NMR in Biomedicine最新文献

Cerebral glucose and oxygen metabolism and blood perfusion play key roles in neuroenergetics and oxidative phosphorylation to produce adenosine triphosphate (ATP) energy molecules in supporting cellular activity and brain function. Their impairments have been linked to numerous brain disorders. This study aimed to develop an in vivo magnetic resonance spectroscopy (MRS) method capable of simultaneously assessing and quantifying the major cerebral metabolic rates of glucose (CMR_Glc) and oxygen (CMRO₂) consumption, lactate formation (CMR_Lac), and tricarboxylic acid (TCA) cycle (V_TCA); cerebral blood flow (CBF); and oxygen extraction fraction (OEF) via a single dynamic MRS measurement using an interleaved deuterium (²H) and oxygen-17 (¹⁷O) MRS approach. We introduced a single-loop multifrequency radio-frequency (RF) surface coil that can be used to acquire proton (¹H) magnetic resonance imaging (MRI) or interleaved low-γ X-nuclei ²H and ¹⁷O MRS. By combining this RF coil with a modified MRS pulse sequence, ¹⁷O-isotope-labeled oxygen gas inhalation, and intravenous ²H-isotope-labeled glucose administration, we demonstrate for the first time the feasibility of simultaneously and quantitatively measuring six important physiological parameters, CMR_Glc, CMRO₂, CMR_Lac, V_TCA, CBF, and OEF, in rat brains at 16.4 T. The interleaved ²H-¹⁷O MRS technique should be readily adapted to image and study cerebral energy metabolism and perfusion in healthy and diseased brains.

Quantitative MRI and MRS have become important tools for the assessment and management of patients with neuromuscular disorders (NMDs). Despite significant progress, there is a need for new objective measures with improved specificity to the underlying pathophysiological alteration. This would enhance our ability to characterize disease evolution and improve therapeutic development. In this study, qMRI methods that are commonly used in clinical studies involving NMDs, like water T2 (T2_H2O) and T1 and fat-fraction (FF) mapping, were employed to evaluate disease activity and progression in the skeletal muscle of golden retriever muscular dystrophy (GRMD) dogs. Additionally, extracellular volume (ECV) fraction and single-voxel bicomponent water T2 relaxometry were included as potential markers of specific histopathological changes within the tissue. Apart from FF, which was not significantly different between GRMD and control dogs and showed no trend with age, T2_H2O, T1, ECV, and the relative fraction of the long-T2 component, A₂, were significantly elevated in GRMD dogs across all age ranges. Moreover, longitudinal assessment starting at 2 months of age revealed significant decreases in T2_H2O, T1, ECV, A₂, and the T2 of the shorter-T2 component, T2₁, in both control and GRMD dogs during their first year of life. Notably, insights from ECV and bicomponent water T2 indicate that (I) the elevated T2_H2O and T1 values observed in dystrophic muscle are primarily driven by an expansion of the extracellular space, likely driven by the edematous component of inflammatory responses to tissue injury and (II) the significant decrease of T2_H2O and T1 with age in control and GRMD dogs reflects primarily the progressive increase in fiber diameter and protein content during tissue development. Our study underscores the potential of multicomponent water T2 relaxometry and ECV to provide valuable insights into muscle pathology in NMDs.

Spin-lock (SL) pulses have been proposed to directly detect neuronal activity otherwise inaccessible through standard functional magnetic resonance imaging. However, the practical limits of this technique remain unexplored. Key challenges in SL-based detection include ultra-weak signal variations, sensitivity to magnetic field inhomogeneities, and potential contamination from blood oxygen level-dependent effects, all of which hinder the reliable isolation of neuronal signals. This pilot study evaluates the performance of the stimulus-induced rotary saturation (SIRS) technique to map visual stimulation response in the human cortex. A rotary echo spin-lock (RESL) preparation followed by a 2D echo planar imaging readout was used to investigate 12 healthy subjects at rest and during continuous exposure to 8 Hz flickering light. The SL amplitude was fixed to the target neuroelectric oscillations at that frequency. The signal variance was used as contrast metric, and two alternative post-processing pipelines (regression-filtering-rectification and normalized subtraction) were statistically evaluated. Higher variance in the SL signal was detected in four of the 12 subjects. Although group-level analysis indicated activation in the occipital pole, analysis of variance revealed that this difference was not statistically significant, highlighting the need for comparable control measures and more robust preparations. Further optimization in sensitivity and robustness is required to noninvasively detect physiological neuroelectric activity in the human brain.

MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex-valued convolutional neural network (CNN) for MRI denoising. A complex-valued CNN incorporating the noise level map (non-blind $ℂ$ DnCNN) was trained with ground truth and simulated noise-corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low-field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real-valued CNN and several other algorithms. For the simulated noise-corrupted testing dataset, the complex-valued models had superior normalized root-mean-square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non-blind $ℂ$ DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low-field data, the non-blind $ℂ$ DnCNN significantly improved the SNR and visual quality of the image. The proposed non-blind $ℂ$ DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non-blind $ℂ$ DnCNN to medical imaging. The method holds the potential to enable improved low-field MRI, facilitating enhanced diagnostic imaging in under-resourced areas.

