Maldistribution and dynamic liquid holdup quantification of quadrilobe catalyst in a trickle bed reactor using gamma-ray computed tomography: Pseudo-3D modelling and empirical modelling using deep neural network

2020 ◽  
Vol 164 ◽  
pp. 195-208
Author(s):  
Binbin Qi ◽  
Omar Farid ◽  
Sebastián Uribe ◽  
Muthanna Al-Dahhan
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Neural Network ◽  
Gamma Ray ◽  
3D Modelling ◽  
Liquid Holdup ◽  
Trickle Bed Reactor ◽  
Empirical Modelling ◽  
Trickle Bed ◽  
Gamma Ray Computed Tomography

Gamma-ray computed tomography for scanning of agarwood tree: Data analysis and comparison

2020 ◽  
Author(s):  
Mohd Fitri Abdul Rahman ◽  
Lahasen Dahing ◽  
Muhamad Noor Izwan Ishak ◽  
Hearie Hassan ◽  
Nur Liyana Abdullah ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Data Analysis ◽  
Gamma Ray ◽  
Tree Data ◽  
Gamma Ray Computed Tomography

Automated vessel segmentation in lung CT and CTA images via deep neural networks

2021 ◽  
pp. 1-15
Author(s):  
Wenjun Tan ◽  
Luyu Zhou ◽  
Xiaoshuo Li ◽  
Xiaoyu Yang ◽  
Yufei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Vessel Segmentation ◽  
Multi Scale ◽  
Vascular Segmentation ◽  
Study Results ◽  
Lung Ct

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

Gamma-Ray Computed Tomography for Imaging of Multiphase Flows

2013 ◽  
Vol 85 (7) ◽  
pp. 1002-1011 ◽  
Author(s):  
André Bieberle ◽  
Hans-Ulrich Härting ◽  
Swapna Rabha ◽  
Markus Schubert ◽  
Uwe Hampel
Keyword(s):  
Computed Tomography ◽  
Multiphase Flows ◽  
Gamma Ray ◽  
Gamma Ray Computed Tomography

scholarly journals GAMMA-RAY COMPUTED TOMOGRAPHY (CT) TECHNIQUE FOR TREE STEM INVESTIGATION

2018 ◽  
Vol 12 (2) ◽  
pp. 73
Author(s):  
Bayu Azmi
Keyword(s):  
Computed Tomography ◽  
Gamma Ray ◽  
Parallel Beam ◽  
Ct Technique ◽  
Gamma Ray Computed Tomography ◽  
Tree Stem

TEKNIK COMPUTED TOMOGRAPHY (CT) SINAR GAMMA UNTUKINVESTIGASIBATANG POHON. Pepohonan memiliki peran yang penting dalam kehidupan manusia. Pohon-pohon dapat meningkatkan kualitas udara, menstabilkan temperatur, dan lain sebagainya. Akhir-akhir ini terdapat beberapa insiden pohon tumbang dan mengakibatkan korban jiwa. Investigasi batang pohon telah dilakukan dengan menggunakan teknik computed tomography (CT) sinar gamma untuk mempelajari kondisi bagian dalam dari batang pohon tersebut. Batang tersebut dipindai menggunakan tomografi generasi pertama yang disebut metode pemindaian parallel beam. 137Cs dengan aktivitas 80 mCi memancarkan foton gamma yang menembus batang pohon dan diterima oleh detektor sintilasi Nal(Tl) pada sisi lainnya. Kedua sumber radiasi gamma dan detektor dikolimasi menggunakan timah hitam dengan diameter celahnya sebesar 5 mm. Terdapat 128 data proyeksi yang kemudian direkonstruksi menjadi citra. Dibutuhkan waktu sekitar 522 menit untuk mendapatkan 128 data proyeksi tersebut. Citra hasil rekonstruksi menunjukkan bahwa terdapat variasi densitas dan dua lubang pada batang pohon tersebut dengan jelas. CT sinar gamma menjadi salah satu teknik yang menjanjikan untuk investigasi pohon. Pengembangan lebih lanjut dibutuhkan untuk mengurangi waktu pemindaian dan meningkatkan kualitas citra.

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