NMR in Biomedicine最新文献

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.

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.

Adequate perfusion is critical for maintaining normal brain function and aberrations thereof are hallmarks of many diseases. Pseudo-Continuous Arterial Spin Labeling (pCASL) MRI enables noninvasive quantitative perfusion mapping without contrast agent injection and with a higher signal-to-noise ratio (SNR) than alternative methods. Despite its great potential, pCASL remains challenging, unstable, and relatively low-resolution in rodents - especially in mice - thereby limiting the investigation of perfusion properties in many transgenic or other relevant rodent models of disease. Here, we address this gap by developing a novel experimental setup for high-resolution pCASL imaging in mice and combining it with the enhanced SNR of cryogenic probes. We show that our new experimental setup allows for optimal positioning of the carotids within the cryogenic coil, rendering labeling reproducible. With the proposed methodology, we managed to increase the spatial resolution of pCASL perfusion images by a factor of 15 in mice; a factor of 6 in rats is gained compared to the state of the art just by virtue of the cryogenic coil. We also show that the improved pCASL perfusion imaging allows much better delineation of specific brain areas, both in healthy animals as well as in rat and mouse models of stroke. Our results bode well for future high-definition pCASL perfusion imaging in rodents.

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.

Visualizing neuroimaging data is a key step in evaluating data quality, interpreting results, and communicating findings. This survey focuses on diffusion MRI tractography, which has been widely used in both research and clinical domains within the neuroimaging community. With an increasing number of tractography tools and software, navigating this landscape poses a challenge, especially for newcomers. A systematic exploration of a diverse range of features is proposed across 27 research tools, delving into their main purpose and examining the presence or absence of prevalent visualization and interactive techniques. The findings are structured within a proposed taxonomy, providing a comprehensive overview. Insights derived from this analysis will help (novice) researchers, clinicians, and developers in identifying knowledge gaps and navigating the landscape of tractography visualization tools.

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.