NMR in Biomedicine最新文献

Articular cartilage (AC) is a specialized connective tissue that covers the ends of long bones and facilitates the load-bearing of joints. It consists of chondrocytes distributed throughout an extracellular matrix and organized into three zones: superficial, middle, and deep. Nuclear magnetic resonance (NMR) techniques can be used to characterize this layered structure. In this study, devoted to a better understanding of the NMR response of this complex tissue, 20 specimens excised from femoral and tibial cartilage of bovine samples were analyzed by the low-field single-sided NMR-MOUSE-PM10. A multiparametric depth-wise analysis was performed to characterize the laminar structure of AC and investigate the origin of the NMR dependence on depth. The depth dependence of the single parameters T₁, T₂, and D has been described in literature, but their simultaneous measurement has not been fully exploited yet, as well as the extent of their variability. A novel parameter, α, evaluated by applying a double-quantum-like sequence, has been measured. The significant decrease in T₁, T₂, and D from the middle to the deep zone is consistent with depth-dependent composition and structure changes of the complex matrix of fibers confining and interacting with water. The α parameter appears to be a robust marker of the layered structure with a well-reproducible monotonic trend across the zones. The discrimination of cartilage zones was reinforced by a multivariate principal component analysis statistical analysis. The large number of samples allowed for the identification of the smallest number of parameters or their combination able to classify samples. The first two components clustered the data according to the different zones, highlighting the sensitivity of the NMR parameters to the structural and compositional variations of AC. Using two parameters, the best result was obtained by considering T₁ and α. Single-sided NMR devices, portable and low-cost, provide information on NMR parameters related to tissue composition and structure.

Diffusion tensor imaging (DTI) provides insight into the skeletal muscle microstructure and can be acquired using a stimulated echo acquisition mode (STEAM)-based approach to quantify time-dependent tissue diffusion. This study examined diffusion metrics and signal-to-noise ratio (SNR) in the supraspinatus muscle obtained with a STEAM-DTI sequence with different diffusion encoding times (Δ) and compared them to measures from a spin echo (SE) sequence. Ten healthy subjects (mean age 31.5 ± 4.7 years; five females) underwent 3-Tesla STEAM and SE-DTI of the shoulder in three sessions. STEAM was acquired with Δ of 100/200/400/600 ms. The diffusion encoding time in SE scans was 19 ms (b = 500 s/mm²). Region of interest-based measurement of fractional anisotropy (FA), mean diffusivity (MD), and SNR was performed. Intraclass correlation coefficients (ICCs) were computed to assess test-retest reliability. ANOVA with post-hoc pairwise tests was used to compare measures between different Δ of STEAM as well as STEAM and SE, respectively. FA was significantly higher (FA_STEAM: 0.38-0.46 vs. FA_SE: 0.26) and MD significantly lower (MD_STEAM: 1.20-1.33 vs. MD_SE: 1.62 × 10^-3 mm²/s) in STEAM compared to SE (p < 0.001, respectively). SNR was significantly higher for SE (72.3 ± 8.7) than for STEAM (p < 0.001). ICCs were excellent for FA in STEAM (≥0.911) and SE (0.960). For MD, ICCs were good for STEAM_100ms-600ms (≥0.759) and SE (0.752). STEAM and SE exhibited excellent reliability for FA and good reliability for MD in the supraspinatus muscle. SNR was significantly higher in SE compared to STEAM.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful imaging technique sensitive to tissue molecular composition, pH, and metabolic processes in situ. CEST MRI uniquely probes the physical exchange of protons between water and specific molecules within tissues, providing a window into physiological phenomena that remain invisible to standard MRI. However, given the very low concentration (millimolar range) of CEST compounds, the effects measured are generally only on the order of a few percent of the water signal. Consequently, a few critical challenges, including correction of motion artifacts and magnetic field (B₀ and B₁ ⁺) inhomogeneities, have to be addressed in order to unlock the full potential of CEST MRI. Motion, whether from patient movement or inherent physiological pulsations, can distort the CEST signal, hindering accurate quantification. B₀ and B₁ ⁺ inhomogeneities, arising from scanner hardware imperfections, further complicate data interpretation by introducing spurious variations in the signal intensity. Without proper correction of these confounding factors, reliable analysis and clinical translation of CEST MRI remain challenging. Motion correction methods aim to compensate for patient movement during (prospective) or after (retrospective) image acquisition, reducing artifacts and preserving data quality. Similarly, B₀ and B₁ ⁺ inhomogeneity correction techniques enhance the spatial and spectral accuracy of CEST MRI. This paper aims to provide a comprehensive review of the current landscape of motion and magnetic field inhomogeneity correction methods in CEST MRI. The methods discussed apply to saturation transfer (ST) MRI in general, including semisolid magnetization transfer contrast (MTC) and relayed nuclear Overhauser enhancement (rNOE) studies.

Given the increasing global prevalence of metabolic syndrome, this study aimed to assess the potential of MRI-derived radiomics in noninvasively grading fibrosis. The study included 79 prospectively enrolled participants who had undergone MRE due to known or suspected liver disease between November 2022 and September 2023. Among them, 48 patients were diagnosed with histopathologically confirmed liver fibrosis. A total of 107 radiomic features per patient were extracted from MRI imaging. The dataset was then divided into training and test sets for model development and validation. Stepwise feature reduction was employed to identify the most relevant features and subsequently used to train a gradient-boosted tree model. The gradient-boosted tree model, trained on the training cohort with identified radiomic features to differentiate fibrosis grades, exhibited good performances, achieving AUC values from 0.997 to 0.998. In the independent test cohort of 24 patients, the radiomics model demonstrated AUC values ranging from 0.617 to 0.830, with the highest AUC of 0.830 (95% CI 0.520-0.830) for classifying fibrosis grade 2. Incorporating ADC values did not improve the model's performance. In conclusion, our study emphasizes the significant promise of using radiomics analysis on MRI images for noninvasively staging liver fibrosis. This method provides valuable insights into tissue characteristics and patterns, enabling a retrospective liver fibrosis severity assessment from nondedicated MRI scans.

Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning (DL) model to detect rigid motion in T₁-weighted brain images. We leveraged a 2D convolutional neural network (CNN) trained on motion-synthesized data for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed 93% agreement with the labeling of a radiologist a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is aimed at inline automatic detection of motion artifacts, accelerating part of the time-consuming quality assessment (QA) process and augmenting expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.

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.

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.

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.

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.