Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGrey matter sublayer thickness estimation in themouse cerebellum
41
0
0.0
(
0
)
Added by
Da Ma
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The cerebellar grey matter morphology is an important feature to study neurodegenerative diseases such as Alzheimers disease or Downs syndrome. Its volume or thickness is commonly used as a surrogate imaging biomarker for such diseases. Most studies about grey matter thickness estimation focused on the cortex, and little attention has been drawn on the morphology of the cerebellum. Using ex vivo high-resolution MRI, it is now possible to visualise the different cell layers in the mouse cerebellum. In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI. Application to mouse model of Downs syndrome found reduced cortical and layer thicknesses in the transchromosomic group.
rate research
Read More
We have implemented three different optical methods to quantitatively assess the thickness of thin GaSe flakes transferred on both transparent substrates, like Gel-Film, or SiO2/Si substrates. We show how their apparent color can be an efficient way to make a quick rough estimation of the thickness of the flakes. This method is more effective for SiO2/Si substrates as the thickness dependent color change is more pronounced on these substrates than on transparent substrates. On the other hand, for transparent substrates, the transmittance of the flakes in the blue region of the visible spectrum can be used to estimate the thickness. We find that the transmittance of flakes in the blue part of the spectrum decreases at a rate of 1.2%/nm. On SiO2/Si, the thickness of the flakes can be accurately determined by fitting optical contrast spectra to a Fresnel law-based model. Finally, we also show how the quantitative analysis of transmission mode optical microscopy images can be a powerful method to quickly probe the environmental degradation of GaSe flakes exposed to aging conditions.
We present a Bayesian probabilistic model to estimate the brain white matter atlas from high angular resolution diffusion imaging (HARDI) data. This model incorporates a shape prior of the white matter anatomy and the likelihood of individual observed HARDI datasets. We first assume that the atlas is generated from a known hyperatlas through a flow of diffeomorphisms and its shape prior can be constructed based on the framework of large deformation diffeomorphic metric mapping (LDDMM). LDDMM characterizes a nonlinear diffeomorphic shape space in a linear space of initial momentum uniquely determining diffeomorphic geodesic flows from the hyperatlas. Therefore, the shape prior of the HARDI atlas can be modeled using a centered Gaussian random field (GRF) model of the initial momentum. In order to construct the likelihood of observed HARDI datasets, it is necessary to study the diffeomorphic transformation of individual observations relative to the atlas and the probabilistic distribution of orientation distribution functions (ODFs). To this end, we construct the likelihood related to the transformation using the same construction as discussed for the shape prior of the atlas. The probabilistic distribution of ODFs is then constructed based on the ODF Riemannian manifold. We assume that the observed ODFs are generated by an exponential map of random tangent vectors at the deformed atlas ODF. Hence, the likelihood of the ODFs can be modeled using a GRF of their tangent vectors in the ODF Riemannian manifold. We solve for the maximum a posteriori using the Expectation-Maximization algorithm and derive the corresponding update equations. Finally, we illustrate the HARDI atlas constructed based on a Chinese aging cohort of 94 adults and compare it with that generated by averaging the coefficients of spherical harmonics of the ODF across subjects.
Dementia disorders are increasingly becoming sources of a broad range of problems, strongly interfering with normal daily tasks of a growing number of individuals. Such neurodegenerative diseases are often accompanied with progressive brain atrophy that, at late stages, leads to drastically reduced brain dimensions. At the moment, this structural involution can be followed with XCT or MRI measurements that share numerous disadvantages in terms of usability, invasiveness and costs. In this work, we aim to retrieve information concerning the brain atrophy stage and its evolution, proposing a novel approach based on non-invasive time-resolved Near Infra-Red (tr-NIR) measurements. For this purpose, we created a set of human-head atlases, in which we eroded the brain as it would happen in a clinical brain-atrophy progression. With these realistic meshes, we reproduced a longitudinal tr-NIR study exploiting a Monte-Carlo photon propagation algorithm to model the varying cerebral spinal fluid (CSF). The study of the time-resolved reflectance curve at late photon arrival times exhibited peculiar slope-changes upon CSF layer increase that were confirmed under several measurement conditions. The performance of the technique suggests good sensitivity to CSF variation, useful for a fast and non-invasive observation of the dementia progression.
This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness. For anisotropic medical volumes, there is a significant performance gap between thin-slice (mostly 1mm) and thick-slice (mostly 5mm) volumes. Prior arts tend to use 3D approaches for the thin-slice and 2D approaches for the thick-slice, respectively. We aim at a unified approach for both thin- and thick-slice medical volumes. Inspired by recent advances in video analysis, we propose AlignShift, a novel parameter-free operator to convert theoretically any 2D pretrained network into thickness-aware 3D network. Remarkably, the converted networks behave like 3D for the thin-slice, nevertheless degenerate to 2D for the thick-slice adaptively. The unified thickness-aware representation learning is achieved by shifting and fusing aligned virtual slices as per the input imaging thickness. Extensive experiments on public large-scale DeepLesion benchmark, consisting of 32K lesions for universal lesion detection, validate the effectiveness of our method, which outperforms previous state of the art by considerable margins without whistles and bells. More importantly, to our knowledge, this is the first method that bridges the performance gap between thin- and thick-slice volumes by a unified framework. To improve research reproducibility, our code in PyTorch is open source at https://github.com/M3DV/AlignShift.
We report on observations of a gamma-ray burst (GRB 061126) with an extremely bright (R ~ 12 mag at peak) early-time optical afterglow. The optical afterglow is already fading as a power law 22 seconds after the trigger, with no detectable prompt contribution in our first exposure, which was coincident with a large prompt-emission gamma-ray pulse. The optical--infrared photometric spectral energy distribution is an excellent fit to a power law, but it exhibits a moderate red-to-blue evolution in the spectral index at about 500 s after the burst. This color change is contemporaneous with a switch from a relatively fast decay to slower decay. The rapidly decaying early afterglow is broadly consistent with synchrotron emission from a reverse shock, but a bright forward-shock component predicted by the intermediate- to late-time X-ray observations under the assumptions of standard afterglow models is not observed. Indeed, despite its remarkable early-time brightness, this burst would qualify as a dark burst at later times on the basis of its nearly flat optical-to-X-ray spectral index. Our photometric spectral energy distribution provides no evidence of host-galaxy extinction, requiring either large quantities of grey dust in the host system (at redshift 1.1588 +/- 0.0006, based upon our late-time Keck spectroscopy) or separate physical origins for the X-ray and optical afterglows.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
