NMR in Biomedicine最新文献

Although the information obtained from in vivo proton magnetic resonance spectroscopy (¹H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although it is difficult to measure the hippocampus because of its comparatively small size, location, and shape. This has been markedly improved by the advent of a clinically feasible 1-mm isotropic resolution 6-min DTI protocol at 3 T of the hippocampus with limited brain coverage of 20 axial-oblique slices aligned along its long axis. However, manual segmentation is too laborious for large population studies, and it cannot be automatically segmented directly on the diffusion images using traditional T₁ or T₂ image-based methods because of the limited brain coverage and different contrast. An automatic method is proposed here that segments the hippocampus directly on high-resolution diffusion images based on an extension of well-known deep learning architectures like UNet and UNet++ by including additional dense residual connections. The method was trained on 100 healthy participants with previously performed manual segmentation on the 1-mm DTI, then evaluated on typical healthy participants (n = 53), yielding an excellent voxel overlap with a Dice score of ~ 0.90 with manual segmentation; notably, this was comparable with the inter-rater reliability of manually delineating the hippocampus on diffusion magnetic resonance imaging (MRI) (Dice score of 0.86). This method also generalized to a different DTI protocol with 36% fewer acquisitions. It was further validated by showing similar age trajectories of volumes, fractional anisotropy, and mean diffusivity from manual segmentations in one cohort (n = 153, age 5-74 years) with automatic segmentations from a second cohort without manual segmentations (n = 354, age 5-90 years). Automated high-resolution diffusion MRI segmentation of the hippocampus will facilitate large cohort analyses and, in future research, needs to be evaluated on patient groups.

Dimethyl sulfoxide (DMSO) has wide biomedical applications such as cryoprotectant and hydrophobic drug carrier. Here, we report for the first time that DMSO can generate a distinctive chemical exchange saturation transfer (CEST) signal at around -2 ppm. Structural analogs of DMSO, including aprotic and protic solvents, also demonstrated CEST signals from -1.4 to -3.8 ppm. When CEST detectable barbituric acid (BA) was dissolved in DMSO solution and was co-loaded to liposome, two obvious peaks at 5 and -2 ppm were observed, indicating that DMSO and related solvent system can be monitored in a label-free manner via CEST, which can be further applied to imaging drug nanocarriers. With reference to previous studies, there could be molecular interactions or magnetization transfer pathways, such as the relayed nuclear Overhauser enhancement (rNOE), that lead to this detectable CEST contrast at negative offset frequencies of the Z-spectrum. Our findings suggest that small molecules of organic solvents could be involved in magnetization transfer processes with water and readily detected by CEST magnetic resonance imaging (MRI), providing a new avenue for detecting solvent-water and solvent-drug interactions.

Magnetic resonance imaging (MRI) was applied to determine the sex of polar cod (Boreogadus saida Lepechin, 1774) (Actinopterygii: Gadidae) and to follow the gonadal development in individual animals over time. Individual unanaesthetised fish were transferred to a measurement chamber inside a preclinical 9.4 T MRI scanner and continuously perfused with aerated seawater. A screening procedure at an average of 3.5 h, consisting of a set of MRI scans of different orientations, was repeated every 4 weeks on the same set of reproducing B. saida (n = 10) with a body length of about 20 cm. Adapted multi-slice flow-compensated fast low-angle shot (FcFLASH) and rapid acquisition with relaxation enhancement (RARE) protocols with an in-plane resolution of 313 μm and an acquisition time of 2.5 min were used to visualise the morphology of various organs, including the gonads within the field of view (FOV). The MR images provided high resolution, enabling specific sex determination, calculation of gonad volumes, and determination of oocyte sizes. Gonad maturation was followed over 4 months from November 2021 until shortly before spawning in February 2022. The gonad volume increased by 2.3-25.5% for males and by 11.5-760.7% for females during the observation period. From October to February, the oocyte diameter increased from 427 μm (n = 1) to 1346 ± 27 μm (n = 4). Interestingly, individual oocytes showed changes in MR contrast over time that can be attributed to the morphological development of the oocytes. The results fit well with previous literature data from classical invasive studies. The presented approach has great potential for various ecophysiological applications such as monitoring natural or delayed development of internal organs or sex determination under different environmental conditions.

