Model-Based Calibration of a Robotic C-Arm System Using X-Ray Imaging

2018 ◽  
Vol 03 (03n04) ◽  
pp. 1841002 ◽  
Author(s):  
Sabine Thürauf ◽  
Oliver Hornung ◽  
Mario Körner ◽  
Florian Vogt ◽  
Alois Knoll ◽  
...  
Keyword(s):  
State Of The Art ◽  
Spatial Accuracy ◽  
System Behavior ◽  
X Ray ◽  
Additional Information ◽  
Cartesian Space ◽  
Model Based ◽  
X Ray Imaging ◽  
3D Volume ◽  
Non Linear System

In interventional radiology or surgery, C-arm systems are typical imaging modalities. Apart from 2D X-ray images, C-arm systems are able to perform 2D/3D overlays. For this application, a previously recorded 3D volume is projected on a 2D X-ray image for providing additional information to the clinician. The required accuracy for this application is 1.5[Formula: see text]mm. Such a spatial accuracy is only achievable with C-arms, if a calibration is performed. State-of-the-art approaches interpolate between values of lookup tables of a sampled Cartesian volume. However, due to the non-linear system behavior in Cartesian space, a trade-off between the calibration effort and the calibrated volume is necessary. This leads to the calibration of the most relevant subvolume and high calibration times. We discuss a new model-based calibration approach for C-arm systems which potentially leads to a smaller calibration effort and simultaneously to an increased calibrated volume. In this work, we demonstrate that it is possible to calibrate a robotic C-arm system using X-ray images and that a static model of the system is required to achieve the desired accuracy for 2D/3D overlays, if re-orientations of the system are performed.

scholarly journals Thomson linac-based X-ray generator: a primer for theory and design

2016 ◽  
Vol 34 (4) ◽  
pp. 637-644 ◽  
Author(s):  
I.A. Artyukov ◽  
E.G. Bessonov ◽  
M.V. Gorbunkov ◽  
Y.Y. Maslova ◽  
N.L. Popov ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Laser Radiation ◽  
Conceptual Design ◽  
Large Scale ◽  
State Of The Art ◽  
Thomson Scattering ◽  
Powerful Laser ◽  
X Ray ◽  
X Ray Imaging

AbstractThe paper presents a general theoretical framework and related Monte Carlo simulation of novel type of the X-ray sources based on relativistic Thomson scattering of powerful laser radiation. Special attention is paid to the linac X-ray generators by way of two examples: conceptual design for production of 12.4 keV photons and presently operating X-ray source of 29.4 keV photons. Our analysis shows that state-of-the-art laser and accelerator technologies enable to build up a compact linac-based Thomson source for the same X-ray imaging and diffraction experiments as in using of a large-scale X-ray radiation facility like a synchrotron or Thomson generator based on electron storage ring.

A model-based tracking method for measuring 3D dynamic joint motion using an alternating biplane x-ray imaging system

2018 ◽  
Vol 45 (8) ◽  
pp. 3637-3649 ◽  
Author(s):  
Cheng-Chung Lin ◽  
Jia-Da Li ◽  
Tung-Wu Lu ◽  
Mei-Ying Kuo ◽  
Chien-Chung Kuo ◽  
...  
Keyword(s):  
Imaging System ◽  
Joint Motion ◽  
Tracking Method ◽  
X Ray ◽  
Model Based ◽  
X Ray Imaging

scholarly journals Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6816
Author(s):  
Jannis N. Kahlen ◽  
Michael Andres ◽  
Albert Moser
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
Electrical Equipment ◽  
Diagnostic Systems ◽  
Additional Information ◽  
Model Based ◽  
Augmentation Techniques ◽  
Abnormal Condition

Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available.

Practical Implications of Using a Solid Electrolyte in Batteries with Sodium Anode: A Combined X-ray Tomography and Model-based Study

2019 ◽  
Author(s):  
Ronja Haas ◽  
Constantin Pompe ◽  
Markus Osenberg ◽  
André Hilger ◽  
Ingo Manke ◽  
...  
Keyword(s):  
Solid Electrolyte ◽  
Protective Layer ◽  
Nucleation Site ◽  
Dendrite Growth ◽  
X Ray ◽  
Model Based ◽  
X Ray Imaging ◽  
Practical Implications

Herein X-ray tomography of cycled symmetric sodium cells with Na-β-alumina is used to validate the success of a protective layer. X-ray imaging after cell failure reveals that the solid electrolyte unexpectedly broke apart alongside a crack. The crack might have acted as the nucleation site for sodium dendrite growth, which ended in short-circuiting and cell failure.<br><br>

scholarly journals Detection of Covid-19 with X-ray Images using Deep and Machine Learning

2021 ◽  
pp. 596-599
Author(s):  
Prof. Dr. Rajalakshmi M C ◽  
Bhuvana Sahi M ◽  
Bindu P ◽  
Pavan Kumar N ◽  
Pramod Athrey A
Keyword(s):  
Transfer Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Community ◽  
Network Architectures ◽  
X Rays ◽  
Community Based ◽  
X Ray ◽  
X Ray Imaging ◽  
Medical Image Classification

In this study, a dataset of X-ray images from patients with confirmed Covid -19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are Firstly, a collection of 5222 X-ray images including 3875 images with confirmed Covid -19 disease and 1347 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid -19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X -rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.

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