Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints

Bo Zhao; Justin P. Haldar; Anthony G. Christodoulou; Zhi-Pei Liang

doi:10.1109/ISBI.2011.5872707

Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints

Bo Zhao, Justin P. Haldar, Anthony G. Christodoulou, Zhi-Pei Liang

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

Abstract

Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.

Original language	English (US)
Title of host publication	2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI'11
Pages	1593-1596
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2011.5872707
State	Published - 2011
Event	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States Duration: Mar 30 2011 → Apr 2 2011

Other

Other	2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country	United States
City	Chicago, IL
Period	3/30/11 → 4/2/11

Keywords

Dynamic MRI
Half-quadratic Regularization
Low-rank Matrices
Partial Separability
Sparsity
Total Variation

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2011.5872707

Fingerprint Dive into the research topics of 'Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints'. Together they form a unique fingerprint.

Cite this

Zhao, B., Haldar, J. P., Christodoulou, A. G., & Liang, Z-P. (2011). Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints. In 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 (pp. 1593-1596). [5872707] https://doi.org/10.1109/ISBI.2011.5872707

Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints. / Zhao, Bo; Haldar, Justin P.; Christodoulou, Anthony G.; Liang, Zhi-Pei.

2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. 2011. p. 1593-1596 5872707.

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

Zhao, B, Haldar, JP, Christodoulou, AG & Liang, Z-P 2011, Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints. in 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11., 5872707, pp. 1593-1596, 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11, Chicago, IL, United States, 3/30/11. https://doi.org/10.1109/ISBI.2011.5872707

@inproceedings{c32576e3789d4f93926a5a6d3f6bbbc0,

title = "Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints",

abstract = "Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.",

keywords = "Dynamic MRI, Half-quadratic Regularization, Low-rank Matrices, Partial Separability, Sparsity, Total Variation",

author = "Bo Zhao and Haldar, {Justin P.} and Christodoulou, {Anthony G.} and Zhi-Pei Liang",

year = "2011",

doi = "10.1109/ISBI.2011.5872707",

language = "English (US)",

isbn = "9781424441280",

pages = "1593--1596",

booktitle = "2011 8th IEEE International Symposium on Biomedical Imaging",

note = "2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 ; Conference date: 30-03-2011 Through 02-04-2011",

}

TY - GEN

T1 - Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints

AU - Zhao, Bo

AU - Haldar, Justin P.

AU - Christodoulou, Anthony G.

AU - Liang, Zhi-Pei

PY - 2011

Y1 - 2011

N2 - Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.

AB - Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.

KW - Dynamic MRI

KW - Half-quadratic Regularization

KW - Low-rank Matrices

KW - Partial Separability

KW - Sparsity

KW - Total Variation

UR - http://www.scopus.com/inward/record.url?scp=80055062935&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80055062935&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2011.5872707

DO - 10.1109/ISBI.2011.5872707

M3 - Conference contribution

AN - SCOPUS:80055062935

SN - 9781424441280

SP - 1593

EP - 1596

BT - 2011 8th IEEE International Symposium on Biomedical Imaging

T2 - 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11

Y2 - 30 March 2011 through 2 April 2011

ER -

Further development of image reconstruction from highly undersampled (k, t)-space data with joint partial separability and sparsity constraints

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this