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Permeability characterizes how saturating fluids penetrate and flow through porous rocks. It is an important quantity that can be used to model the behavior of reservoir rocks and to optimize underground resource exploration. Permeability can be characterized via nuclear magnetic resonance (NMR) relaxometry techniques by utilizing the yielded pore volume and pore length scales. However, for heterogeneous rocks a simple estimation of a permeability value based on the existing models may be inadequate. In this work, a new relaxation technique is applied to rock cores in conjunction with magnetic resonance imaging schemes. This allows the interior structures of studied rock cores to be recorded along a chosen sample axis. Based on the spatially resolved relaxation maps obtained, a factor of local connectivity was introduced for the first time and calculated by using the correlation degree between adjacent relaxation time distributions originating from neighboring image slices. Consequently, a permeability profile was estimated by consecutively considering the local porosity, the spatially resolved relaxation time distribution, and the connectivity factor. Experimental results prove that permeability profiles trace the heterogeneity of rock samples. Furthermore, averaged permeabilities of rock cores were calculated while taking into account local connectivity. The values obtained approach better to brine‐permeability measurements as compared to data processing disregarding connectivity. Key Points: A rapid, nondestructive method was proposed to estimate rock permeabilityRock heterogeneity can be visualized by using obtained permeability profilesThe obtained permeability values from the proposed technique were comparable to conventional approach [ABSTRACT FROM AUTHOR]

Complex materials are ubiquitous in science, engineering and nature. One important parameter for characterising their morphology is the degree of anisotropy. Magnetic resonance imaging offers non-invasive methods for quantitative measurements of the materials anisotropy, most commonly via diffusion tensor imaging and the subsequent extraction of the spatially resolved fractional anisotropy (FA) value. Here, we propose an alternative way of determining the FA as a sample average for cases where spatially resolved methods are not needed or not applicable. It is based on a particular diffusion-diffusion correlation spectroscopy protocol, allowing for the extraction of the mean (i.e. sample averaged) FA value. We demonstrate that mean FA values obtained from three anisotropic biological tissues are consistent with those extracted using diffusion tensor imaging. Moreover, we show that differences of mean FA values in healthy and tumour-bearing mouse brains allow to distinguish these tissue types. We anticipate that the proposed method will be beneficial in the wider context of medical and material science. [ABSTRACT FROM AUTHOR]

Iron nanoparticles are highly-effective magnetic nanoparticles for T 2 magnetic resonance imaging (MRI). However, the stability of their magnetic properties is dependent on good protection of the iron core from oxidation in aqueous media. Here we report the synthesis of custom-synthesized phosphonate-grafted polyelectrolytes (PolyM3) of various chain lengths, for efficient coating of iron nanoparticles with a native iron oxide shell. The size of the nanoparticle-polyelectrolyte assemblies was investigated by transmission electron microscopy and dynamic light scattering, while surface attachment was confirmed by Fourier transform infrared spectroscopy. Low cytotoxicity was observed for each of the nanoparticle-polyelectrolyte (“Fe-PolyM3”) assemblies, with good cell viability (>80%) remaining up to 100 μg mL −1 Fe in HeLa cells. When applied in T 2 -weighted MRI, corresponding T 2 relaxivities ( r 2 ) of the Fe-PolyM3 assemblies were found to be dependent on the chain length of the polyelectrolyte. A significant increase in contrast was observed when polyelectrolyte chain length was increased from 6 to 65 repeating units, implying a critical chain length required for stabilization of the α-Fe nanoparticle core. [ABSTRACT FROM AUTHOR]

Background: New daily persistent headache (NDPH) is a rare primary headache disorder characterized by daily and persistent sudden onset headaches. Specific abnormalities in gray matter and white matter structure are associated with pain, but have not been well studied in NDPH. The objective of this work is to explore the fiber tracts and structural connectivity, which can help reveal unique gray and white matter structural abnormalities in NDPH., Methods: The regional radiomics similarity networks were calculated from T1 weighted (T1w) MRI to depict the gray matter structure. The fiber connectivity matrices weighted by diffusion metrics like fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD) were built, meanwhile the fiber tracts were segmented by anatomically-guided superficial fiber segmentation (Anat-SFSeg) method to explore the white matter structure from diffusion MRI. The considerable different neuroimaging features between NDPH and healthy controls (HC) were extracted from the connectivity and tract-based analyses. Finally, decision tree regression was used to predict the clinical scores (i.e. pain intensity) from the above neuroimaging features., Results: T1w and diffusion MRI data were available in 51 participants after quality control: 22 patients with NDPH and 29 HCs. Significantly decreased morphological similarity was found between the right superior frontal gyrus and right hippocampus. The superficial white matter (SWM) showed significantly decreased FA in fiber tracts including the right superficial-frontal, left superficial-occipital, bilateral superficial-occipital-temporal (Sup-OT) and right superficial-temporal, meanwhile significant increased RD was found in the left Sup-OT. For the fiber connectivity, NDPH showed significantly decreased FA in the bilateral basal ganglion and temporal lobe, increased MD in the right frontal lobe, and increased RD in the right frontal lobe and left temporal-occipital lobe. Clinical scores could be predicted dominantly by the above significantly different neuroimaging features through decision tree regression., Conclusions: Our research indicates the structural abnormalities of SWM and the neural pathways projected between regions like right hippocampus and left caudate nucleus, along with morphological similarity changes between the right superior frontal gyrus and right hippocampus, constitute the pathological features of NDPH. The decision tree regression demonstrates correlations between these structural changes and clinical scores., (© 2024. The Author(s).)

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.