Correlated Curve Estimation with Application to Perfusion CT

• Activity Number: 288
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Yuan Wang and Jianhua Hu and Kim-Anh Do and Brian Hobbs*
• Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
• Keywords: dynamic imaging ; correlated curves ; functional data ; kernel smoothing ; perfusion CT

Abstract

For many cancer imaging applications, radiologists identify the presence of solid tumors through informal assessment of the extent to which candidate regions of interest absorb and maintain contrast over a series of a few repeated scans. Often multiple interdependent ROIs are evaluated in isolation, using data from only a few scans. Independent estimation appears limiting for analysis of sparse functional data derived from dynamic imaging techniques that use physiological models to derive multiple interdependent biomarkers acquired from multiple regions of interests (ROI) within the same organ. We consider statistical methods for joint estimation of sparse spatiotemporally correlated imaging-biomarkers using semi-parametric models. Joint prediction is used to identify liver metastases using perfusion characteristics from multiple intra-patient ROIs acquired using dynamic computed tomography.