CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives

被引：19

作者：

Lei, Yiming ^{[1
]}

Niu, Chuang ^{[2
]}

Zhang, Junping ^{[1
]}

Wang, Ge ^{[2
]}

Shan, Hongming ^{[3
,4
]}

机构：

[1] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China

[2] Rensselaer Polytech Inst, Biomed Imaging Ctr, Ctr Biotechnol & Interdisciplinary Studies, Ctr Computat Innovat,Dept Biomed Engn, Troy, NY 12180 USA

[3] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, MOE Frontiers Ctr Brain Sci, Key Lab Computat Neurosci & Brain Inspired Intell, Shanghai 200433, Peoples R China

[4] Shanghai Ctr Brain Sci & Brain Inspired Technol, Shanghai 200031, Peoples R China

来源：

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES | 2024年 / 8卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Computed tomography; Noise reduction; Image denoising; Biomedical imaging; Task analysis; Deep learning; Image reconstruction; Computed tomography (CT); deep learning; image deblurring; image denoising; LOW-DOSE CT; GENERATIVE ADVERSARIAL NETWORK; ADMM ALGORITHM; SUPERRESOLUTION; NOISE; RECONSTRUCTION; MR; TRANSFORMER; DOMAIN;

D O I：

10.1109/TRPMS.2023.3341903

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：

This article reviews the deep learning methods for computed tomography image denoising and deblurring separately and simultaneously. Then, we discuss promising directions in this field, such as a combination with large-scale pretrained models and large language models. Currently, deep learning is revolutionizing medical imaging in a data-driven manner. With rapidly evolving learning paradigms, related algorithms and models are making rapid progress toward clinical applications.

引用

页码：153 / 172

页数：20

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