor microstructure. This multiparametric approach shows promise for translation to in vivo applications, potentially enabling improved noninvasive tumor characterization and informing surgical planning.
The diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MRI) data. In the initial phase, volumetric changes, comprising cortical thickness, white matter, grey matter, cerebrospinal fluid, and total intracranial volume, were derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizing the CAT12 toolbox in statistical parametric mapping (SPM). Linear regression was utilized on these variables over time to create slopes that reflect volumetric change rates, which then served as inputs for machine learning classifiers. The slopes of cortical thickness exhibited the greatest classification accuracy, reaching 82.5% with a random forest model for differentiating AD from CN individuals. During the second phase, a deep learning methodology was utilized, relying solely on the MRI scans and excluding the outcomes from the first phase. A pre-trained 3D ResNet-101 convolutional neural network (CNN) model extracted spatial characteristics from MRI volumes, whereas long short-term memory (LSTM) networks recorded temporal dynamics across subsequent annual scans. This hybrid CNN-LSTM design markedly improved classification performance, attaining 96.7% accuracy for AD against CN and enhancing the distinction of MCI cases. Nonetheless, discrepancies in MCI categorization were chiefly ascribed to the restricted access to annual MRI data and the model's pre-training on CN and AD cohorts. These findings highlight the potential of integrating volumetric statistical analysis with deep learning for automated AD categorization. This work enhances neuroimaging diagnostic methods by utilizing both spatial and temporal MRI data, enabling early diagnosis and better evaluation of disease development.
MRI can detect the most significant pathological changes of muscle-fat replacement and muscle edema in muscular dystrophies. MRI-derived texture analysis is superior to conventional MRI for identifying pathological changes in muscular dystrophies. Furthermore, the combination of selected radiomics features and clinical biomarkers can enhance the diagnostic accuracy for muscular dystrophies. Muscular dystrophies are difficult to discriminate from their mimickers, so further research is warranted to identify the optimal feature combination and validate the performance of the combined model in myopathies prone to misdiagnosis. This study evaluated the diagnostic accuracy of radiomics features and clinical biomarkers in differentiating muscular dystrophies from their mimickers in a cohort of 161 myopathy patients using machine learning techniques. Multiple machine learning algorithms were jointly applied to screen robust features. The results showed that the combined nomogram exhibited better performance than the individual model using clinical or radiomic features, achieving an AUC of 0.955 in the training set and 0.923 in the validation set. Decision curve analysis confirmed the clinical utility of the nomogram. This multiparametric approach, combining texture features from MRI T1-weighted sequences and STIR sequences with clinical biomarkers (age, gender, and creatine kinase), significantly enhanced the discriminative power. The better performance of the nomogram compared with expert evaluations demonstrated its potential application in distinguishing muscular dystrophies from their mimickers.
This study aimed to propose a mixed single-echo and multiecho MyoFoldstar sequence enabling simultaneous myocardial multiparametric mapping and wall-motion quantification. MyoFoldstar is designed as a 2D, single breath-holding sequence that sequentially performs joint T1/T2 mapping, T2* mapping, and cine imaging within 17 heartbeats, using a golden-angle radial gradient-echo (GRE) readout and ECG synchronization. The joint T1/T2 mapping collects seven single-shot, single-echo images on the first seven heartbeats with inversion and T2 preparation (T2prep). The subsequent T2* mapping acquires multishot, multiecho data over six heartbeats, followed by segmented cine with a single-echo readout over four heartbeats. In vivo feasibility was demonstrated in 12 healthy volunteers at 3 T and compared with conventional single-task sequences (MOLLI and SASHA for T1, T2prep bSSFP for T2, and BB-meGRE for T2*). Accuracy was validated in phantom studies. Both in vivo and phantom studies demonstrated the feasibility of MyoFoldstar for simultaneously acquiring cardiac T1, T2, T2*, and cine imaging. MyoFoldstar-derived myocardial T1 (1521 ± 102 ms), T2 (43.2 ± 3.0 ms), and T2* (19.2 ± 2.4 ms) showed good agreement with values from conventional single-task sequences (T1: 1248 ± 35 ms by MOLLI and 1580 ± 38 ms by SASHA; T2: 43.5 ± 2.2 ms by T2prep bSSFP; T2*: 23.4 ± 2.6 ms by BB-meGRE). Compared with conventional cine, MyoFoldstar images exhibited reduced myocardium-blood contrast, yet left ventricular function quantifications were mainly preserved (r > 0.9). Phantom results indicated that MyoFoldstar achieves good accuracy relative to reference standards. MyoFoldstar enables rapid myocardial T1, T2, T2*, and cine imaging within a single breath-hold scan, delivering a time-saving and comprehensive assessment of cardiovascular magnetization resonance.
