NMR in Biomedicine最新文献

Understanding the effects of white matter (WM) axon fibre microstructure on T1 relaxation is important for neuroimaging. Here, we have studied the interrelationship between T1 and axon fibre configurations at 3T and 7T. T1 and S0 (=signal intensity at zero TI) were computed from MP2RAGE images acquired with six inversion recovery times. Multishell diffusion MRI images were analysed for fractional anisotropy (FA); MD; V1; the volume fractions for the first (f₁), second (f₂) and third (f₃) fibre configuration; and fibre density cross-section images for the first (fdc₁), second (fdc₂) and third (fdc₃) fibres. T1 values were plotted as a function of FA, f₁, f₂, f₃, fdc₁, fdc₂ and fdc₃ to examine interrelationships between the longitudinal relaxation and the diffusion MRI microstructural measures. T1 values decreased with increasing FA, f₁ and f₂ in a nonlinear fashion. At low FA values (from 0.2 to 0.4), a steep shortening of T1 was followed by a shallow shortening by 6%-10% at both fields. The steep shortening was associated with decreasing S0 and MD. T1 also decreased with increasing fdc₁ values in a nonlinear fashion. Instead, only a small T1 change as a function of either f₃ or fdc₃ was observed. In WM areas selected by fdc₁ only masks, T1 was shorter than in those with fdc₂/fdc₃. In WM areas with high single fibre populations, as delineated by f₁/fdc₁ masks, T1 was shorter than in tissue with high complex fibre configurations, as segmented by f₂/fdc₂ or f₃/fdc₃ masks. T1 differences between these WM areas are attributable to combined effects by T1 anisotropy and lowered FA. The current data show strong interrelationships between T1, axon fibre configuration and orientation in healthy WM. It is concluded that diffusion MRI microstructural measures are essential in the effort to interpret quantitative T1 images in terms of tissue state in health and disease.

Purpose: To develop and validate a data acquisition scheme combined with a motion-resolved reconstruction and dictionary-matching-based parameter estimation to enable free-breathing isotropic resolution self-navigated whole-liver simultaneous water-specific ${\text{T}}_{\text{1}}$ ( ${\text{wT}}_{\text{1}}$ ) and ${\text{T}}_{\text{2}}$ ( ${\text{wT}}_{\text{2}}$ ) mapping for the characterization of diffuse and oncological liver diseases.

Methods: The proposed data acquisition consists of a magnetization preparation pulse and a two-echo gradient echo readout with a radial stack-of-stars trajectory, repeated with different preparations to achieve different ${\text{T}}_{\text{1}}$ and ${\text{T}}_{\text{2}}$ contrasts in a fixed acquisition time of 6 min. Regularized reconstruction was performed using self-navigation to account for motion during the free-breathing acquisition, followed by water-fat separation. Bloch simulations of the sequence were applied to optimize the sequence timing for $B_1$ insensitivity at 3 T, to correct for relaxation-induced blurring, and to map ${\text{T}}_{\text{1}}$ and ${\text{T}}_{\text{2}}$ using a dictionary. The proposed method was validated on a water-fat phantom with varying relaxation properties and in 10 volunteers against imaging and spectroscopy reference values. The performance and robustness of the proposed method were evaluated in five patients with abdominal pathologies.

Results: Simulations demonstrate good $B_1$ insensitivity of the proposed method in measuring ${\text{T}}_{\text{1}}$

Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasets to fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information.

This work develops and evaluates a self-navigated variable density spiral (VDS)-based manifold regularization scheme to prospectively improve dynamic speech magnetic resonance imaging (MRI) at 3 T. Short readout duration spirals (1.3-ms long) were used to minimize sensitivity to off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a sensitivity-encoding-based nonlocal soft-weighted temporal regularization scheme. Our approach was compared with view-sharing, low-rank, temporal finite difference, extra dimension-based sparsity reconstruction constraints. Undersampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges was performed in a retrospective undersampling experiment on one volunteer. For prospective undersampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring was performed by three experts in voice research. Region of interest analysis at articulator boundaries was performed in both experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective undersampling, a spatial resolution of 2.4 × 2.4 mm²/pixel and a temporal resolution of 17.4 ms/frame for single-slice imaging, and 52.2 ms/frame for concurrent three-slice imaging, were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared with all other reconstruction constraints. While it exhibited faint (nonsignificant) alias artifacts that were similar to temporal finite difference, it provided statistically significant improvements compared with the other constraints. In conclusion, the self-navigated manifold regularized scheme enabled robust high spatiotemporal resolution dynamic speech MRI at 3 T.

Maternal obesity and hyperglycemia are linked to an elevated risk for obesity, diabetes, and steatotic liver disease in the adult offspring. To establish and validate a noninvasive workflow for perinatal metabolic phenotyping, fixed neonates of common mouse strains were analyzed postmortem via magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS) to assess liver volume and hepatic lipid (HL) content. The key advantage of nondestructive MRI/MRS analysis is the possibility of further tissue analyses, such as immunohistochemistry, RNA extraction, and even proteomics, maximizing the data that can be gained per individual and therefore facilitating comprehensive correlation analyses. This study employed an MRI and ¹H-MRS workflow to measure liver volume and HL content in 65 paraformaldehyde-fixed murine neonates at 11.7 T. Liver volume was obtained using semiautomatic segmentation of MRI acquired by a RARE sequence with 0.5-mm slice thickness. HL content was measured by a STEAM sequence, applied with and without water suppression. T₁ and T₂ relaxation times of lipids and water were measured for respective correction of signal intensity. The HL content, given as CH₂/(CH₂ + H₂O), was calculated, and the intrasession repeatability of the method was tested. The established workflow yielded robust results with a variation of ~3% in repeated measurements for HL content determination. HL content measurements were further validated by correlation analysis with biochemically assessed triglyceride contents (R² = 0.795) that were measured in littermates. In addition, image quality also allowed quantification of subcutaneous adipose tissue and stomach diameter. The highest HL content was measured in C57Bl/6N (4.2%) and the largest liver volume and stomach diameter in CBA (53.1 mm³ and 6.73 mm) and NMRI (51.4 mm³ and 5.96 mm) neonates, which also had the most subcutaneous adipose tissue. The observed effects were independent of sex and litter size. In conclusion, we have successfully tested and validated a robust MRI/MRS workflow that allows assessment of morphology and HL content and further enables paraformaldehyde-fixed tissue-compatible subsequent analyses in murine neonates.

Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school-age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non-invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)-based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD-200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area.

The liver plays a central role in metabolic homeostasis, as exemplified by a variety of clinical disorders with hepatic and systemic metabolic disarrays. Of particular interest are the complex interactions between lipid and carbohydrate metabolism in highly prevalent conditions such as obesity, diabetes, and fatty liver disease. Limited accessibility and the need for invasive procedures challenge direct investigations in humans. Hence, noninvasive dynamic evaluations of glycolytic flux and steady-state assessments of lipid levels and composition are crucial for basic understanding and may open new avenues toward novel therapeutic targets. Here, three different MR spectroscopy (MRS) techniques that have been combined in a single interleaved examination in a 7T MR scanner are evaluated. ¹H-MRS and ¹³C-MRS probe endogenous metabolites, while deuterium metabolic imaging (DMI) relies on administration of deuterated tracers, currently ²H-labelled glucose, to map the spatial and temporal evolution of their metabolic fate. All three techniques have been optimized for a robust single-session clinical investigation and applied in a preliminary study of healthy subjects. The use of a triple-channel ¹H/²H/¹³C RF coil enables interleaved examinations with no need for repositioning. Short-echo-time STEAM spectroscopy provides well resolved spectra to quantify lipid content and composition. The relative benefits of using water saturation versus metabolite cycling and types of respiratory synchronization were evaluated. ²H-MR spectroscopic imaging allowed for registration of time- and space-resolved glucose levels following oral ingestion of ²H-glucose, while natural abundance ¹³C-MRS of glycogen provides a dynamic measure of hepatic glucose storage. For DMI and ¹³C-MRS, the measurement precision of the method was estimated to be about 0.2 and about 16 mM, respectively, for 5 min scanning periods. Excellent results were shown for the determination of dynamic uptake of glucose with DMI and lipid profiles with ¹H-MRS, while the determination of changes in glycogen levels by ¹³C-MRS is also feasible but somewhat more limited by signal-to-noise ratio.

Noninvasive extracellular pH (pH_e) mapping with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) using MR spectroscopic imaging (MRSI) has been demonstrated on 3T clinical MR scanners at 8 $\times 8\times 10$  mm³ spatial resolution and applied to study various liver cancer treatments. Although pH_e imaging at higher resolution can be achieved by extending the acquisition time, a postprocessing method to increase the resolution is preferable, to minimize the duration spent by the subject in the MR scanner. In this work, we propose to improve the spatial resolution of pH_e mapping with BIRDS by incorporating anatomical information in the form of multiparametric MRI and using an unsupervised deep-learning technique, Deep Image Prior (DIP). Specifically, we used high-resolution $T_1$ , $T_2$ , and diffusion-weighted imaging (DWI) MR images of rabbits with VX2 liver tumors as inputs to a U-Net architecture to provide anatomical information. U-Net parameters were optimized to minimize the difference between the output super-resolution image and the experimentally acquired low-resolution pH_e image using the mean-absolute error. In this way, the super-resolution pH_e image would be consistent with both anatomical MR images and the low-resolution pH_e measurement from the scanner. The method was developed based on data from 49 rabbits implanted with VX2 liver tumors. For evaluation, we also acquired high-resolution pH_e images from two rabbits, which were used as ground truth. The results indicate a good match between the spatial characteristics of the super-resolution images and the high-resolution ground truth, supported by the low pixelwise absolute error.

Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B₁ measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of ${\overrightarrow{B}}_1^{+}$ and ${\overrightarrow{B}}_1^{-}$ by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.

The decoupled 8 × 2 transceiver array has been shown to achieve a mean B₁ ⁺ of 11.7 uT with a coefficient of variation of ~11% over the intracranial brain volume for 7-T MR imaging. However, this array may be thought to give lower signal-to-noise ratio (SNR) and higher g-factors for parallel imaging compared with a radio frequency (RF) receive-only coil due to the latter's higher coil count and use of coil overlap to reduce the mutual impedance. Nonetheless, because the transceiver's highly decoupled design (pertinent for transmission) should also be constructive for reception, we measured the noise correlation, g-factors, and SNR for the decoupled transceiver in comparison with a commercial reference coil. We found that although the transceiver has half the number of receive elements in comparison with the reference coil (16 vs. 32), comparable g-factors and SNR over the head were obtained. From five subjects, the transceiver versus reference coil SNR was 65 ± 10 versus 67 ± 15. The mean noise correlation for all coil pairs was 10% ± 5% and 12% ± 9% (transceiver and reference coil, respectively). As changes in load impedance may alter the S parameters, we also examined the performance of the transceiver with tuned and matched (TM) versus untuned and unmatched (UTM) conditions on five subjects. We found that the noise correlation and SNR are robust to load variation; a noise correlation of 10% ± 5% and 10% ± 6% was determined with TM versus UTM conditions (SNR_UTM/SNR_TM = 0.97 ± 0.08). Finally, we demonstrate the performance of the array in human brain using T2-weighted turbo spin echo imaging, finding excellent SNR performance in both caudal and rostral brain regions.