Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality

被引：39

作者：

Nakamura, Yuko ^{[1
]}

Higaki, Toru ^{[1
]}

Tatsugami, Fuminari ^{[1
]}

Honda, Yukiko ^{[1
]}

Narita, Keigo ^{[1
]}

Akagi, Motonori ^{[1
]}

Awai, Kazuo ^{[1
]}

机构：

[1] Hiroshima Univ, Diagnost Radiol, Hiroshima, Japan

来源：

JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY | 2020年 / 44卷 / 02期

关键词：

neural networks (computer); tomography; x-ray computed; machine learning; artificial intelligence; deep learning; CONVOLUTIONAL NEURAL-NETWORK; LOW TUBE VOLTAGE; ITERATIVE RECONSTRUCTION; ABDOMINAL CT; REDUCTION; ALGORITHM;

D O I：

10.1097/RCT.0000000000000928

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction.

引用

页码：161 / 167

页数：7

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