spatial sensitivity
Recently Published Documents

There are currently no noninvasive imaging methods available for astrogliosis mapping in the central nervous system despite its essential role in the response to injury, disease, and infection. We have developed a machine learning-based multidimensional MRI framework that provides a signature of astrogliosis, distinguishing it from normative brain at the individual level. We investigated ex vivo cortical tissue specimen derived from subjects who sustained blast induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathology. By performing a combined postmortem radiology and histopathology correlation study we found that astrogliosis induces microstructural changes that are robustly detected using our framework, resulting in MRI neuropathology maps that are significantly and strongly correlated with co-registered histological images of increased glial fibrillary acidic protein deposition. The demonstrated high spatial sensitivity in detecting reactive astrocytes at the individual level has great potential to significantly impact neuroimaging studies in diseases, injury, repair, and aging.

Abstract In this paper, aiming at the problem of the electrostatic sensor signal satisfying the gaussian distribution, the non-parametric kernel estimation method is introduced, and the electrode induction model of the electrostatic sensor is finally fitted by combining the goodness of fit and the simulation data samples. This model satisfies the gaussian distribution and the electrostatic signal satisfying the gaussian distribution is given in the theory. Maxwell simulation software was used to simulate the theoretical sensitivity of the electrostatic sensor and the axial and radial spatial sensitivity characteristics of different sensor parameters were obtained. Within a certain range, the relative permittivity of the insulating tube is also discussed. Finally, an insulating tube with a relative permittivity of 3 is selected as the material of the insulating tube. Finally, the experiment is carried out on the experimental equipment and the conclusions obtained in the article are confirmed.

Abstract This work aims to provide a way of performing parallel imaging with a single-channel variable-frequency resonant device. Different spatial sensitivity profiles required for SENSE reconstruction are achieved by switching between the device eigenmodes. A device capable of such switching is manufactured and several k-spaces are acquired using the device different eigenmodes. The k-spaces are then subject to downsampling to obtain the sensitivity profiles of each eigenmode and then - to perform SENSE-based reconstruction of the unaliased images with different acceleration factors.

Prostate cancer is a high-incidence cancer, often detected late in life. The prostate gland is an accessory gland that secretes citrate; an impaired citrate secretion reflects imbalances in the activity of enzymes in the TCA Cycle in mitochondria. Profiling studies on prostate tumours have identified significant metabolite, proteomic, and transcriptional modulations with an increased mitochondrial metabolic activity associated with localised prostate cancer. Here, we focus on the androgen receptor, c-Myc, phosphatase and tensin Homolog deleted on chromosome 10 (PTEN), and p53 as amongst the best-characterised genomic drivers of prostate cancer implicated in metabolic dysregulation and prostate cancer progression. We outline their impact on metabolic function before discussing how this may affect metabolite pools and in turn chromatin structure and the epigenome. We reflect on some recent literature indicating that mitochondrial mutations and OGlcNAcylation may also contribute to this crosstalk. Finally, we discuss the technological challenges of assessing crosstalk given the significant differences in the spatial sensitivity and throughput of genomic and metabolomic profiling approaches.

Borehole measurements of nuclear magnetic resonance (NMR) are routinely used to estimate in situ rock and fluid properties. Conventional NMR interpretation methods often neglect bed-boundary and layer-thickness effects in the calculation of fluid volumetric concentrations and NMR relaxation-diffusion correlations. Such effects introduce notable spatial averaging of intrinsic rock and fluid properties across thinly bedded formations or in the vicinity of boundaries between layers exhibiting large property contrasts. Forward modeling and inversion methods can mitigate the aforementioned effects and improve the accuracy of true layer properties in the presence of mud-filtrate invasion and borehole environmental effects across spatially complex formations. We have developed a fast and accurate algorithm to simulate borehole NMR measurements using the concept of spatial sensitivity functions (SSFs) that honor NMR physics and incorporate tool, borehole, and formation geometry. Tool sensitivity maps are derived from a 3D multiphysics forward model that couples NMR tool properties, magnetization evolution, and electromagnetic propagation. In addition, a multifluid relaxation model based on Brownstein-Tarr’s equation is introduced to estimate layer NMR porosity decays and relaxation-diffusion correlations from pore-size-dependent rock and fluid properties. The latter model is convolved with the SSFs to reproduce borehole NMR measurements. The results indicate that NMR spatial sensitivity is controlled by porosity, electrical conductivity, excitation pulse duration, and tool geometry. We benchmark and verify the SSF-derived forward approximation against 3D multiphysics simulations for a series of synthetic cases with variable bed thickness and petrophysical properties, and in the presence of mud-filtrate invasion in a vertical well. Results indicate that the approximation can be executed in a few seconds in a central processing unit, by a factor of 1000 times faster than rigorous multiphysics calculations, with maximum root-mean-square errors of 1%.