Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix

Kyong Hwan Jin; Juyoung Lee; Dongwook Lee; Jong Chul Ye

doi:10.1109/ISBI.2016.7493526

Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix

Kyong Hwan Jin, Juyoung Lee, Dongwook Lee, Jong Chul Ye

Department of Electrical Engineering and Computer Science

Korea Advanced Institute of Science and Technology

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

3 Scopus citations

Abstract

In this paper, we propose a sparse and low-rank decomposition of annihilating filter-based Hankel matrix for MRI artifact removal. Based on the observation that some MR artifacts are originated from k-space outliers, we employ the recently proposed image modeling method using annihilating filter-based low-rank Hankel matrix approach (ALOHA) to decompose the sparse outliers from the low-rank component. Unlike the recent sparse and low rank decomposition for dynamic MRI, the proposed approach can be applied even for static images, because the k-space low rank component comes from the intrinsic image properties. We demonstrate that the proposed algorithm clearly removes several types of artifacts such as impulse noises, motion artifacts, and herringbone artifacts from MR images.

Original language	English
Title of host publication	2016 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2016 - Proceedings
Publisher	IEEE Computer Society
Pages	1388-1391
Number of pages	4
ISBN (Electronic)	9781479923502
DOIs	https://doi.org/10.1109/ISBI.2016.7493526
State	Published - 15 Jun 2016
Event	2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic Duration: 13 Apr 2016 → 16 Apr 2016

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2016-June
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Country/Territory	Czech Republic
City	Prague
Period	13/04/16 → 16/04/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Hankel matrix
MRI artifacts
annihilation filter
robust principal component anlaysis
sparse and low-rank decomposition

Access to Document

10.1109/ISBI.2016.7493526

Cite this

Jin, K. H., Lee, J., Lee, D., & Ye, J. C. (2016). Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (pp. 1388-1391). Article 7493526 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June). IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493526

@inproceedings{66e5c641a250491bbc794c52dac01219,

title = "Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix",

abstract = "In this paper, we propose a sparse and low-rank decomposition of annihilating filter-based Hankel matrix for MRI artifact removal. Based on the observation that some MR artifacts are originated from k-space outliers, we employ the recently proposed image modeling method using annihilating filter-based low-rank Hankel matrix approach (ALOHA) to decompose the sparse outliers from the low-rank component. Unlike the recent sparse and low rank decomposition for dynamic MRI, the proposed approach can be applied even for static images, because the k-space low rank component comes from the intrinsic image properties. We demonstrate that the proposed algorithm clearly removes several types of artifacts such as impulse noises, motion artifacts, and herringbone artifacts from MR images.",

keywords = "Hankel matrix, MRI artifacts, annihilation filter, robust principal component anlaysis, sparse and low-rank decomposition",

author = "Jin, {Kyong Hwan} and Juyoung Lee and Dongwook Lee and Ye, {Jong Chul}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",

year = "2016",

month = jun,

day = "15",

doi = "10.1109/ISBI.2016.7493526",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "1388--1391",

booktitle = "2016 IEEE International Symposium on Biomedical Imaging",

}

Jin, KH, Lee, J, Lee, D & Ye, JC 2016, Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix. in 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings., 7493526, Proceedings - International Symposium on Biomedical Imaging, vol. 2016-June, IEEE Computer Society, pp. 1388-1391, 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016, Prague, Czech Republic, 13/04/16. https://doi.org/10.1109/ISBI.2016.7493526

Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix. / Jin, Kyong Hwan; Lee, Juyoung; Lee, Dongwook et al.
2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society, 2016. p. 1388-1391 7493526 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June).

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

TY - GEN

T1 - Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix

AU - Jin, Kyong Hwan

AU - Lee, Juyoung

AU - Lee, Dongwook

AU - Ye, Jong Chul

PY - 2016/6/15

Y1 - 2016/6/15

N2 - In this paper, we propose a sparse and low-rank decomposition of annihilating filter-based Hankel matrix for MRI artifact removal. Based on the observation that some MR artifacts are originated from k-space outliers, we employ the recently proposed image modeling method using annihilating filter-based low-rank Hankel matrix approach (ALOHA) to decompose the sparse outliers from the low-rank component. Unlike the recent sparse and low rank decomposition for dynamic MRI, the proposed approach can be applied even for static images, because the k-space low rank component comes from the intrinsic image properties. We demonstrate that the proposed algorithm clearly removes several types of artifacts such as impulse noises, motion artifacts, and herringbone artifacts from MR images.

AB - In this paper, we propose a sparse and low-rank decomposition of annihilating filter-based Hankel matrix for MRI artifact removal. Based on the observation that some MR artifacts are originated from k-space outliers, we employ the recently proposed image modeling method using annihilating filter-based low-rank Hankel matrix approach (ALOHA) to decompose the sparse outliers from the low-rank component. Unlike the recent sparse and low rank decomposition for dynamic MRI, the proposed approach can be applied even for static images, because the k-space low rank component comes from the intrinsic image properties. We demonstrate that the proposed algorithm clearly removes several types of artifacts such as impulse noises, motion artifacts, and herringbone artifacts from MR images.

KW - Hankel matrix

KW - MRI artifacts

KW - annihilation filter

KW - robust principal component anlaysis

KW - sparse and low-rank decomposition

UR - http://www.scopus.com/inward/record.url?scp=84978388526&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2016.7493526

DO - 10.1109/ISBI.2016.7493526

M3 - Conference contribution

AN - SCOPUS:84978388526

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 1388

EP - 1391

BT - 2016 IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016

Y2 - 13 April 2016 through 16 April 2016

ER -

Jin KH, Lee J, Lee D, Ye JC. Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society. 2016. p. 1388-1391. 7493526. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2016.7493526

Sparse and low-rank decomposition of MR artifact images using annihilating filter-based Hankel matrix

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this