Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Combining convolutional sparse coding with total variation for sparse-view CT reconstruction

Not Accessible

Your library or personal account may give you access

Abstract

Conventional dictionary-learning-based computed tomography (CT) reconstruction methods extract patches from an original image to train, ignoring the consistency of pixels in overlapping patches. To address the problem, this paper proposes a method combining convolutional sparse coding (CSC) with total variation (TV) for sparse-view CT reconstruction. The proposed method inherits the advantages of CSC by directly processing the whole image without dividing it into overlapping patches, which preserves more details and reduces artifacts caused by patch aggregation. By introducing a TV regularization term to enhance the constraint of the image domain, the noise can be effectively further suppressed. The alternating direction method of multipliers algorithm is employed to solve the objective function. Numerous experiments are conducted to validate the performance of the proposed method in different views. Qualitative and quantitative results show the superiority of the proposed method in terms of noise suppression, artifact reduction, and image details recovery.

© 2022 Optica Publishing Group

Full Article  |  PDF Article
More Like This
Convolutional sparse coding for compressed sensing photoacoustic CT reconstruction with partially known support

Zezheng Qin, Yiming Ma, Lingyu Ma, Guangxing Liu, and Mingjian Sun
Biomed. Opt. Express 15(2) 524-539 (2024)

Low-dose CT via convolutional neural network

Hu Chen, Yi Zhang, Weihua Zhang, Peixi Liao, Ke Li, Jiliu Zhou, and Ge Wang
Biomed. Opt. Express 8(2) 679-694 (2017)

Few-view image reconstruction with fractional-order total variation

Yi Zhang, Weihua Zhang, Yinjie Lei, and Jiliu Zhou
J. Opt. Soc. Am. A 31(5) 981-995 (2014)

Data Availability

Data underlying the results presented in this paper are available in Ref. [39].

39. “NIH-AAPM-Mayo clinic low dose CT GrandChallenge,” 2016, http://www.aapm.org/GrandChallenge/LowDoseCT/.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (13)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (4)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (30)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved