MLF-IOSC: Multi-Level Fusion Network With Independent Operation Search Cell for Low-Dose CT Denoising

被引：9

作者：

Shen, Jinbo ^{[1
]}

Luo, Mengting ^{[2
]}

Liu, Han ^{[1
]}

Liao, Peixi ^{[3
]}

Chen, Hu ^{[1
]}

Zhang, Yi ^{[4
]}

机构：

[1] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China

[2] Sichuan Univ, Natl Key Lab Fundamental Sci Synthet Vis, Chengdu 610065, Peoples R China

[3] Sixth Peoples Hosp Chengdu, Dept Sci Res & Educ, Chengdu 610065, Peoples R China

[4] Sichuan Univ, Sch Cyber Sci & Engn, Chengdu 610065, Peoples R China

来源：

IEEE TRANSACTIONS ON MEDICAL IMAGING | 2023年 / 42卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Computed tomography; Noise reduction; Convolution; Computer architecture; Laplace equations; Image reconstruction; Search problems; Low-dose CT; deep learning; neural architecture search; Laplacian;

D O I：

10.1109/TMI.2022.3224396

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Computed tomography (CT) is widely used in clinical medicine, and low-dose CT (LDCT) has become popular to reduce potential patient harm during CT acquisition. However, LDCT aggravates the problem of noise and artifacts in CT images, increasing diagnosis difficulty. Through deep learning, denoising CT images by artificial neural network has aroused great interest for medical imaging and has been hugely successful. We propose a framework to achieve excellent LDCT noise reduction using independent operation search cells, inspired by neural architecture search, and introduce the Laplacian to further improve image quality. Employing patch-based training, the proposed method can effectively eliminate CT image noise while retaining the original structures and details, hence significantly improving diagnosis efficiency and promoting LDCT clinical applications.

引用

页码：1145 / 1158

页数：14

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