The conventional gradient-echo steady-state signal model is the basis of various spoiled gradient-echo (SPGR) based quantitative MRI models, including variable flip angle (VFA) MRI and dynamic contrast-enhanced MRI (DCE). However, including preparation pulses, such as fat suppression or saturation bands, disrupts the steady-state and leads to a bias in T₁ and DCE parameter estimates. This work introduces a signal model that improves the accuracy of VFA T₁-mapping and DCE for interrupted spoiled gradient-echo (I-SPGR) acquisitions. The proposed model was applied to a VFA T₁-mapping I-SPGR sequence in the Gold Standard T₁-phantom (3 T), in the brain of four healthy volunteers (3 T), and to an abdominal DCE examination (1.5 T). T₁-values obtained with the proposed and conventional model were compared to reference T₁-values. Bland-Altman analysis (phantom) and analysis of variance (in vivo) were used to test whether bias from both methods was significantly different (p = 0.05). The proposed model outperformed the conventional model by decreasing the bias in the phantom with respect to the phantom reference values (mean bias -2 vs. -35% at 3 T) and in vivo with respect to the conventional SPGR (-6 vs. -37% bias in T₁, p < 0.01). The proposed signal model estimated approximately 48% (depending on baseline T₁) higher contrast concentrations in vivo, which resulted in decreased DCE pharmacokinetic parameter estimates of up to 35%. The proposed signal model improves the accuracy of quantitative parameter estimation from disrupted steady-state I-SPGR sequences. It therefore provides a flexible method for applying fat suppression, saturation bands, and other preparation pulses in VFA T₁-mapping and DCE.

Surgical biopsy of brainstem tumors carries a risk of neurological injury. We performed magnetic resonance spectroscopy (MRS) of 2-hydroxyglutarate (2HG) and glycine in patients with brainstem tumors to assess the feasibility of detecting and quantifying 2HG in the brainstem to obviate the need for a diagnostic biopsy and to establish the clinical significance of glycine MRS in brainstem tumors in vivo. Twenty adult patients with radiographically identified presumed brainstem gliomas were prospectively enrolled in the study. Proton MRS was obtained at 3T with a protocol tailored for detection of 2HG and glycine (TE 97-ms PRESS). Spectra were fit using LCModel software and in-house basis signals of metabolites and lipids. The metabolite concentrations were quantified with reference to water and examined with respect to clinical outcomes, including postgadolinium MRI and overall survival time. MRS data from 19 patients were included in subsequent analysis, excluding suboptimal data from one patient. Tumors with elevated 2HG (> 1.9 mM, N = 8) and undetectable 2HG (< 0.3 mM, N = 11) were clearly distinguishable. Tumors with elevated glycine (> 1.5 mM, N = 4) showed rapid progression. Kaplan-Meier survival analyses with metabolite measures demonstrated that tumors with 2HG higher than 1.0 mM were significantly associated with a favorable prognosis (p = 0.01). In contrast, tumors with glycine higher than 2.5 mM showed a strong association with poor survival (p = 0.0005). The data confirm detection of 2HG in brainstem tumors at a concentration that is consistent with an IDH mutation and expected good prognosis, whereas elevated glycine in brainstem tumors portends rapid tumor progression and a worse prognosis.

Mild traumatic brain injuries (TBIs) are frequent in the European population. The pathophysiological changes after TBI include metabolic changes, but these are not observable using current clinical tools. We aimed to evaluate multinuclear MRI as a mean of assessing these changes. In our model, pigs were exposed to a controlled cortical impact (CCI) directly on the dura and scanned at 2 h and 2 days after injury. A multinuclear MRI protocol was used. It included hyperpolarized [1-¹³C]pyruvate MRI, which allows depiction of hyperpolarized carbon-13, through its metabolism from pyruvate to lactate or bicarbonate. At Day 2, cerebral microdialysis were performed, and tissue was obtained for analyses. At Day 0, the cerebral blood flow was reduced in the affected hemisphere (TBI: 31.7 mL/100 mL/min, contralateral: 35.6 mL/100 mL/min, p = 0.1227), and the impacted area showed reduced oxygenation (R₂*, TBI: 33.11 s^-1, contralateral: 22.20 s^-1, p = 0.035). At both days, the lactate-to-pyruvate ratios (hyperpolarized [1-¹³C]pyruvate) were increased (Day 0: p = 0.023, Day 2: p = 0.022). However, this study can only evaluate the total injury and, thus, cannot differentiate effects from craniotomy and CCI. This metabolic difference was not found using cerebral microdialysis nor a lactate dehydrogenase (LDH) activity assay. The metabolic changes depicted in this study contributes to our understanding of mild TBI; however, the clinical potential of multinuclear MRI is yet to be determined.

Magnetic Resonance Fingerprinting (MRF) can be accelerated with simultaneous multislice (SMS) imaging for joint T₁ and T₂ quantification. However, the high inter-slice and in-plane acceleration in SMS-MRF causes severe aliasing artifacts, limiting the multiband (MB) factors to typically 2 or 3. Deep learning has demonstrated superior performance compared to the conventional dictionary matching approach for single-slice MRF, but its effectiveness in SMS-MRF remains unexplored. In this paper, we introduced a new deep learning approach with decoupled spatiotemporal feature learning for SMS-MRF to achieve high MB factors for accurate and volumetric T₁ and T₂ quantification in neuroimaging. The proposed method leverages information from both spatial and temporal domains to mitigate the significant aliasing in SMS-MRF. Neural networks, trained using either acquired SMS-MRF data or simulated data generated from single-slice MRF acquisitions, were evaluated. The performance was further compared with both dictionary matching and a deep learning approach based on residual channel attention U-Net. Experimental results demonstrated that the proposed method, trained with acquired SMS-MRF data, achieves the best performance in brain T₁ and T₂ quantification, outperforming dictionary matching and residual channel attention U-Net. With a MB factor of 4, rapid T₁ and T₂ mapping was achieved with 1.5 s per slice for quantitative brain imaging.