NMR in Biomedicine最新文献

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) arises from a physiological and physical cascade of events taking place at the level of the cortical microvasculature which constitutes a medium with complex geometry. Several analytical models of the BOLD contrast have been developed, but these have not been compared directly against detailed bottom-up modeling methods. Using a 3D modeling method based on experimentally measured images of mice microvasculature and Monte Carlo simulations, we quantified the accuracy of two analytical models to predict the amplitude of the BOLD response from 1.5 to 7 T, for different echo time (TE) and for both gradient echo and spin echo acquisition protocols. We also showed that accounting for the tridimensional structure of the microvasculature results in more accurate prediction of the BOLD amplitude, even if the values for SO₂ were averaged across individual vascular compartments. A secondary finding is that modeling the venous compartment as two individual compartments results in more accurate prediction of the BOLD amplitude compared with standard homogenous venous modeling, arising from the bimodal distribution of venous SO₂ across the microvasculature in our data.

Deployment of new, more portable, and less costly neuroimaging technologies such as portable magnetoencephalography, electroencephalography, positron emission tomography, functional near-infrared spectroscopy, high-density diffuse optical tomography, and magnetic resonance imaging is advancing rapidly. Given this trajectory toward increasing use of neuroimaging outside the hospital, we sought to identify ethical, legal, and societal implications (ELSI) of these new technologies by understanding the perspectives of those scientists and engineers developing and implementing portable neuroimaging technologies in the United States, Europe, and Asia. Based on a literature review, we identified and contacted 19 potential interviewees and then conducted 11 semi-structured interviews in English by Zoom. Analysis of the interviews revealed key themes and ELSI issues. Developers reported that without proper ELSI guidance, portable and accessible neuroimaging technology could be misused, fail to comply with applicable regulation and policy, and ultimately fall short in its mission to provide neuroimaging for the world. Our interviews suggested that ELSI guidance should address differences between imaging modalities because they vary in capability, limitations, and likelihood of generating incidental findings.

Slice-to-volume registration and super-resolution reconstruction are commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion among all input stacks as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Here, we presented an MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Experiments on linearly and randomly simulated motion illustrated that CP demonstrated higher sensitivity in detecting small motion with a lower baseline bias, and achieved a higher assessment accuracy of 95.45% in identifying the minimum motion stack, compared to the SVD-based method with 58.18%. CP also showed superior motion assessment capabilities in real-data evaluations. Additionally, combining CP with the existing SRR-SVR pipeline significantly improved 3D volume reconstruction. The results indicated that our proposed CP showed superior performance compared to SVD-based methods with higher sensitivity to motion, assessment accuracy, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.

This study aimed to investigate the metabolic changes in the kidneys in a murine adenine-diet model of chronic kidney disease (CKD). Kidney fibrosis is the common pathological manifestation across CKD aetiologies. Sustained inflammation and fibrosis cause changes in preferred energy metabolic pathways in the cells of the kidney. Kidney cortical tissue from mice receiving a control or adenine-supplemented diet for 8 weeks (late inflammation and fibrosis) and 12 weeks (8 weeks of treatment followed by 4 weeks recovery) were analysed by 2D-correlated nuclear magnetic resonance spectroscopy and compared with histopathology and biomarkers of kidney damage. Tissue metabolite and lipid levels were assessed using the MestreNova software. Expression of genes related to inflammation, fibrosis, and metabolism were measured using quantitative polymerase chain reaction. Animals showed indicators of severely impaired kidney function at 8 and 12 weeks. Significantly increased fibrosis was present at 8 weeks but not in the recovery group suggesting some reversal of fibrosis and amelioration of inflammation. At 8 weeks, metabolites associated with glycolysis were increased, while lipid signatures were decreased. Genes involved in fatty acid oxidation were decreased at 8 weeks but not 12 weeks while genes associated with glycolysis were significantly increased at 8 weeks but not at 12 weeks. In this murine model of CKD, kidney fibrosis was associated with the accumulation of triglyceride and free lactate. There was an up-regulation of glycolytic enzymes and down-regulation of lipolytic enzymes. These metabolic changes reflect the energy demands associated with progressive kidney disease where there is a switch from fatty acid oxidation to that of glycolysis.

The detection of a secondary inorganic phosphate (Pi) resonance, a possible marker of mitochondrial content in vivo, using phosphorus magnetic resonance spectroscopy (³¹P-MRS), poses technical challenges at 3 Tesla (T). Overcoming these challenges is imperative for the integration of this biomarker into clinical research. To evaluate the repeatability and reliability of measuring resting skeletal muscle alkaline Pi (Pi_alk) using with ³¹P-MRS at 3 T. After an initial set of experiments on five subjects to optimize the sequence, resting ³¹P-MRS of the quadriceps muscles were acquired on two visits (~4 days apart) using an intra-subjects design, from 13 sedentary to moderately active young male and female adults (22 ± 3 years old) within a whole-body 3 T MR system. Measurement variability attributed to changes in coil position, shimming procedure, and spectral analysis were quantified. ³¹P-MRS data were acquired with a ³¹P/-proton (¹H) dual-tuned surface coil positioned on the quadriceps using a pulse-acquire sequence. Test-retest absolute and relative repeatability was analyzed using the coefficient of variation (CV) and intra-class correlation coefficients (ICC), respectively. After sequence parameter optimization, Pi_alk demonstrated high intra-subject repeatability (CV: 10.6 ± 5.4%, ICC: 0.80). Proximo-distal change in coil position along the length of the quadriceps introduced Pi_alk quantitation variability (CV: 28 ± 5%), due to magnetic field inhomogeneity with more distal coil locations. In contrast, Pi_alk measurement variability due to repeated shims from the same muscle volume (0.40 ± 0.09mM; CV: 6.6%), and automated spectral processing (0.37 ± 0.01mM; CV: 2.3%), was minor. The quantification of Pi_alk in skeletal muscle via surface coil 31P-MRS at 3 T demonstrated excellent reproducibility. However, caution is advised against placing the coil at the distal part of the quadriceps to mitigate shimming inhomogeneity.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.