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 | |
| 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 |
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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
Cite this
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
}
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
ER -