Sparse-view X-ray CT reconstruction with Gamma regularization

Junfeng Zhang; Yining Hu; Jian Yang; Yang Chen; Jean Louis Coatrieux; Limin Luo

doi:10.1016/j.neucom.2016.12.019

Sparse-view X-ray CT reconstruction with Gamma regularization

Junfeng Zhang, Yining Hu, Jian Yang, Yang Chen^*, Jean Louis Coatrieux, Limin Luo

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

By providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging. Recent contributions have demonstrated that the total variation (TV) constraint can lead to improved solution by regularizing the underdetermined ill-posed problem of sparse-view reconstruction. However, when the projection views are reduced below certain numbers, the performance of TV regularization tends to deteriorate with severe artifacts. In this paper, we explore the applicability of Gamma regularization for the sparse-view CT reconstruction. Experiments on simulated data and clinical data demonstrate that the Gamma regularization can lead to good performance in sparse-view reconstruction.

Original language	English
Pages (from-to)	251-269
Number of pages	19
Journal	Neurocomputing
Volume	230
DOIs	https://doi.org/10.1016/j.neucom.2016.12.019
Publication status	Published - 22 Mar 2017
Externally published	Yes

Keywords

Computer tomography
Gamma regularization
TV regularization
l-norm

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10.1016/j.neucom.2016.12.019

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title = "Sparse-view X-ray CT reconstruction with Gamma regularization",

abstract = "By providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging. Recent contributions have demonstrated that the total variation (TV) constraint can lead to improved solution by regularizing the underdetermined ill-posed problem of sparse-view reconstruction. However, when the projection views are reduced below certain numbers, the performance of TV regularization tends to deteriorate with severe artifacts. In this paper, we explore the applicability of Gamma regularization for the sparse-view CT reconstruction. Experiments on simulated data and clinical data demonstrate that the Gamma regularization can lead to good performance in sparse-view reconstruction.",

keywords = "Computer tomography, Gamma regularization, TV regularization, l-norm",

author = "Junfeng Zhang and Yining Hu and Jian Yang and Yang Chen and Coatrieux, \{Jean Louis\} and Limin Luo",

note = "Publisher Copyright: {\textcopyright} 2016 Elsevier B.V.",

year = "2017",

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doi = "10.1016/j.neucom.2016.12.019",

language = "English",

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T1 - Sparse-view X-ray CT reconstruction with Gamma regularization

AU - Zhang, Junfeng

AU - Hu, Yining

AU - Yang, Jian

AU - Chen, Yang

AU - Coatrieux, Jean Louis

AU - Luo, Limin

PY - 2017/3/22

Y1 - 2017/3/22

N2 - By providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging. Recent contributions have demonstrated that the total variation (TV) constraint can lead to improved solution by regularizing the underdetermined ill-posed problem of sparse-view reconstruction. However, when the projection views are reduced below certain numbers, the performance of TV regularization tends to deteriorate with severe artifacts. In this paper, we explore the applicability of Gamma regularization for the sparse-view CT reconstruction. Experiments on simulated data and clinical data demonstrate that the Gamma regularization can lead to good performance in sparse-view reconstruction.

AB - By providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging. Recent contributions have demonstrated that the total variation (TV) constraint can lead to improved solution by regularizing the underdetermined ill-posed problem of sparse-view reconstruction. However, when the projection views are reduced below certain numbers, the performance of TV regularization tends to deteriorate with severe artifacts. In this paper, we explore the applicability of Gamma regularization for the sparse-view CT reconstruction. Experiments on simulated data and clinical data demonstrate that the Gamma regularization can lead to good performance in sparse-view reconstruction.

KW - Computer tomography

KW - Gamma regularization

KW - TV regularization

KW - l-norm

UR - https://www.scopus.com/pages/publications/85009360455

U2 - 10.1016/j.neucom.2016.12.019

DO - 10.1016/j.neucom.2016.12.019

M3 - Article

AN - SCOPUS:85009360455

SN - 0925-2312

VL - 230

SP - 251

EP - 269

JO - Neurocomputing

JF - Neurocomputing

ER -

Sparse-view X-ray CT reconstruction with Gamma regularization

Abstract

Keywords

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