NMR in Biomedicine最新文献

This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled patients with histopathologically confirmed IEE and GBM from June 2016 to June 2021. Radiomics features were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) sequence images, and classification models were constructed using EL methods and logistic regression (LR). The efficiency of the models was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The combined EL model, based on clinical parameters and radiomic features from T1WI and T2WI images, demonstrated good discriminative ability, achieving an area under the receiver operating characteristics curve (AUC) of 0.96 (95% CI 0.94-0.98), a specificity of 0.84, an accuracy of 0.92, and a sensitivity of 0.95 in the training set, and an AUC of 0.89 (95% CI 0.83-0.94), a specificity of 0.83, an accuracy of 0.81, and a sensitivity of 0.74 in the validation set. The discriminative efficacy of the EL model was significantly higher than that of the LR model. Favorable calibration performance and clinical applicability for the EL model were observed. The EL model combining preoperative MR-based tumor radiomics and clinical data showed high accuracy and sensitivity in differentiating IEE from GBM preoperatively, which may potentially assist in clinical management of these brain tumors.

Obesity is associated with important changes in cardiac energetics and function, and an increased risk of adverse cardiovascular outcomes. Multi-nuclear MRS and MRI techniques have the potential to provide a comprehensive non-invasive assessment of cardiac metabolic perturbation in obesity. A rat model of obesity was created by high-fat diet feeding. This model was characterized using in vivo hyperpolarized [1-¹³C]pyruvate and [2-¹³C]pyruvate MRS, echocardiography and perfused heart ³¹P MRS. Two groups of obese rats were subsequently treated with either caloric restriction or the glucagon-like peptide-1 analogue/agonist liraglutide, prior to reassessment. The model recapitulated cardiovascular consequences of human obesity, including mild left ventricular hypertrophy, and diastolic, but not systolic, dysfunction. Hyperpolarized ¹³C and ³¹P MRS demonstrated that obesity was associated with reduced myocardial pyruvate dehydrogenase flux, altered cardiac tricarboxylic acid (TCA) cycle metabolism, and impaired myocardial energetic status (lower phosphocreatine to adenosine triphosphate ratio and impaired cardiac ΔG_~ATP). Both caloric restriction and liraglutide treatment were associated with normalization of metabolic changes, alongside improvement in cardiac diastolic function. In this model of obesity, hyperpolarized ¹³C and ³¹P MRS demonstrated abnormalities in cardiac metabolism at multiple levels, including myocardial substrate selection, TCA cycle, and high-energy phosphorus metabolism. Metabolic changes were linked with impairment of diastolic function and were reversed in concert following either caloric restriction or liraglutide treatment. With hyperpolarized ¹³C and ³¹P techniques now available for human use, the findings support a role for multi-nuclear MRS in the development of new therapies for obesity.

Proton magnetic resonance spectroscopic imaging (¹H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in ¹H-MRSI has led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, Marchenko-Pastur principal component analysis (MP-PCA) based denoising and low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo ¹H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower-concentration metabolites. Our study provided a framework for how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical ¹H-FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low-concentration metabolites.

Posttraumatic stress disorder (PTSD) is a chronic psychiatric condition that follows exposure to a traumatic stressor. Though previous in vivo proton (¹H) MRS) research conducted at 4 T or lower has identified alterations in glutamate metabolism associated with PTSD predisposition and/or progression, no prior investigations have been conducted at higher field strength. In addition, earlier studies have not extensively addressed the impact of psychiatric comorbidities such as major depressive disorder (MDD) on PTSD-associated ¹H-MRS-visible brain metabolite abnormalities. Here we employ 7 T ¹H MRS to examine concentrations of glutamate, glutamine, GABA, and glutathione in the medial prefrontal cortex (mPFC) of PTSD patients with MDD (PTSD+_MDD+; N = 6) or without MDD (PTSD+_MDD-; N = 5), as well as trauma-unmatched controls without PTSD but with MDD (PTSD-_MDD+; N = 9) or without MDD (PTSD-_MDD-; N = 18). Participants with PTSD demonstrated decreased ratios of GABA to glutamine relative to healthy PTSD-_MDD- controls but no single-metabolite abnormalities. When comorbid MDD was considered, however, MDD but not PTSD diagnosis was significantly associated with increased mPFC glutamine concentration and decreased glutamate:glutamine ratio. In addition, all participants with PTSD and/or MDD collectively demonstrated decreased glutathione relative to healthy PTSD-_MDD- controls. Despite limited findings in single metabolites, patterns of abnormality in prefrontal metabolite concentrations among individuals with PTSD and/or MDD enabled supervised classification to separate them from healthy controls with 80+% sensitivity and specificity, with glutathione, glutamine, and myoinositol consistently among the most informative metabolites for this classification. Our findings indicate that MDD can be an important factor in mPFC glutamate metabolism abnormalities observed using ¹H MRS in cohorts with PTSD.