Accurate and non-invasive subtyping of localised renal tumours is an important unmet clinical challenge in uro-oncology and has significant implications for patient mortality and quality of life. Developing novel imaging methods to characterise and stratify indeterminate kidney tumours at an early stage has the potential to address this clinical challenge. Here we applied sodium MRI (23Na-MRI) to estimate kidney tumour sodium content in a prospectively recruited case series of 10 patients (mean age ± SD 64 ± 8 years; 7:3 male:female ratio). The patients had localised renal tumours which included six renal oncocytomas (ROs), two chromophobe renal cell carcinomas (chRCCs), three clear cell RCCs (ccRCC) and one papillary RCC (pRCC). The patients underwent 23Na-MRI at 3 T (3D sodium cones and double-angle B1 mapping) and 1H-MRI which included R2* mapping and intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI). The following imaging parameters were quantified within the renal tumours and in the normal-appearing kidney parenchyma: apparent total sodium concentration (TSC); apparent 23Na and 1H relaxation rates (R2*); perfusion fraction (fp); and diffusion coefficient (Dt). 23Na-MRI findings were correlated with conventional 1H-MRI measures of perfusion, hypoxia and cellularity. The mean apparent TSC in ccRCC and in pRCC were 135 ± 59 mM and 81 mM, respectively. The apparent TSC was significantly higher in ROs compared to chRCCs: 162 ± 58 mM vs. 71 ± 2 mM (p < 0.05). The apparent TSC inversely correlated with 1H-R2* (Spearman r = -0.39, p < 0.05). In conclusion, this study showed the feasibility and potential of using 23Na-MRI in renal tumours to probe sodium concentrations. These preliminary findings suggest a differential sodium content between benign ROs and malignant chRCCs. The inverse correlation between sodium concentration and 1H-R2* as a surrogate of hypoxia may indicate a biophysical relationship between the two which requires further validation in larger patient cohorts.
Low-field magnetic resonance imaging (MRI) offers a cost-effective and accessible alternative to high-field systems but inherently suffers from low signal-to-noise ratio (SNR) and prolonged scan times. Although k-space under-sampling shortens scan time, the resulting incomplete data typically introduces resolution loss and artifacts in the reconstructed images. Deep learning-based MRI reconstruction methods primarily focus on high-field, high-quality MRI data and image-domain reconstruction, with limited attention to the unique characteristics of low-field data and the relationship between k-space and image-domain representations. Furthermore, existing k-space reconstruction approaches often overlook the intrinsic encoding dependencies within k-space and lack mechanisms for learnable feature fusion across the two domains. To address the limitations, we propose DUAG, a deep dual-domain interaction reconstruction framework with adaptive gating fusion for low-field MRI. The framework employs a cascaded deep architecture with multi-scale U-Net structures to achieve hierarchical feature representation and incorporates a hybrid dual-domain interaction module. Attention mechanisms are introduced to model long-range dependencies, enabling precise capture of underlying correlations among k-space frequency encodings and image-domain pixels. In addition, an adaptive gating fusion strategy is designed for dynamical weighting and cross-domain feature fusions. In order to enhance feature reuse and improve the generalization ability of the model. Experiments on public 0.3 T low-field dataset show that DUAG reaches 42.79 ± 0.75 PSNR and 0.920 ± 0.010 SSIM. To further verify the generalization capability, we conducted real-world experiments on the laboratory-collected 0.5 T low-field MRI scanner data, DUAG achieves superior reconstruction performance of 34.13 ± 1.02 PSNR and 0.889 ± 0.013 SSIM. The proposed framework provides a promising solution for high-quality low-field MRI reconstruction, which is expected to promote the deployment of cost-effective MRI systems in resource-constrained clinical settings.
