Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation

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

doi:10.1109/ICIP.2016.7532501

Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation

Kyong Hwan Jin, Dongwook Lee, Juyoung 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

Recently, an annihilating filter based low-rank Hankel matrix approach (ALOHA) was proposed as a general framework for sparsity-driven k-space interpolation method for compressed sensing MRI (CS-MRI). The principle of ALOHA framework is based on the fundamental duality between the transform domain sparsity in the primary space and the low-rankness of weighted Hankel matrix in Fourier domain, which converts CS-MRI to a k-space interpolation problem using structured matrix completion. In this review, we explain the theory behind ALOHA. Experimental results with in vivo data for multi-coil dynamic imaging, parametric mapping as well as Nyquist ghost correction confirmed that the proposed method has potential to be a general solution of various MR imaging problems.

Original language	English
Title of host publication	2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Publisher	IEEE Computer Society
Pages	968-972
Number of pages	5
ISBN (Electronic)	9781467399616
DOIs	https://doi.org/10.1109/ICIP.2016.7532501
State	Published - 3 Aug 2016
Event	23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: 25 Sep 2016 → 28 Sep 2016

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2016-August
ISSN (Print)	1522-4880

Conference

Conference	23rd IEEE International Conference on Image Processing, ICIP 2016
Country/Territory	United States
City	Phoenix
Period	25/09/16 → 28/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

ALOHA
Annihilating filter
Nyquist ghost correction
Parallel MRI
Structured low rank matrix completion
T1/T2 parametric mapping

Access to Document

10.1109/ICIP.2016.7532501

Cite this

Jin, K. H., Lee, D., Lee, J., & Ye, J. C. (2016). Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (pp. 968-972). Article 7532501 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532501

@inproceedings{c50638010a2244918959f4126a3ed250,

title = "Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation",

abstract = "Recently, an annihilating filter based low-rank Hankel matrix approach (ALOHA) was proposed as a general framework for sparsity-driven k-space interpolation method for compressed sensing MRI (CS-MRI). The principle of ALOHA framework is based on the fundamental duality between the transform domain sparsity in the primary space and the low-rankness of weighted Hankel matrix in Fourier domain, which converts CS-MRI to a k-space interpolation problem using structured matrix completion. In this review, we explain the theory behind ALOHA. Experimental results with in vivo data for multi-coil dynamic imaging, parametric mapping as well as Nyquist ghost correction confirmed that the proposed method has potential to be a general solution of various MR imaging problems.",

keywords = "ALOHA, Annihilating filter, Nyquist ghost correction, Parallel MRI, Structured low rank matrix completion, T1/T2 parametric mapping",

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

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",

year = "2016",

month = aug,

day = "3",

doi = "10.1109/ICIP.2016.7532501",

language = "English",

series = "Proceedings - International Conference on Image Processing, ICIP",

publisher = "IEEE Computer Society",

pages = "968--972",

booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",

}

Jin, KH, Lee, D, Lee, J & Ye, JC 2016, Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings., 7532501, Proceedings - International Conference on Image Processing, ICIP, vol. 2016-August, IEEE Computer Society, pp. 968-972, 23rd IEEE International Conference on Image Processing, ICIP 2016, Phoenix, United States, 25/09/16. https://doi.org/10.1109/ICIP.2016.7532501

Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation. / Jin, Kyong Hwan; Lee, Dongwook; Lee, Juyoung et al.
2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. IEEE Computer Society, 2016. p. 968-972 7532501 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August).

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

TY - GEN

T1 - Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation

AU - Jin, Kyong Hwan

AU - Lee, Dongwook

AU - Lee, Juyoung

AU - Ye, Jong Chul

PY - 2016/8/3

Y1 - 2016/8/3

N2 - Recently, an annihilating filter based low-rank Hankel matrix approach (ALOHA) was proposed as a general framework for sparsity-driven k-space interpolation method for compressed sensing MRI (CS-MRI). The principle of ALOHA framework is based on the fundamental duality between the transform domain sparsity in the primary space and the low-rankness of weighted Hankel matrix in Fourier domain, which converts CS-MRI to a k-space interpolation problem using structured matrix completion. In this review, we explain the theory behind ALOHA. Experimental results with in vivo data for multi-coil dynamic imaging, parametric mapping as well as Nyquist ghost correction confirmed that the proposed method has potential to be a general solution of various MR imaging problems.

AB - Recently, an annihilating filter based low-rank Hankel matrix approach (ALOHA) was proposed as a general framework for sparsity-driven k-space interpolation method for compressed sensing MRI (CS-MRI). The principle of ALOHA framework is based on the fundamental duality between the transform domain sparsity in the primary space and the low-rankness of weighted Hankel matrix in Fourier domain, which converts CS-MRI to a k-space interpolation problem using structured matrix completion. In this review, we explain the theory behind ALOHA. Experimental results with in vivo data for multi-coil dynamic imaging, parametric mapping as well as Nyquist ghost correction confirmed that the proposed method has potential to be a general solution of various MR imaging problems.

KW - ALOHA

KW - Annihilating filter

KW - Nyquist ghost correction

KW - Parallel MRI

KW - Structured low rank matrix completion

KW - T1/T2 parametric mapping

UR - http://www.scopus.com/inward/record.url?scp=85006829782&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2016.7532501

DO - 10.1109/ICIP.2016.7532501

M3 - Conference contribution

AN - SCOPUS:85006829782

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 968

EP - 972

BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings

PB - IEEE Computer Society

T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016

Y2 - 25 September 2016 through 28 September 2016

ER -

Recent progresses of accelerated MRI using annihilating filter-based low-rank interpolation

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this