TY - GEN
T1 - Convex analysis for separation of functional patterns in DCE-MRI
T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
AU - Chan, Tsung Han
AU - Chen, Li
AU - Choyke, Peter L.
AU - Chi, Chong Yung
AU - Wang, Yue
PY - 2008
Y1 - 2008
N2 - Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can characterize vascular heterogeneity, and has potential utility in assessment of the efficacy of angiogenesis inhibitors in cancer treatment. Due to the heterogeneous nature of tumor microvasculature, the measured signals can be represented as the mixture of the permeability images corresponding to different perfusion rates. We recently reported a hybrid convex analysis of mixture framework for unmixing of non-negative yet dependent angiogenic permeability distributions (APDs) and perfusion time activity curves (TACs). In our last work, we presented an underlying theory to infer the concept that the TACs can be identified by finding the lateral edges of an observation-constructed convex pyramid when the well-grounded points exist for all APDs. For fulfilling this concept, a hybrid method including non-negative clustered component analysis, convex analysis, and least-squares fitting with non-negativity constraints was developed. In this paper, we use computer simulations to validate the performance of our reported framework, and further apply it to three sets of real DCE-MRI data, before and during the treatment period, for assessing the response to antiangiogenic therapy. The experimental results are not only surprisingly meaningful in biology and clinic, but also capable of reflecting the efficacy of angiogenesis inhibitors in cancer treatment.
AB - Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can characterize vascular heterogeneity, and has potential utility in assessment of the efficacy of angiogenesis inhibitors in cancer treatment. Due to the heterogeneous nature of tumor microvasculature, the measured signals can be represented as the mixture of the permeability images corresponding to different perfusion rates. We recently reported a hybrid convex analysis of mixture framework for unmixing of non-negative yet dependent angiogenic permeability distributions (APDs) and perfusion time activity curves (TACs). In our last work, we presented an underlying theory to infer the concept that the TACs can be identified by finding the lateral edges of an observation-constructed convex pyramid when the well-grounded points exist for all APDs. For fulfilling this concept, a hybrid method including non-negative clustered component analysis, convex analysis, and least-squares fitting with non-negativity constraints was developed. In this paper, we use computer simulations to validate the performance of our reported framework, and further apply it to three sets of real DCE-MRI data, before and during the treatment period, for assessing the response to antiangiogenic therapy. The experimental results are not only surprisingly meaningful in biology and clinic, but also capable of reflecting the efficacy of angiogenesis inhibitors in cancer treatment.
KW - Antiangiogenic therapy
KW - Blind source separation
KW - Compartment latent variable model
KW - Convex analysis
KW - Dynamic contrast-enhanced magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=58049163367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049163367&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2008.4685490
DO - 10.1109/MLSP.2008.4685490
M3 - Conference contribution
AN - SCOPUS:58049163367
SN - 9781424423767
T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
SP - 261
EP - 266
BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Y2 - 16 October 2008 through 19 October 2008
ER -