Low-dose computed tomography image reconstruction via structure tensor total variation regularization

Junfeng Wu; Xuanqin Mou; Yongyi Shi; Ti Bai; Yang Chen

doi:10.1117/12.2293266

Low-dose computed tomography image reconstruction via structure tensor total variation regularization

Junfeng Wu, Xuanqin Mou, Yongyi Shi, Ti Bai, Yang Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

The X-ray computer tomography (CT) scanner has been extensively used in medical diagnosis. How to reduce radiation dose exposure while maintain high image reconstruction quality has become a major concern in the CT field. In this paper, we propose a statistical iterative reconstruction framework based on structure tensor total variation regularization for low dose CT imaging. An accelerated proximal forward-backward splitting (APFBS) algorithm is developed to optimize the associated cost function. The experiments on two physical phantoms demonstrate that our proposed algorithm outperforms other existing algorithms such as statistical iterative reconstruction with total variation regularizer and filtered back projection (FBP).

Original language	English (US)
Title of host publication	Medical Imaging 2018
Subtitle of host publication	Physics of Medical Imaging
Editors	Taly Gilat Schmidt, Guang-Hong Chen, Joseph Y. Lo
Publisher	SPIE
ISBN (Electronic)	9781510616356
DOIs	https://doi.org/10.1117/12.2293266
State	Published - 2018
Externally published	Yes
Event	Medical Imaging 2018: Physics of Medical Imaging - Houston, United States Duration: Feb 12 2018 → Feb 15 2018

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	10573
ISSN (Print)	1605-7422

Other

Other	Medical Imaging 2018: Physics of Medical Imaging
Country/Territory	United States
City	Houston
Period	2/12/18 → 2/15/18

Keywords

Low dose CT
proximal forward-backward splitting
structure tensor total variation

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Biomaterials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging

Access to Document

10.1117/12.2293266

Cite this

Wu, J., Mou, X., Shi, Y., Bai, T., & Chen, Y. (2018). Low-dose computed tomography image reconstruction via structure tensor total variation regularization. In T. G. Schmidt, G.-H. Chen, & J. Y. Lo (Eds.), Medical Imaging 2018: Physics of Medical Imaging Article 105733D (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10573). SPIE. https://doi.org/10.1117/12.2293266

Low-dose computed tomography image reconstruction via structure tensor total variation regularization. / Wu, Junfeng; Mou, Xuanqin; Shi, Yongyi et al.
Medical Imaging 2018: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Guang-Hong Chen; Joseph Y. Lo. SPIE, 2018. 105733D (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10573).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, J, Mou, X, Shi, Y, Bai, T & Chen, Y 2018, Low-dose computed tomography image reconstruction via structure tensor total variation regularization. in TG Schmidt, G-H Chen & JY Lo (eds), Medical Imaging 2018: Physics of Medical Imaging., 105733D, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10573, SPIE, Medical Imaging 2018: Physics of Medical Imaging, Houston, United States, 2/12/18. https://doi.org/10.1117/12.2293266

@inproceedings{8d32020f359e4bd4bafb608f14c5d8ca,

title = "Low-dose computed tomography image reconstruction via structure tensor total variation regularization",

abstract = "The X-ray computer tomography (CT) scanner has been extensively used in medical diagnosis. How to reduce radiation dose exposure while maintain high image reconstruction quality has become a major concern in the CT field. In this paper, we propose a statistical iterative reconstruction framework based on structure tensor total variation regularization for low dose CT imaging. An accelerated proximal forward-backward splitting (APFBS) algorithm is developed to optimize the associated cost function. The experiments on two physical phantoms demonstrate that our proposed algorithm outperforms other existing algorithms such as statistical iterative reconstruction with total variation regularizer and filtered back projection (FBP).",

keywords = "Low dose CT, proximal forward-backward splitting, structure tensor total variation",

author = "Junfeng Wu and Xuanqin Mou and Yongyi Shi and Ti Bai and Yang Chen",

note = "Funding Information: This work is supported by the Natural Science Foundation of China (NSFC) (61571359), Natural Science Basic Research Plan in Shaanxi Province of China (2016JK6065), Scientific Research Program Funded by Shaanxi Provincial Education Department of China (15JK1535) and by the Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education of China (K93-9-2017-04). Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Physics of Medical Imaging ; Conference date: 12-02-2018 Through 15-02-2018",

year = "2018",

doi = "10.1117/12.2293266",

language = "English (US)",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Schmidt, {Taly Gilat} and Guang-Hong Chen and Lo, {Joseph Y.}",

booktitle = "Medical Imaging 2018",

}

TY - GEN

T1 - Low-dose computed tomography image reconstruction via structure tensor total variation regularization

AU - Wu, Junfeng

AU - Mou, Xuanqin

AU - Shi, Yongyi

AU - Bai, Ti

AU - Chen, Yang

N1 - Funding Information: This work is supported by the Natural Science Foundation of China (NSFC) (61571359), Natural Science Basic Research Plan in Shaanxi Province of China (2016JK6065), Scientific Research Program Funded by Shaanxi Provincial Education Department of China (15JK1535) and by the Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education of China (K93-9-2017-04). Publisher Copyright: © 2018 SPIE.

PY - 2018

Y1 - 2018

N2 - The X-ray computer tomography (CT) scanner has been extensively used in medical diagnosis. How to reduce radiation dose exposure while maintain high image reconstruction quality has become a major concern in the CT field. In this paper, we propose a statistical iterative reconstruction framework based on structure tensor total variation regularization for low dose CT imaging. An accelerated proximal forward-backward splitting (APFBS) algorithm is developed to optimize the associated cost function. The experiments on two physical phantoms demonstrate that our proposed algorithm outperforms other existing algorithms such as statistical iterative reconstruction with total variation regularizer and filtered back projection (FBP).

AB - The X-ray computer tomography (CT) scanner has been extensively used in medical diagnosis. How to reduce radiation dose exposure while maintain high image reconstruction quality has become a major concern in the CT field. In this paper, we propose a statistical iterative reconstruction framework based on structure tensor total variation regularization for low dose CT imaging. An accelerated proximal forward-backward splitting (APFBS) algorithm is developed to optimize the associated cost function. The experiments on two physical phantoms demonstrate that our proposed algorithm outperforms other existing algorithms such as statistical iterative reconstruction with total variation regularizer and filtered back projection (FBP).

KW - Low dose CT

KW - proximal forward-backward splitting

KW - structure tensor total variation

UR - http://www.scopus.com/inward/record.url?scp=85049256590&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049256590&partnerID=8YFLogxK

U2 - 10.1117/12.2293266

DO - 10.1117/12.2293266

M3 - Conference contribution

AN - SCOPUS:85049256590

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2018

A2 - Schmidt, Taly Gilat

A2 - Chen, Guang-Hong

A2 - Lo, Joseph Y.

PB - SPIE

T2 - Medical Imaging 2018: Physics of Medical Imaging

Y2 - 12 February 2018 through 15 February 2018

ER -

Low-dose computed tomography image reconstruction via structure tensor total variation regularization

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this