TY - GEN
T1 - Low-rank approximations for dynamic imaging
AU - Haldar, Justin P.
AU - Liang, Zhi-Pei
PY - 2011
Y1 - 2011
N2 - This paper describes a framework for dynamic imaging based on the representation of a spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough to accurately and parsimoniously represent a wide range of dynamic imaging data. Representation using a low-rank model leads to new schemes for data acquisition and image reconstruction, enabling reconstruction from highly-undersampled datasets. Theoretical considerations and algorithms are discussed, and empirical results are provided to illustrate the performance of the approach.
AB - This paper describes a framework for dynamic imaging based on the representation of a spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough to accurately and parsimoniously represent a wide range of dynamic imaging data. Representation using a low-rank model leads to new schemes for data acquisition and image reconstruction, enabling reconstruction from highly-undersampled datasets. Theoretical considerations and algorithms are discussed, and empirical results are provided to illustrate the performance of the approach.
KW - Dynamic Imaging
KW - Low-Rank Matrix Recovery
KW - Partial Separability
UR - http://www.scopus.com/inward/record.url?scp=80055026821&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80055026821&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2011.5872582
DO - 10.1109/ISBI.2011.5872582
M3 - Conference contribution
AN - SCOPUS:80055026821
SN - 9781424441280
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1052
EP - 1055
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 -