NMR in Biomedicine最新文献

The focus of this work was to identify the optimal magnetic resonance imaging (MRI) contrast between orthotopic U-87 MG tumours and normal appearing brain with the eventual goal of treatment response monitoring. U-87 MG human glioblastoma cells were injected into the brain of RNU nude rats (n = 9). The rats were imaged at 7 T at three timepoints for all animals: 3-5, 7-9, and 11-13 days after implantation. Whole-brain T₁-weighted (before and after gadolinium contrast agent injection), diffusion, and fluid-attenuated inversion recovery scans were performed. In addition, single-slice saturation-transfer-weighted chemical exchange saturation transfer (CEST), magnetization transfer (MT), and water saturation shift referencing (WASSR) contrast Z-spectra and T₁ and T₂ maps were also acquired. The MT and WASSR Z-spectra and T₁ map were fitted to a two-pool quantitative MT model to estimate the T₂ of the free and macromolecular-bound water molecules, the relative macromolecular pool size (M_{0, MT}), and the magnetization exchange rate from the macromolecular pool to the free pool (R_MT). The T₁-corrected apparent exchange-dependent relaxation (AREX) metric to isolate the CEST contributions was also calculated. The lesion on M_{0, MT} and AREX maps with a B₁ of 2 μT best matched the hyperintensity on the post-contrast T₁-weighted image. There was also good separation in Z-spectra between the lesion and contralateral cortex in the 2-μT CEST and 3- and 5-μT MT Z-spectra at all time points. A pairwise Wilcoxon signed-rank tests with Holm-Bonferroni adjustment on MRI parameters was performed and the differences between enhancing lesion and contralateral cortex for the MT ratio with 2 μT saturation at 3.6 ppm frequency offset (corresponding to the amide chemical group) and M_{0, MT} were both strongly significant (p < 0.001) at all time points. This work has identified that differences between enhancing lesion and contralateral cortex are strongest in MTR with B₁ = 2 μT at 3.6 ppm and relative macromolecular pool size (M_{0, MT}) images over entire period of 3-13 days after cancer cell implantation.

Baluns are crucial in MRI RF coils, essential for minimizing common-mode currents, maintaining signal-to-noise ratio, and ensuring patient safety. This paper introduces the innovative float solenoid balun, based on the renowned solenoid cable trap, and conducts a comparative analysis with the widely used float bazooka balun. Leveraging robust inductive coupling between the cable shield and float resonator, the float solenoid balun offers compact dimensions and post-installation adjustability. Through electromagnetic simulations and bench testing across static fields (1.5, 3, and 7 T), the float solenoid balun demonstrates superior common-mode rejection ratios compared to the float bazooka balun. Notably, its float design facilitates easy post-installation adjustment and eliminates the need for soldering on the cable shield, enhancing usability and reducing risks. Furthermore, the solenoid balun's compact footprint addresses the increasing demand for smaller baluns in modern MRI scanners with denser coil arrays. The float solenoid balun offers a promising solution by conserving valuable space within the RF coil, simplifying practical hardware implementation and cable routing, and accommodating more elements in RF arrays, with great potential for enhancing MRI performance.

Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T₁ and T₂) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T₁-weighted MPRAGE images ((1-mm)³ isotropic resolution) were acquired. Whole-brain water T₁ and T₂ measurements were made with DESPOT ((1.4 mm)³ isotropic resolution) at 3T. T₁ and T₂ maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T₁ and T₂ values were calculated for each parcel in each subject. Linear models of T₁ and T₂ as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T₁ and T₂ were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging.

Massively multidimensional diffusion magnetic resonance imaging combines tensor-valued encoding, oscillating gradients, and diffusion-relaxation correlation to provide multicomponent subvoxel parameters depicting some tissue microstructural features. This method was successfully implemented ex vivo in microimaging systems and clinical conditions with tensor-valued gradient waveform of variable duration giving access to a narrow diffusion frequency (ω) range. We demonstrate here its preclinical in vivo implementation with a protocol of 389 contrast images probing a wide diffusion frequency range of 18 to 92 Hz at b-values up to 2.1 ms/μm² enabled by the use of modulated gradient waveforms and combined with multislice high-resolution and low-distortion echo planar imaging acquisition with segmented and full reversed phase-encode acquisition. This framework allows the identification of diffusion ω-dependence in the rat cerebellum and olfactory bulb gray matter (GM), and the parameter distributions are shown to resolve two water pools in the cerebellum GM with different diffusion coefficients, shapes, ω-dependence, relaxation rates, and spatial repartition whose attribution to specific microstructure could modify the current understanding of the origin of restriction in GM.

This study investigated the association between the fatty acid composition of abdominal adipose tissue in NAFLD patients using chemical shift-encoded MRI and the development of insulin resistance and T2DM. We enrolled 231 subjects with NAFLD who underwent both abdominal magnetic resonance spectroscopy and chemical shift-encoded MRI: comprising of 49 T2DM patients and 182 subjects without. MRI- and MRS-based liver fat fraction was measured from a circular region of interest on the right lobe of the liver. The abdominal fatty acid compositions were measured at the umbilical level with chemical shift-encoded MRI. Bland-Altman analysis, Student's t test, Mann-Whitney U test, and Spearman correlation analysis were performed. The logistic regression was applied to identify the independent factors for T2DM. Then, the predictive performance was assessed by Receiver operating characteristic curve analyses. An excellent agreement was found between liver fat fraction measured by MRS and MRI. (slope = 0.8; bias =-0.92%). In, patients with T2DM revealed lower fractions of mono-unsaturated fatty acid (F_mufa) (33.68 ± 10.62 vs 38.62 ± 12.21, P =.0089) and higher fractions of saturated fatty acid (F_sfa) (34.11 ± 9.746 vs 31.25 ± 8.66, P =.0351) of visceral fat tissue compared with patients without. BMI, HDL-c, F_mufa and F_sfa of visceral fat were independent factors for T2DM. Furthermore, F_sfa-S% was positively correlated with liver enzyme levels (P =.003 and 0.04). However, F_mufa-V% was negatively correlated with fasting blood glucose, HbA1c and HOMA-IR (P =.004, P =.001 and P =.03 respectively). Hence, the evaluation of fatty acid compositions of abdominal fat tissue using chemical shift-encoded MRI may have a predictive value for T2DM in patients with NAFLD.

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.