Large poly(ethylene) glycol (PEG) chains are often conjugated to proteins or biomolecules to inhibit proteolytic degradation, mask immunogenic response, reduce clearance rates, and improve biodistribution of therapeutics, vaccines, drug delivery systems, and gene therapy formulations. The PEG macromolecular chain can also be used as a noninvasive reporter to track biologics in vivo by magnetic resonance spectroscopy (MRS). Rapid internal dynamics of PEG render the transverse 1H spin relaxation time to be comparable to water (~0.5 s) and amenable to imaging through traditional pulsed field gradient techniques. While water signal grossly exceeds that of PEG it is possible to filter 1H MRS signal of PEGylated conjugates through one of two ways-(1) stimulated echo acquisition mode (STEAM) MRS, which leverages huge differences in the diffusion of water versus PEGylated constructs, and (2) 13C-edited 1H MRS of fully 13C-enriched PEGylated constructs. Here, we compare both approaches. A 15 kDa 13C-enriched PEG chain was prepared alone, conjugated to bovine serum-albumin (BSA), and incorporated into a 52-nm-diameter PEG-poly(lactic acid) (PLA) nanoparticle. These three PEG constructs were then separately monitored in real time by 13C-edited 1H MRS, after introducing them into rat animal models intravenously. A 13C-editing scheme was employed to monitor 1H MRS PEG signal in the vasculature via a radiofrequency coil placed around the tail. An observed two-component decay of the PEG signal is attributed to perfusion and early equilibration (alpha phase) and slow clearance (beta phase). 13C-PEG alone, 13C-PEG-BSA, and 13C-PEG-PLA nanoparticles exhibited half-lives of 38.6 min, 23.4 h, and 11.9 h, respectively. The relatively rapid clearance rates associated with the PEG-PLA nanoparticles is expected to arise from enzymatic degradation of the PLA chain. Using STEAM-based editing schemes, we then evaluated sensitivity and water suppression in diffusion-edited 1H MRS for (12C)-PEGylated BSA contrasting 2-, 20-, and 40-kDa PEG chains, in imaging phantom samples. Larger molecular weight PEG chains (i.e., 40 kDa) proved far superior to smaller PEG chain reporters due to reduced inhomogeneities and longer T2, upon employing either a 13C-HQMC filter or a STEAM-based diffusion filter.
Rhythmic motor paradigms are widely used to study sensorimotor timing, yet magnetic resonance imaging (MRI) research has largely focused on central processes, with limited insight into peripheral neuromuscular mechanisms. Motor unit MRI (MUMRI), a motion-sensitive technique in which muscle contraction induces intravoxel water redistribution and transient signal attenuation, enables in vivo visualization of muscle activity. In this study, we developed and validated a combined behavioral-MUMRI paradigm to characterize muscle recruitment during rhythmic foot tapping. Healthy participants performed an auditory-paced tapping task inside an MRI scanner while timing was recorded via an MRI-compatible force transducer and muscle activity was measured using single-slice MUMRI. A variable-latency cueing design systematically sampled the temporal relationship between auditory cues, motor execution, and image acquisition, allowing identification of the optimal latency window for detecting contraction-related signal changes. Fixed-latency acquisitions were then used to assess reproducibility. Behavioral results showed stable performance across conditions, with low variability in tapping accuracy (mean coefficient of variation [CoV] ≈0.078). Transient, localized signal reductions consistent with muscle contraction were observed in anterior lower leg muscles during dorsiflexion. Voxel-wise analyses demonstrated high within-condition reproducibility and latency-dependent spatial patterns, with the greatest average consistency when tapping aligned with scanner rhythm (r ≈0.68). These findings establish a robust framework for integrating rhythmic motor tasks with MUMRI, highlighting the importance of precise temporal alignment for reliable measurement of muscle activity. This approach provides a reproducible method for linking motor behavior to peripheral neuromuscular dynamics and offers potential for advancing both basic and clinical MRI research.
