scholarly journals Development of X-ray Coherent Scatter Projection Imaging Systems

2021 ◽  
Author(s):  
Christopher Dydula
Keyword(s):  
Imaging Systems ◽  
Coherent Scatter ◽  
X Ray

scholarly journals Fluoroscopy-guided robotic biopsy intervention system

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Yusuf Özbek ◽  
Michael Vogele ◽  
Christian Plattner ◽  
Pedro Costa ◽  
Mario Griesser ◽  
...  
Keyword(s):  
Percutaneous Biopsy ◽  
Practical Experience ◽  
Imaging Systems ◽  
Robotic Platform ◽  
End Effector ◽  
X Ray ◽  
Needle Path ◽  
Custom Made ◽  
Fluoroscopic Imaging ◽  
Movement Parameters

AbstractFluoroscopy-guided percutaneous biopsy interventions are mostly performed with traditional free-hand technique. The practical experience of the surgeon influences the duration of the intervention and the radiation exposure for patients and him-/herself. Especially when the placement of heavy and long instruments in double oblique angles is required, manual techniques reach their technical limitations very fast. The system presented herein automatizes the needle positioning using only two 2D scans while the robotic platform guides the intervention. These two images were used to plan the needle pathway and to estimate the pose of the robot using a custom-made end-effector with embedded registration fiducials. The estimated pose was subsequently used to transfer the planed needle path to the robot’s coordinate system and finally to compute the movement parameters in order to align the robot with this plan. To evaluate the system, two phantoms with 11 different targets on it were developed. The targets were punctured, and the application accuracy was measured quantitatively. The solution achieved sub-millimetric accuracy for needle placement (min. 0.23, max. 1.04 in mm). Our approach combines the advantages of fluoroscopic imaging and ensures automatic needle alignment with highly reduced X-ray radiation. The proposed system shows promising potential to be a guidance platform that is easy to combine with available fluoroscopic imaging systems and provides valuable help to the physician in more difficult interventions.

scholarly journals Nanoimaging using soft X-ray and EUV laser-plasma sources

2018 ◽  
Vol 167 ◽  
pp. 03001 ◽  
Author(s):  
Przemyslaw Wachulak ◽  
Alfio Torrisi ◽  
Mesfin Ayele ◽  
Andrzej Bartnik ◽  
Joanna Czwartos ◽  
...  
Keyword(s):  
Laser Plasma ◽  
Material Science ◽  
Imaging Systems ◽  
Fibroblast Cells ◽  
Plasma Sources ◽  
Full Field ◽  
X Ray ◽  
Water Window ◽  
Imaging Results ◽  
New Research

In this work we present three experimental, compact desk-top imaging systems: SXR and EUV full field microscopes and the SXR contact microscope. The systems are based on laser-plasma EUV and SXR sources based on a double stream gas puff target. The EUV and SXR full field microscopes, operating at 13.8 nm and 2.88 nm wavelengths are capable of imaging nanostructures with a sub-50 nm spatial resolution and short (seconds) exposure times. The SXR contact microscope operates in the “water-window” spectral range and produces an imprint of the internal structure of the imaged sample in a thin layer of SXR sensitive photoresist. Applications of such desk-top EUV and SXR microscopes, mostly for biological samples (CT26 fibroblast cells and Keratinocytes) are also presented. Details about the sources, the microscopes as well as the imaging results for various objects will be presented and discussed. The development of such compact imaging systems may be important to the new research related to biological, material science and nanotechnology applications.

scholarly journals Truncated Inception Net: COVID-19 Outbreak Screening using Chest X-rays

2020 ◽  
Author(s):  
Dipayan Das ◽  
KC Santosh ◽  
Umapada Pal
Keyword(s):  
Neural Network ◽  
World Wide ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
X Ray Imaging ◽  
Proposed Model ◽  
Different Types

Abstract Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in less than a couple of months, and the infection, caused by SARS-CoV-2, is spreading at an unprecedented rate. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans and chest X-rays (CXRs). In this paper, motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems, a deep learning-based Convolutional Neural Network (CNN) model, which we call Truncated Inception Net, is proposed to screen COVID-19 positive CXRs from other non-COVID and/or healthy cases. To validate our proposal, six different types of datasets were employed by taking the following CXRs: COVID-19 positive, Pneumonia positive, Tuberculosis positive, and healthy cases into account. The proposed model achieved an accuracy of 99.96% (AUC of 1.0) in classifying COVID- 19 positive cases from combined Pneumonia and healthy cases. Similarly, it achieved an accuracy of 99.92% (AUC of 0.99) in classifying COVID-19 positive cases from combined Pneumonia, Tuberculosis and healthy CXRs. To the best of our knowledge, as of now, the achieved results outperform the existing AI-driven tools for screening COVID-19 using CXRs.

scholarly journals Image Segmentation Techniques in Bone Structure Psychiatry

2020 ◽  
Vol 9 (07) ◽  
pp. 25102-25112
Author(s):  
Ajayi Olayinka Adedoyin ◽  
Olamide Timothy Tawose ◽  
Olu Sunday Adetolaju
Keyword(s):  
Image Segmentation ◽  
Bone Structure ◽  
Performance Comparison ◽  
Imaging Systems ◽  
Intrinsic Properties ◽  
X Ray ◽  
Practical Applications ◽  
X Ray Imaging ◽  
Segmentation Algorithms ◽  
Bone Structures

Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

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