Chemical exchange saturation transfer (CEST) MRI at 3 T suffers from low specificity due to overlapping CEST effects from multiple metabolites, while higher field strengths (B₀) allow for better separation of Z-spectral "peaks," aiding signal interpretation and quantification. However, data acquisition at higher B₀ is restricted by equipment access, field inhomogeneity and safety issues. Herein, we aim to synthesize higher-B₀ Z-spectra from readily available data acquired with 3 T clinical scanners using a deep learning framework. Trained with simulation data using models based on Bloch-McConnell equations, this framework comprised two deep neural networks (DNNs) and a singular value decomposition (SVD) module. The first DNN identified B₀ shifts in Z-spectra and aligned them to correct frequencies. After B₀ correction, the lower-B₀ Z-spectra were streamlined to the second DNN, casting into the key feature representations of higher-B₀ Z-spectra, obtained through SVD truncation. Finally, the complete higher-B₀ Z-spectra were recovered from inverse SVD, given the low-rank property of Z-spectra. This study constructed and validated two models, a phosphocreatine (PCr) model and a pseudo-in-vivo one. Each experimental dataset, including PCr phantoms, egg white phantoms, and in vivo rat brains, was sequentially acquired on a 3 T human and a 9.4 T animal scanner. Results demonstrated that the synthetic 9.4 T Z-spectra were almost identical to the experimental ground truth, showing low RMSE (0.11% ± 0.0013% for seven PCr tubes, 1.8% ± 0.2% for three egg white tubes, and 0.79% ± 0.54% for three rat slices) and high R² (>0.99). The synthesized amide and NOE contrast maps, calculated using the Lorentzian difference, were also well matched with the experiments. Additionally, the synthesis model exhibited robustness to B₀ inhomogeneities, noise, and other acquisition imperfections. In conclusion, the proposed framework enables synthesis of higher-B₀ Z-spectra from lower-B₀ ones, which may facilitate CEST MRI quantification and applications.

Purpose: Balanced steady-state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice-encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice-encoded calibration scan in addition to the 2D data, without being nearly as time-demanding as our previous method. This coil localization method is titled UNfolding Coil Localized Errors from an imperfect slice profile using a Structured Autocalibration Matrix (UNCLE SAM).

Methods: Retrospective and prospective evaluations were carried out. Both featured a 2D acquisition and a separate slice-encoded calibration of the center in-plane $k$ -space lines across all desired slice-encoding steps.

Results: Retrospective results featured a slice-by-slice comparison of the slice-encoded images with UNCLE SAM. UNCLE SAM's subtraction from the slice-encoded image was compared with a subtraction from the flow-corrupted 2D image, to demonstrate UNCLE SAM's capability to unfold outflowing spins. UNCLE SAM's comparison with slice encoding showed that UNCLE SAM was able to unfold up to 74% of what slice encoding achieved. Prospective results showed significant reduction in outflow effects with only a marginal increase in scan time from the 2D acquisition.

Conclusions: We developed a method that effectively unfolds most outflowing spins from corrupting the target slice and does not require the explicit use of slice-encoding gradients. This development offers a method to reduce most outflow effects from the target slice within a clinically feasible scan duration compared with the fully sampled slice-encoding technique.

Both inflow and the partial volume effect (PVE) are sources of error when measuring the arterial input function (AIF) in dynamic contrast-enhanced (DCE) MRI. This is relevant, as errors in the AIF can propagate into pharmacokinetic parameter estimations from the DCE data. A method was introduced for flow correction by estimating and compensating the number of the perceived pulse of spins during inflow. We hypothesized that the PVE has an impact on concentration-time curves similar to inflow. Therefore, we aimed to study the efficiency of this method to compensate for both effects simultaneously. We first simulated an AIF with different levels of inflow and PVE contamination. The peak, full width at half-maximum (FWHM), and area under curve (AUC) of the reconstructed AIFs were compared with the true (simulated) AIF. In clinical data, the PVE was included in AIFs artificially by averaging the signal in voxels surrounding a manually selected point in an artery. Subsequently, the artificial partial volume AIFs were corrected and compared with the AIF from the selected point. Additionally, corrected AIFs from the internal carotid artery (ICA), the middle cerebral artery (MCA), and the venous output function (VOF) estimated from the superior sagittal sinus (SSS) were compared. As such, we aimed to investigate the effectiveness of the correction method with different levels of inflow and PVE in clinical data. The simulation data demonstrated that the corrected AIFs had only marginal bias in peak value, FWHM, and AUC. Also, the algorithm yielded highly correlated reconstructed curves over increasingly larger neighbourhoods surrounding selected arterial points in clinical data. Furthermore, AIFs measured from the ICA and MCA produced similar peak height and FWHM, whereas a significantly larger peak and lower FWHM was found compared with the VOF. Our findings indicate that the proposed method has high potential to compensate for PVE and inflow simultaneously. The corrected AIFs could thereby provide a stable input source for DCE analysis.

