Search

Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Huabing Liu reports a relationship with Beijing Limecho Ltd that includes: employment., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Inside-out nuclear magnetic resonance (NMR) is a unique technique for investigating large in-situ objects outside of tools, to provide pore structure and pore-bearing fluids properties. However, in borehole, objects towards azimuthal orientations pose different properties, referred to as azimuthal spatial heterogeneity. This may lead to ambiguous evaluations by utilizing present inside-out NMR measurement, which hardly resolves azimuthal information and loses the location information of oil/gas. In this paper, we for the first time design and construct an innovative tool to investigate the heterogeneity of large in-situ samples. The most key component, array coil, which performs with azimuthal selection, measurement consistency and interactive isolation, configured in this novel tool to capture heterogeneity information. Whereas, strong coupling between neighboring coil elements largely decrease the coil sensitivity. Capacitive decoupling network is bridged into adjacent ports without segmenting coils to be decoupled and could be easily adjusted by electrical relays. The coil model and numerical simulation are firstly given to demonstrate the array coil configuration, B 1 field map and mutual coupling effects on coil sensitivity. Capacitive network is then introduced to be theoretically and practically analyzed to minimize coupling effects. Simulation and experimental results demonstrate that these coil elements have excellent consistency and independence to feasibly acquire the azimuthal NMR data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Measured nuclear magnetic resonance (NMR) transverse relaxation data in articular cartilage has been shown to be multi-exponential and correlated to the health of the tissue. The observed relaxation rates are dependent on experimental parameters such as solvent, data acquisition methods, data analysis methods, and alignment to the magnetic field. In this study, we show that diffusive exchange occurs in porcine articular cartilage and impacts the observed relaxation rates in T 1 -T 2 correlation experiments. By using time domain analysis of T 2 -T 2 exchange spectroscopy, the diffusive exchange time can be quantified by measurements that use a single mixing time. Measured characteristic times for exchange are commensurate with T 1 in this material and so impacts the observed T 1 behavior. The approach used here allows for reliable quantification of NMR relaxation behavior in cartilage in the presence of diffusive fluid exchange between two environments., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Recent compressed sensing techniques allow signal acquisition with less sampling than required by the Nyquist-Shannon theorem which reduces the data acquisition time in magnetic resonance imaging (MRI). However, prior knowledge becomes essential to reconstruct detailed features when the sampling rate is exceedingly low. In this work, one compressed sensing scheme developed in wireless sensing networks was adapted for the purpose of reconstructing magnetic resonance images by using one-dimensional principal component analysis (1D-PCA). Moreover, another related reconstruction method was proposed based on two-dimensional principal component analysis (2D-PCA). When comparing with one wavelet compressed sensing method, we demonstrate that these techniques are feasible and efficient at high undersampling rates., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The time-dependent apparent diffusion coefficient as measured by pulsed gradient NMR can be used to estimate parameters of porous structures including the surface-to-volume ratio and the mean curvature of pores. In this work, the short-time diffusion limit and in particular the influence of the temporal profile of diffusion gradients on the expansion as proposed by Mitra et al. (1993) is investigated. It is shown that flow-compensated waveforms, i.e. those whose first moment is zero, are blind to the term linear in observation time, which is the term that is proportional to mean curvature and surface permeability. A gradient waveform that smoothly interpolates between flow-compensated and bipolar waveform is proposed and the degree of flow-compensation is used as a second experimental dimension. This two-dimensional ansatz is shown to yield an improved precision when characterizing the confining domain. This technique is demonstrated with simulations and in experiments performed with cylindrical capillaries of 100 μm radius., (Copyright © 2015 Elsevier Inc. All rights reserved.)