Filter exchange imaging (FEXI) is a double diffusion-encoding (DDE) sequence that is specifically sensitive to exchange between sites with different apparent diffusivities. FEXI uses a diffusion-encoding filtering block followed by a detection block at varying mixing times to map the exchange rate. Long mixing times enhance the sensitivity to exchange, but they pose challenges for imaging applications that require a stimulated echo sequence with crusher gradients. Thin imaging slices require strong crushers, which can introduce significant diffusion weighting and bias exchange rate estimates. Here, we treat the crushers as an additional encoding block and consider FEXI as a triple diffusion-encoding sequence. This allows the bias to be corrected in the case of multi-Gaussian diffusion, but not easily in the presence of restricted diffusion. Our approach addresses challenges in the presence of restricted diffusion and relies on the ability to independently gauge sensitivities to exchange and restricted diffusion for arbitrary gradient waveforms. It follows two principles: (i) the effects of crushers are included in the forward model using signal cumulant expansion; and (ii) timing parameters of diffusion gradients in filter and detection blocks are adjusted to maintain the same level of restriction encoding regardless of the mixing time. This results in the tuned exchange imaging (TEXI) protocol. The accuracy of exchange mapping with TEXI was assessed through Monte Carlo simulations in spheres of identical sizes and gamma-distributed sizes, and in parallel hexagonally packed cylinders. The simulations demonstrate that TEXI provides consistent exchange rates regardless of slice thickness and restriction size, even with strong crushers. However, the accuracy depends on b-values, mixing times, and restriction geometry. The constraints and limitations of TEXI are discussed, including suggestions for protocol adaptations. Further studies are needed to optimize the precision of TEXI and assess the approach experimentally in realistic, heterogeneous substrates.

The aim of the current study is to demonstrate the feasibility of radiofrequency (RF) pulses generated via an optimal control (OC) algorithm to perform magnetic resonance elastography (MRE) and quantify the mechanical properties of materials with very short transverse relaxation times (T2 < 5 ms) for the first time. OC theory applied to MRE provides RF pulses that bring isochromats from the equilibrium state to a fixed target state, which corresponds to the phase pattern of a conventional MRE acquisition. Such RF pulses applied with a constant gradient allow to simultaneously perform slice selection and motion encoding in the slice direction. Unlike conventional MRE, no additional motion-encoding gradients (MEGs) are needed, enabling shorter echo times. OC pulses were implemented both in turbo spin echo (OC rapid acquisition with refocused echoes [RARE]) and ultrashort echo time (OC UTE) sequences to compare their motion-encoding efficiency with the conventional MEG encoding (classical MEG MRE). MRE experiments were carried out on agar phantoms with very short T2 values and on an ex vivo bovine tendon. Magnitude images, wave field images, phase-to-noise ratio (PNR), and shear storage modulus maps were compared between OC RARE, OC UTE, and classical MEG MRE in samples with different T2 values. Shear storage modulus values of the agar phantoms were in agreement with values found in the literature, and that of the bovine tendon was corroborated with rheometry measurements. Only the OC sequences could encode motion in very short T2 samples, and only OC UTE sequences yielded magnitude images enabling proper visualization of short T2 samples and tissues. The OC UTE sequence produced the best PNRs, demonstrating its ability to perform anatomical and mechanical characterization. Its success warrants in vivo confirmation in further studies.

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B₀ shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at T_E = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

