scholarly journals Mouse livers machine learning identification based on hyperspectral x-ray computed tomography reconstructed x-ray absorption spectra

AIP Advances ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 115009
Author(s):  
Zheng Fang ◽  
Shuo Zhong ◽  
Weifeng Hu ◽  
Siyuan Cheng
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Absorption Spectra ◽  
X Ray ◽  
X Ray Computed ◽  
X Ray Absorption

scholarly journals Luminescence and X-ray Absorption Properties of Uniform Eu3+:(H3O)Lu3F10 Nanoprobes

Nanomaterials ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 1153 ◽  
Author(s):  
Daniel González-Mancebo ◽  
Ana Isabel Becerro ◽  
Ariadna Corral ◽  
Marcin Balcerzyk ◽  
Manuel Ocaña
Keyword(s):  
Computed Tomography ◽  
Emission Spectra ◽  
Luminescent Properties ◽  
Lanthanide Ions ◽  
Absorption Properties ◽  
X Ray ◽  
Water Dispersible ◽  
X Ray Computed ◽  
X Ray Absorption ◽  
Fluoride Nanoparticles

Due to the high atomic number of lutetium and the low phonon energy of the fluoride matrix, Lu-based fluoride nanoparticles doped with active lanthanide ions are potential candidates as bioprobes in both X-ray computed tomography and luminescent imaging. This paper shows a method for the fabrication of uniform, water-dispersible Eu3+:(H3O)Lu3F10 nanoparticles doped with different Eu contents. Their luminescent properties were studied by means of excitation and emission spectra as well as decay curves. The X-ray attenuation capacity of the phosphor showing the highest emission intensity was subsequently analyzed and compared with a commercial contrast agent. The results indicated that the 10% Eu3+-doped (H3O)Lu3F10 nanoparticles fabricated with the proposed polyol-based method are good candidates to be used as dual probes for luminescent imaging and X-ray computed tomography.

Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning

2020 ◽  
Vol 36 ◽  
pp. 101460
Author(s):  
Christian Gobert ◽  
Andelle Kudzal ◽  
Jennifer Sietins ◽  
Clara Mock ◽  
Jessica Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
X Ray ◽  
Computed Tomography Scans ◽  
X Ray Computed

scholarly journals Crystal structure, NIR luminescence and X-ray computed tomography of Nd3+:Ba0.3Lu0.7F2.7 nanospheres

2017 ◽  
Vol 46 (20) ◽  
pp. 6580-6587 ◽  
Author(s):  
Daniel González-Mancebo ◽  
Ana Isabel Becerro ◽  
Eugenio Cantelar ◽  
Fernando Cussó ◽  
Arnaud Briat ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Computed Tomography ◽  
X Ray ◽  
X Ray Computed ◽  
X Ray Absorption ◽  
Nir Luminescence

Uniform, hydrophilic Nd3+-doped Ba0.3Lu0.7F2.7 50 nm spheres are obtained in the absence of additives. Their NIR excitation and emission as well as their X-ray absorption make them ideal candidates as bioimaging probes.

scholarly journals Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

2020 ◽  
Vol 124 (15) ◽  
Author(s):  
Matthew R. Carbone ◽  
Mehmet Topsakal ◽  
Deyu Lu ◽  
Shinjae Yoo
Keyword(s):  
Machine Learning ◽  
Absorption Spectra ◽  
X Ray ◽  
Quantitative Accuracy ◽  
X Ray Absorption

scholarly journals A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

2021 ◽  
Vol 9 (B) ◽  
pp. 1283-1289
Author(s):  
Jane Aurelia ◽  
Zuherman Rustam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computed Tomography ◽  
Support Vector Machine ◽  
Support Vector ◽  
X Ray ◽  
Network Support ◽  
Computed Tomography Images ◽  
X Ray Computed

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.

scholarly journals Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning

2020 ◽  
Vol 34 ◽  
pp. 101183 ◽  
Author(s):  
Yunhui Zhu ◽  
Ziling Wu ◽  
W. Douglas Hartley ◽  
Jennifer M. Sietins ◽  
Christopher B. Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Computer Vision ◽  
Post Processing ◽  
X Ray ◽  
Pore Evolution ◽  
X Ray Computed ◽  
Binder Jetting
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