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.

SU-GG-I-98: Using MTF to Assess the Influence of X-Ray Focal Spot Blur on the Resolution of Fluoroscopic Imaging Systems

2010 ◽  
Vol 37 (6Part4) ◽  
pp. 3123-3124
Author(s):  
K Fetterly ◽  
D Magnuson ◽  
M Hindal ◽  
B Schueler
Keyword(s):  
Focal Spot ◽  
Imaging Systems ◽  
X Ray ◽  
Fluoroscopic Imaging

scholarly journals MODELLING ERRORS IN X-RAY FLUOROSCOPIC IMAGING SYSTEMS USING PHOTOGRAMMETRIC BUNDLE ADJUSTMENT WITH A DATA-DRIVEN SELF-CALIBRATION APPROACH

2018 ◽  
Vol XLII-1 ◽  
pp. 101-106
Author(s):  
J. C. K. Chow ◽  
D. D. Lichti ◽  
K. D. Ang ◽  
K. Al-Durgham ◽  
G. Kuntze ◽  
...  
Keyword(s):  
Bundle Adjustment ◽  
Imaging Systems ◽  
Data Driven ◽  
Video Frame ◽  
X Ray ◽  
Target Field ◽  
X Ray Imaging ◽  
Data Driven Approach ◽  
Fluoroscopic Imaging

<p><strong>Abstract.</strong> X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluoroscopic imaging can further acquire X-ray images at video frame rates, thus enabling non-invasive in-vivo motion studies of joints, gastrointestinal tract, etc. For both the qualitative and quantitative analysis of static and dynamic X-ray images, the data should be free of systematic biases. Besides precise fabrication of hardware, software-based calibration solutions are commonly used for modelling the distortions. In this primary research study, a robust photogrammetric bundle adjustment was used to model the projective geometry of two fluoroscopic X-ray imaging systems. However, instead of relying on an expert photogrammetrist’s knowledge and judgement to decide on a parametric model for describing the systematic errors, a self-tuning data-driven approach is used to model the complex non-linear distortion profile of the sensors. Quality control from the experiment showed that 0.06<span class="thinspace"></span>mm to 0.09<span class="thinspace"></span>mm 3D reconstruction accuracy was achievable post-calibration using merely 15 X-ray images. As part of the bundle adjustment, the location of the virtual fluoroscopic system relative to the target field can also be spatially resected with an RMSE between 3.10<span class="thinspace"></span>mm and 3.31<span class="thinspace"></span>mm.</p>

scholarly journals Semi-automated protein crystal mounting device for the sulfur single-wavelength anomalous diffraction method

2010 ◽  
Vol 43 (2) ◽  
pp. 341-346 ◽  
Author(s):  
Yu Kitago ◽  
Nobuhisa Watanabe ◽  
Isao Tanaka
Keyword(s):  
Diffraction Method ◽  
Systematic Errors ◽  
Protein Crystal ◽  
X Rays ◽  
Anomalous Diffraction ◽  
X Ray ◽  
Frozen Solution ◽  
Custom Made ◽  
Protein Molecules ◽  
X Ray Absorption

Use of longer-wavelength X-rays has advantages for the detection of small anomalous signals from light atoms, such as sulfur, in protein molecules. However, the accuracy of the measured diffraction data decreases at longer wavelengths because of the greater X-ray absorption. The capillary-top mounting method (formerly the loopless mounting method) makes it possible to eliminate frozen solution around the protein crystal and reduces systematic errors in the evaluation of small anomalous differences. However, use of this method requires custom-made tools and a large amount of skill. Here, the development of a device that can freeze the protein crystal semi-automatically using the capillary-top mounting method is described. This device can pick up the protein crystal from the crystallization drop using a micro-manipulator, and further procedures, such as withdrawal of the solution around the crystal by suction and subsequent flash freezing of the protein crystal, are carried out automatically. This device makes it easy for structural biologists to use the capillary-top mounting method for sulfur single-wavelength anomalous diffraction phasing using longer-wavelength X-rays.

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.

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