Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFull field X-ray Scatter Tomography
151
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
In X-ray imaging, photons are transmitted through and absorbed by the subject, but are also scattered in significant quantities. Previous attempts to use scattered photons for biological imaging used pencil or fan beam illumination. Here we present 3D X-ray Scatter Tomography using full-field illumination. Synchrotron imaging experiments were performed of a phantom and the chest of a juvenile rat. Transmitted and scattered photons were simultaneously imaged with separate cameras; a scientific camera directly downstream of the sample stage, and a pixelated detector with a pinhole imaging system placed at 45${}^circ$ to the beam axis. We obtained scatter tomogram feature fidelity sufficient for segmentation of the lung and major airways in the rat. The image contrast in scatter tomogram slices approached that of transmission imaging, indicating robustness to the amount of multiple scattering present in our case. This opens the possibility of augmenting full-field 2D imaging systems with additional scatter detectors to obtain complementary modes or to improve the fidelity of existing images without additional dose, potentially leading to single-shot or reduced-angle tomography or overall dose reduction for live animal studies.
rate research
Read More
A novel Positron Emission Tomography system, based on plastic scintillators, is being developed by the J-PET collaboration. In this article we present the simulation results of the scatter fraction, representing one of the parameters crucial for background studies defined in the NEMA-NU-2-2012 norm. We elaborate an event selection methods allowing to suppress events in which gamma quanta were scattered in the phantom or underwent the multiple scattering in the detector. The estimated scatter fraction for the single-layer J-PET scanner varies from 37% to 53% depending on the applied energy threshold.
A fundamental problem in X-ray Computed Tomography (CT) is the scatter due to interaction of photons with the imaged object. Unless corrected, scatter manifests itself as degradations in the reconstructions in the form of various artifacts. Scatter correction is therefore critical for reconstruction quality. Scatter correction methods can be divided into two categories: hardware-based; and software-based. Despite success in specific settings, hardware-based methods require modification in the hardware, or increase in the scan time or dose. This makes software-based methods attractive. In this context, Monte-Carlo based scatter estimation, analytical-numerical, and kernel-based methods were developed. Furthermore, data-driven approaches to tackle this problem were recently demonstrated. In this work, two novel physics-inspired deep-learning-based methods, PhILSCAT and OV-PhILSCAT, are proposed. The methods estimate and correct for the scatter in the acquired projection measurements. They incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it. They use a common deep neural network architecture and cost function, both tailored to the problem. Numerical experiments with data obtained by Monte-Carlo simulations of the imaging of phantoms reveal significant improvement over a recent purely projection-domain deep neural network scatter correction method.
Due to the energy-dependent nature of the attenuation coefficient and the polychromaticity of the X-ray source, beam hardening effect occurs when X-ray photons penetrate through an object, causing a nonlinear projection data. When a linear reconstruction algorithm, such as filtered backprojection, is applied to reconstruct the projection data, beam hardening artifacts which show as cupping and streaks are present in the CT image. The aim of this study was to develop a fast and accurate beam hardening correction method which can deal with beam hardening artifacts induced by multi-materials objects. Based on spectrum estimation, the nonlinear attenuation process of the X-ray projection was modeled by reprojecting a template image with the estimated polychromatic spectrum. The template images were obtained by segmenting the uncorrected into different components using a simple segmentation algorithm. Numerical simulations, experimental phantom data and animal data which were acquired on a modern diagnostic CT scanner (Discovery CT750 HD, GE Healthcare, WI, USA) and a modern C-Arm CT scanner (Artis Zee, Siemens Healthcare, Forchheim, Germany), respectively, were used to evaluate the proposed method. The results show the proposed method significantly reduced both cupping and streak artifacts, and successfully recovered the Hounsfield Units (HU) accuracy.
Optical coherence tomography angiography (OCTA) has been established as a powerful tool for investigating vascular diseases and is expected to become a standard of care technology. However, its widespread clinical usage is hindered by technical gaps such as limited field of view (FOV), lack of quantitative flow information, and suboptimal motion correction. Here we report a new imaging platform, termed spectrally extended line field (SELF) OCTA that provides advanced solutions to the above-mentioned challenges. SELF-OCTA breaks the speed limitations and achieves two-fold gain in FOV without sacrificing signal strength through parallel image acquisition. Towards quantitative angiography, the frequency flow imaging mechanism overcomes the imaging speed bottleneck by obviating the requirement for superfluous B-scans. In addition, the frequency flow imaging mechanism facilitates OCTA-data based motion tracking with overlap between adjacent line fields. Since it can be implemented in any existing OCT device without significant hardware modification or affecting existing functions, we expect that SELF-OCTA will make non-invasive, wide field, quantitative, and low-cost angiographic imaging available to larger patient populations.
The degradation mechanism in a sodium cell of a layered Na0.48Al0.03Co0.18Ni0.18Mn0.47O2 (NCAM) cathode with P3/P2 structure is investigated by revealing the changes in microstructure and composition upon cycling. The work aims to rationalize the gradual performance decay and the alteration of the electrochemical response in terms of polarization, voltage signature, and capacity loss. Spatial reconstructions of the electrode by X-ray computed tomography at the nanoscale supported by quantitative and qualitative analyses show fractures and deformations in the cycled layered metal-oxide particles, as well as inorganic side compounds deposited on the material. These irreversible morphological modifications reflect structural heterogeneities across the cathode particles due to formation of various domains with different Na+ intercalation degrees. Besides, X-ray photoelectron spectroscopy data suggest that the latter inorganic species in the cycled electrode are mainly composed of NaF, Na2O, and NaCO3 formed by parasitic electrolyte decomposition. The precipitation of these insulating compounds at the electrode/electrolyte interphase and the related structural stresses induced in the material lead to a decrease in cathode particle size and partial loss of electrochemical activity. The retention of the NCAM phase after cycling suggests that electrolyte upgrade may improve the performance of the cathode to achieve practical application for sustainable energy storage.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