Cerebrospinal fluid (CSF) circulation plays a key role in cerebral waste clearance via the glymphatic system. Although CSF flow velocity is an essential component of CSF dynamics, it has not been sufficiently characterized, and particularly, in studies of the glymphatic system in rat. To investigate the relationship between the flow velocity of CSF in the brain aqueduct and the glymphatic waste clearance rate, using phase-contrast MRI we performed the first measurements of CSF velocity in rats. Phase-contrast MRI was performed using a 7 T system to map mean velocity of CSF flow in the aqueduct in rat brain. The effects of age (3 months old versus 18 months old), gender, strain (Wistar, RNU, Dark Agouti), anesthetic agents (isoflurane versus dexmedetomidine), and neurodegenerative disorder (Alzheimer' disease in Fischer TgF344-AD rats, males and females) on CSF velocity were investigated in eight independent groups of rats (12 rats per group). Our results demonstrated that quantitative velocities of CSF flow in the aqueduct averaged 5.16 ± 0.86 mm/s in healthy young adult male Wistar rats. CSF flow velocity in the aqueduct was not altered by rat gender, strain, and the employed anesthetic agents in all rats, also age in the female rats. However, aged (18 months) Wistar male rats exhibited significantly reduced the CSF flow velocity in the aqueduct (4.31 ± 1.08 mm/s). In addition, Alzheimer's disease further reduced the CSF flow velocity in the aqueduct of male and female rats.

Native T1 mapping is a non-invasive technique used for early detection of diffused myocardial abnormalities, and it provides baseline tissue characterization. Post-contrast T1 mapping enhances tissue differentiation, enables extracellular volume (ECV) calculation, and improves myocardial viability assessment. Accurate and precise segmenting of the left ventricular (LV) myocardium on T1 maps is crucial for assessing myocardial tissue characteristics and diagnosing cardiovascular diseases (CVD). This study presents a deep learning (DL)-based pipeline for automatically segmenting LV myocardium on T1 maps and automatic computation of radial T1 and ECV values. The study employs a multicentric dataset consisting of retrospective multiparametric MRI data of 332 subjects to develop and assess the performance of the proposed method. The study compared DL architectures U-Net and Deep Res U-Net for LV myocardium segmentation, which achieved a dice similarity coefficient of 0.84 ± 0.43 and 0.85 ± 0.03, respectively. The dice similarity coefficients computed for radial sub-segmentation of the LV myocardium on basal, mid-cavity, and apical slices were 0.77 ± 0.21, 0.81 ± 0.17, and 0.61 ± 0.14, respectively. The t-test performed between ground truth vs. predicted values of native T1, post-contrast T1, and ECV showed no statistically significant difference (p > 0.05) for any of the radial sub-segments. The proposed DL method leverages the use of quantitative T1 maps for automatic LV myocardium segmentation and accurately computing radial T1 and ECV values, highlighting its potential for assisting radiologists in objective cardiac assessment and, hence, in CVD diagnostics.

In recent years, diffusion models have made significant progress in accelerating magnetic resonance imaging. Nevertheless, it still has inherent limitations, such as prolonged iteration times and sluggish convergence rates. In this work, we present a novel generalized map generation model based on mean-reverting SDE, called GM-SDE, to alleviate these shortcomings. Notably, the core idea of GM-SDE is optimizing the initial values of the iterative algorithm. Specifically, the training process of GM-SDE diffuses the original k-space data to an intermediary degraded state with fixed Gaussian noise, while the reconstruction process generates the data by reversing this process. Based on the generalized map, three variants of GM-SDE are proposed to learn k-space data with different structural characteristics to improve the effectiveness of model training. GM-SDE also exhibits flexibility, as it can be integrated with traditional constraints, thereby further enhancing its overall performance. Experimental results showed that the proposed method can reduce reconstruction time and deliver excellent image reconstruction capabilities compared to the complete diffusion-based method.