For patients with type 1 diabetes mellitus complicated by severe hypoglycemia, clinical islet transplantation is an efficacious alternative to whole pancreas transplantation. While islet transplantation has improved over the last few years, there remain questions regarding its cost-effectiveness and donor allosensitization, which is exacerbated when islets from more than one donor are required. Understanding the features of a pancreas that would provide viable islets prior to isolation may lead to development of an accurate assay that could identify suitable pancreases and provide significant cost savings to a clinical islet transplantation program. In this pilot study, solid-state high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy was used to assess samples of convenience of human pancreatic tissue taken prior to islet isolation both before and after incubation using the two-layer perfluorocarbon (PFC)/University of Wisconsin (UW) solution cold-storage method. We observed that, prior to incubation, human pancreatic tissue exhibited evidence of hypoxia with decreased peak integrals associated with glucose and increased peak integrals corresponding to lactate and free fatty acids. After incubation, we observed a reversal of the hypoxia-induced damage, as integrals corresponding to glucose increased, and those corresponding to lactate and free fatty acid resonances decreased. Interestingly, a significant correlation between the ratio of the glucose integral (at 3.0-4.5 ppm) to the sum of the fatty acid (at 0.9 ppm) and lactate + fatty acid (at 1.3 ppm) integrals and glucose responsiveness, a measure of islet viability, of the isolated islets, was observed after incubation in PFC/UW solution for pancreases that responded to PFC/UW solution incubation (p = 0.02). Notably, pancreases with little or no change in the integral ratio after PFC/UW solution incubation had poor recovery. These results suggest that tissue recovery is a key feature for determining islet cell viability, and further that HRMAS NMR may be a practical method to quickly assess human donor pancreatic tissue prior to islet isolation for clinical transplantation.

Phase-resolved functional lung (PREFUL) MRI is a proton-based, contrast agent-free technique derived from the Fourier decomposition approach to measure regional ventilation and perfusion dynamics during free-breathing. Besides the necessity of extensive PREFUL postprocessing, the utilized MRI sequence must fulfill specific requirements. This study investigates the impact of sequence selection on PREFUL-MRI-derived functional parameters by comparing the standard spoiled gradient echo (SPGRE) sequence with a lung-optimized balanced steady-state free precession (bSSFP) sequence, thereby facilitating PREFULs clinical application in pulmonary disease assessment. This study comprised a prospective dataset of healthy volunteers and a retrospective dataset of patients with suspected chronic thromboembolic pulmonary hypertension. Both cohorts underwent PREFUL-MRI with both sequences to assess the correspondence of PREFUL ventilation and perfusion parameters (A). Additionally, healthy subjects were scanned a second time to evaluate repeatability (B), whereas patients received dynamic contrast-enhanced (DCE)-MRI, considered the perfusion gold standard for comparison with PREFUL-MRI (C). Signal-to-noise ratio (SNR), calculated from the unprocessed images, was compared alongside median differences of PREFUL-MRI-derived parameters using a paired Wilcoxon signed rank test. Further evaluations included calculation of the Pearson correlation, intraclass-correlation coefficient for repeatability assessment, and spatial overlap (SO) for regional comparison of PREFUL-MRI and DCE-MRI. bSSFP showed a clear SNR advantage over SPGRE (median: 23 vs. 9, p < 0.001). (A) Despite significant differences, parameter values were strongly correlated (r ≥ 0.75). After thresholding, binary maps showed high healthy overlap across both cohorts (SO_Healthy > 86%) and high defect overlap in the patient cohort (SO_Defect ≥ 48%). (B) bSSFP demonstrated slightly higher repeatability across most parameters. (C) Both sequences demonstrated comparable correspondence to DCE-MRI, with SPGRE excelling in absolute quantification and bSSFP in spatial agreement. Although bSSFP showed superior SNR results, both sequences displayed spatial defect concordance and highly correlated PREFUL parameters with deviations regarding repeatability and alignment with DCE-MRI.

We aim to explore the feasibility of head and neck time-of-flight (TOF) magnetic resonance angiography (MRA) at ultra-low-field (ULF). TOF MRA was conducted on a highly simplified 0.05 T MRI scanner with no radiofrequency (RF) and magnetic shielding. A flow-compensated three-dimensional (3D) gradient echo (GRE) sequence with a tilt-optimized nonsaturated excitation RF pulse, and a flow-compensated multislice two-dimensional (2D) GRE sequence, were implemented for cerebral artery and vein imaging, respectively. For carotid artery and jugular vein imaging, flow-compensated 2D GRE sequences were utilized with venous and arterial blood presaturation, respectively. MRA was performed on young healthy subjects. Vessel-to-background contrast was experimentally observed with strong blood inflow effect and background tissue suppression. The large primary cerebral arteries and veins, carotid arteries, jugular veins, and artery bifurcations could be identified in both raw GRE images and maximum intensity projections. The primary brain and neck arteries were found to be reproducible among multiple examination sessions. These preliminary experimental results demonstrated the possibility of artery TOF MRA on low-cost 0.05 T scanners for the first time, despite the extremely low MR signal. We expect to improve the quality of ULF TOF MRA in the near future through sequence development and optimization, ongoing advances in ULF hardware and image formation, and the use of vascular T1 contrast agents.