CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

Lee Sang Min; Lee June-Goo; Lee Gaeun; Choe Jooae; Do Kyung-Hyun; Kim Namkug; Seo Joon Beom

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CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

机译：CT通过使用卷积神经网络的不同重建内核之间的图像转换

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Objective: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram.

机译：目的：我们的研究目的是开发和验证卷积神经网络（CNN）架构，以将用一个内核重建的CT图像转换为具有不同重建内核的图像而不使用SINOGRAM。

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来源
《Nature reviews neuroscience》 |2019年第2期|共9页
作者
Lee Sang Min; Lee June-Goo; Lee Gaeun; Choe Jooae; Do Kyung-Hyun; Kim Namkug; Seo Joon Beom;
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作者单位

Univ Ulsan Coll Med Asan Med Ctr Dept Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea;

Univ Ulsan Coll Med Asan Med Ctr Dept Convergence Med Seoul South Korea;

Univ Ulsan Coll Med Asan Med Ctr Dept Convergence Med Seoul South Korea;

Univ Ulsan Coll Med Asan Med Ctr Dept Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea;

Univ Ulsan Coll Med Asan Med Ctr Dept Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea;

Univ Ulsan Coll Med Asan Med Ctr Dept Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea;

Univ Ulsan Coll Med Asan Med Ctr Dept Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea;

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原文格式 PDF
正文语种 eng
中图分类神经生理学;
关键词
Multidetector computed tomography; Image reconstruction; Machine learning; Emphysema; CNN;

机译：多传输科学的断层扫描;图像重建;机器学习;肺气肿;CNN;

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专利

1. CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network [J] . Lee Sang Min, Lee June-Goo, Lee Gaeun, Korean journal of radiology : . 2019,第2期

机译：卷积神经网络在不带汉字的不同重构核之间进行CT图像转换
2. Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction [J] . Hoyeon Lee, Jongha Lee, Hyeongseok Kim, IEEE Transactions on Radiation and Plasma Medical Sciences . 2019,第2期

机译：基于深度神经网络的Singram合成，用于稀疏CT图像重建
3. Convolutional neural networks for relevance feedback in content based image retrieval A Content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback [J] . Lorenzo Putzu, Luca Piras, Giorgio Giacinto Multimedia Tools and Applications . 2020,第37a38期

机译：基于内容的图像检索的相关反馈的卷积神经网络基于内容的图像检索系统，用于利用特征提取和相关性反馈的卷积神经网络
4. Sharpness preserved sinogram synthesis using convolutional neural network for sparse-view CT imaging [C] . Jiaxing Tan, Yongfeng Gao, Yumei Huo, Society of Photo-Optical Instrumentation Engineers;SPIE Medical Imaging Conference . 2019

机译：使用卷积神经网络进行清晰度保留的正弦图合成以进行稀疏CT成像
5. Convolutional Neural Networks in Application to Medical Image Analysis and Reconstruction [D] . Spuhler, Karl. 2019

机译：应用于医学图像分析与重建的卷积神经网络
6. CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network [O] . Sang Min Lee, June-Goo Lee, Gaeun Lee, 2019

机译：卷积神经网络在不带汉字的不同重构核之间进行CT图像转换
7. Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction [O] . Hoyeon Lee, Jongha Lee, Hyeongseok Kim, 2019

机译：基于深度网络的稀疏视图CT图像重建综合合成

CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

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