Vertigo is a type of dizziness that typically occurs without movement and may arise independently or as a symptom of an underlying condition. It is classified as either central or peripheral, with central vertigo often caused by brain tumors, hemorrhages, or vascular aneurysms. This study aims to identify functional metabolites associated with vertigo and explore their related metabolic pathways. Metabolic profiling of serum samples from 33 vertigo patients and 34 healthy controls was conducted using 1H-QNMR spectroscopy. Following log10 transformation and normalization, univariate and multivariate analyses (FC, p-value, FDR, and VIP) were applied to identify significant metabolites. KEGG-based pathway and disease enrichment analyses were performed to explore biological relevance and clinical associations. ROC analysis was performed to evaluate the ability of selected metabolites to discriminate between vertigo patients and healthy controls within this exploratory dataset, and inter-metabolite relationships were assessed using Pearson correlation. Vertigo patients showed significantly different metabolomic profiles compared to healthy controls. Serum levels of capric acid, homocysteine, O-phosphoethanolamine, methylmalonate, N-carbamoylaspartate, and proline were significantly elevated, while glycylproline levels were decreased. These findings suggest a potential link between vertigo and these functionally relevant metabolites. Vertigo patients showed lower glycylproline and higher homocysteine and capric acid levels than healthy controls. These metabolites discriminated between groups in this exploratory cohort, but they should not be interpreted as clinically validated diagnostic markers. Further independent, longitudinal, and clinically well-characterized studies are required.
Proton magnetic resonance imaging (MRI) and spectroscopy (MRS) are widely used in clinical and research applications. Recent interest in X-nuclei studies highlights their ability to provide additional biochemical information, but the intrinsically low X-nuclear signal-to-noise ratio (SNR) significantly increases scan time. Simultaneous (rather than serial) acquisition of multiple nuclei can significantly reduce experiment time, but most conventional MR systems lack this capability without modifications. We present a cost-effective system that enables simultaneous multinuclear imaging and spectroscopy on conventional MR spectrometers. Our approach offers enhanced flexibility for multinuclear experiments, supports multinuclear array receive capability, and maintains phase stability in the radio frequency (RF) chain. The proposed system comprised multiple transmit and receive mixing channels and a four-channel flexible local oscillator (LO) source. By interfacing with the spectrometer, simultaneous transmit and receive at different frequencies were achieved. The performance of the system was evaluated through bench measurement and phantom multinuclear MRI and MRS experiments. Transmit and receive channel isolation of better than 30 dB was measured on the bench. Simultaneous excitation and reception of 2H and 23Na gradient echo images were acquired, as well as interleaved excitation with simultaneous reception of 1H, 2H, and 23Na FIDs. Water-suppressed 1H and 31P MRS were performed simultaneously on phantoms mimicking muscle metabolites. Results across all experiments showed no signal-to-noise ratio (SNR) loss compared to single-frequency operation. The proposed system supports multiple variations of simultaneous experiments on conventional MRI systems, demonstrating its flexibility in configuring experiments with varying numbers of nuclei (2-4), different transmit modes (simultaneous or interleaved), and supporting receive array coils of up to 16 channels, while maintaining phase stability in the RF chain without the need for retrospective correction.