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Analysis of spatially correlated functional data in tissue perfusion imaging

Banff International Research Station for Mathematical Innovation and Discovery

Tissue perfusion plays a critical role in oncology. Cancerous cell growth and migration requires the proliferation of networks of new blood vessels through the process of angiogenesis, triggering modifications to the vasculature of surrounding host tissue. Measurements from perfusion imaging modalities provide physiological correlates for neovascularization induced by tumor angiogenesis. Such measurements are often generated repeatedly over time and at multiple spatially interdependent units. To reduce model complexity and simplify the resulting inference, possible spatial correlation among neighboring units is often neglected. I will talk about a weighted kernel smoothing estimate of the mean function that leverages the spatial and temporal correlation, particularly, in the presence of sparse observations. The companion problem of developing a simultaneous prediction method for individual curves using discrete samples will also be discussed.

Mathematics; Statistics; Game theory, economics, social and behavioral sciences

video/mp4

Author affiliation: University of Texas MD Anderson Cancer Center

2016-08-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58684

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/