Abstract:
|
Perfusion computed tomography (CTp) is an emerging functional imaging modality that uses physiological models to quantify characteristics pertaining to the passage of fluid through blood vessels. Perfusion characteristics provide physiological correlates for neovascularization induced by tumor angiogenesis. Thus CTp offers promise as a non-invasive quantitative functional imaging tool for cancer detection, prognostication, and treatment monitoring. In this paper, we develop a probabilistic framework for simultaneous supervised classification of multivariate correlated pixels using the total variation over time. The classification approach is applied to discriminate between regions of liver that contain pathologically verified metastases from
|