Estimation of X-ray Energy Spectrum of Cone-Beam Computed Tomography Scanner Using Percentage Depth Dose Measurements and Machine Learning Approach

2021 ◽  
Vol 90 (7) ◽  
pp. 074801
Author(s):  
Yu Hasegawa ◽  
Akihiro Haga ◽  
Dousatsu Sakata ◽  
Yuki Kanazawa ◽  
Masahide Tominaga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Cone Beam ◽  
Learning Approach ◽  
X Ray ◽  
Percentage Depth Dose ◽  
Depth Dose ◽  
Machine Learning Approach ◽  
Dose Measurements ◽  
Computed Tomography Scanner

Prediction and understanding of soft proton contamination in XMM-Newton: a machine learning approach

2021 ◽  
Author(s):  
Elena Kronberg ◽  
Fabio Gastaldello ◽  
Stein Haaland ◽  
Artem Smirnov ◽  
Max Berrendorf ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Solar Wind ◽  
Southern Hemisphere ◽  
Solar Wind Speed ◽  
Flank Region ◽  
Learning Approach ◽  
X Ray ◽  
Machine Learning Approach ◽  
Field Lines

<p>One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes flying mainly in the magnetosphere are soft protons with few tens to hundreds of keV concentrated. One such telescope is the X-ray Multi-Mirror Mission (XMM-Newton) by ESA. Its observing time lost due to the contamination is  about 40%. This affects all the major broad science goals of XMM, ranging from cosmology to astrophysics of neutron stars and black holes. The soft proton background could dramatically impact future X-ray missions such Athena and SMILE missions. Magnetopsheric processes that trigger this background are still poorly understood. We use a machine learning approach to delineate related important parameters and to develop a model to predict the background contamination using 12 years of XMM observations. As predictors we use the location of XMM, solar and geomagnetic activity parameters. We revealed that the contamination is most strongly related to the distance in southern direction, ZGSE, (XMM observations were in the southern hemisphere), the solar wind velocity and the location on the magnetospheric magnetic field lines. We derived simple empirical models for the best two individual predictors and a machine learning model which utilizes an ensemble of the predictors (Extra Trees Regressor) and gives better performance. Based on our analysis, future X-Ray missions in the magnetosphere should minimize observations during  times  associated with high solar wind speed  and avoid closed magnetic field lines, especially at the dusk flank region at least in the southern hemisphere. </p>

scholarly journals A cone-beam X-ray computed tomography data collection designed for machine learning

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Henri Der Sarkissian ◽  
Felix Lucka ◽  
Maureen van Eijnatten ◽  
Giulia Colacicco ◽  
Sophia Bethany Coban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Image Reconstruction ◽  
Data Collection ◽  
Cone Angle ◽  
Open Data ◽  
Cone Beam ◽  
Projection Data ◽  
X Ray ◽  
Artefact Reduction

Abstract Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation.

scholarly journals A machine-learning approach for computation of fractional flow reserve from coronary computed tomography

2016 ◽  
Vol 121 (1) ◽  
pp. 42-52 ◽  
Author(s):  
Lucian Itu ◽  
Saikiran Rapaka ◽  
Tiziano Passerini ◽  
Bogdan Georgescu ◽  
Chris Schwemmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Fractional Flow Reserve ◽  
Learning Approach ◽  
Fractional Flow ◽  
Coronary Computed Tomography ◽  
Flow Reserve ◽  
Machine Learning Approach

scholarly journals A Prediction of Corona Disease Transmission Using A Traditional Machine Learning Approach

2020 ◽  
pp. 447-452
Author(s):  
Chandran Venkatesan ◽  
Elakkiya Balan ◽  
Sumithra M G ◽  
Karthick A ◽  
Jayarajan V ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Transition Process ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
X Ray ◽  
Machine Learning Approach ◽  
Current Scenario ◽  
Chest X Ray ◽  
Bayes Algorithm

In this current scenario, covid pandemic breaks analysis is becoming popular among the researchers. The various data sources from the different countries analyzed to predict the possibility of coronavirus transition from one person to another person. The datasets are not providing more information about the causes of the corona. Many authors provided the solution by using chest X-ray and CT images to predict the corona. In this paper, the covid pandemic transition process from one person to another person was classified using traditional machine learning algorithms. The input labels are encoded and transformed, utilizing the label encoder technique. The XG boost algorithm was outperformed all the other algorithms with overall accuracy and F1-measure of 99%. The Naive Bayes algorithm provides 100% accuracy, precision, recall, and F1-Score due to its improved ability to handle lower datasets.

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