Second order total generalized variation for low-dose computed tomography image reconstruction

Shanzhou Niu; Jianhua Ma; Jing Huang; Zhaoying Bian; Zhengrong Liang; Wufan Chen

doi:10.1109/isbi.2014.6867837

Second order total generalized variation for low-dose computed tomography image reconstruction

Shanzhou Niu
, Jianhua Ma
, Jing Huang
, Zhaoying Bian
, Zhengrong Liang
, Wufan Chen

Southern Medical University
Stony Brook University

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

2 Scopus citations

Abstract

High radiation dose during x-ray computed tomography (CT) examinations can increase the risk of cancer and has become major concerns to patient. Accordingly, minimizing the radiation exposure without sacrificing image quality is a meaningful research topic. In this work, with the aim to reduce radiation during data acquisition, we propose a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating a total generalized variation (TGV) regularization, which is referred to as "PWLS-TGV". Specifically, the TGV regularization utilizes second-order derivatives of the desired image with imposing some higher order smoothness in regions away from the edges and the weighted leastsquares term considers a data-dependent variance estimation serving for improvement of image reconstruction from low-dose CT measurement. Subsequently, an alternating minimization algorithm was adopted to optimize the associative objective function. The experimental results on digital phantom and real patient data show that the present PWLS-TGV method can achieve significant gains over the existing similar methods in noise and artifacts suppression.

Original language	English
Title of host publication	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	173-176
Number of pages	4
ISBN (Electronic)	9781467319591
DOIs	https://doi.org/10.1109/isbi.2014.6867837
State	Published - 29 Jul 2014
Externally published	Yes
Event	11th IEEE International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: 29 Apr 2014 → 2 May 2014

Publication series

Name	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Conference

Conference	11th IEEE International Symposium on Biomedical Imaging, ISBI 2014
Country/Territory	China
City	Beijing
Period	29/04/14 → 2/05/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

CT
Penalized weighted least-squares
Total generalized variation
Total variation

Access to Document

10.1109/isbi.2014.6867837

Cite this

Niu, S., Ma, J., Huang, J., Bian, Z., Liang, Z., & Chen, W. (2014). Second order total generalized variation for low-dose computed tomography image reconstruction. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 173-176). Article 6867837 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6867837

@inproceedings{52ddad9efa8b47fca97da51337eab578,

title = "Second order total generalized variation for low-dose computed tomography image reconstruction",

abstract = "High radiation dose during x-ray computed tomography (CT) examinations can increase the risk of cancer and has become major concerns to patient. Accordingly, minimizing the radiation exposure without sacrificing image quality is a meaningful research topic. In this work, with the aim to reduce radiation during data acquisition, we propose a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating a total generalized variation (TGV) regularization, which is referred to as {"}PWLS-TGV{"}. Specifically, the TGV regularization utilizes second-order derivatives of the desired image with imposing some higher order smoothness in regions away from the edges and the weighted leastsquares term considers a data-dependent variance estimation serving for improvement of image reconstruction from low-dose CT measurement. Subsequently, an alternating minimization algorithm was adopted to optimize the associative objective function. The experimental results on digital phantom and real patient data show that the present PWLS-TGV method can achieve significant gains over the existing similar methods in noise and artifacts suppression.",

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author = "Shanzhou Niu and Jianhua Ma and Jing Huang and Zhaoying Bian and Zhengrong Liang and Wufan Chen",

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Niu, S, Ma, J, Huang, J, Bian, Z, Liang, Z & Chen, W 2014, Second order total generalized variation for low-dose computed tomography image reconstruction. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6867837, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Institute of Electrical and Electronics Engineers Inc., pp. 173-176, 11th IEEE International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 29/04/14. https://doi.org/10.1109/isbi.2014.6867837

Second order total generalized variation for low-dose computed tomography image reconstruction. / Niu, Shanzhou; Ma, Jianhua; Huang, Jing et al.
2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 173-176 6867837 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014).

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

TY - GEN

T1 - Second order total generalized variation for low-dose computed tomography image reconstruction

AU - Niu, Shanzhou

AU - Ma, Jianhua

AU - Huang, Jing

AU - Bian, Zhaoying

AU - Liang, Zhengrong

AU - Chen, Wufan

PY - 2014/7/29

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N2 - High radiation dose during x-ray computed tomography (CT) examinations can increase the risk of cancer and has become major concerns to patient. Accordingly, minimizing the radiation exposure without sacrificing image quality is a meaningful research topic. In this work, with the aim to reduce radiation during data acquisition, we propose a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating a total generalized variation (TGV) regularization, which is referred to as "PWLS-TGV". Specifically, the TGV regularization utilizes second-order derivatives of the desired image with imposing some higher order smoothness in regions away from the edges and the weighted leastsquares term considers a data-dependent variance estimation serving for improvement of image reconstruction from low-dose CT measurement. Subsequently, an alternating minimization algorithm was adopted to optimize the associative objective function. The experimental results on digital phantom and real patient data show that the present PWLS-TGV method can achieve significant gains over the existing similar methods in noise and artifacts suppression.

AB - High radiation dose during x-ray computed tomography (CT) examinations can increase the risk of cancer and has become major concerns to patient. Accordingly, minimizing the radiation exposure without sacrificing image quality is a meaningful research topic. In this work, with the aim to reduce radiation during data acquisition, we propose a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating a total generalized variation (TGV) regularization, which is referred to as "PWLS-TGV". Specifically, the TGV regularization utilizes second-order derivatives of the desired image with imposing some higher order smoothness in regions away from the edges and the weighted leastsquares term considers a data-dependent variance estimation serving for improvement of image reconstruction from low-dose CT measurement. Subsequently, an alternating minimization algorithm was adopted to optimize the associative objective function. The experimental results on digital phantom and real patient data show that the present PWLS-TGV method can achieve significant gains over the existing similar methods in noise and artifacts suppression.

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ER -

Niu S, Ma J, Huang J, Bian Z, Liang Z, Chen W. Second order total generalized variation for low-dose computed tomography image reconstruction. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 173-176. 6867837. (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). doi: 10.1109/isbi.2014.6867837

Second order total generalized variation for low-dose computed tomography image reconstruction

Abstract

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UN SDGs

Keywords

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