Quantitative analysis of metabolic and dynamic imaging data produces maps of parameters that show promise for improving medical diagnosis and therapeutic monitoring for patients with brain tumors. Statistical ROI analysis of these maps can be used to quantitatively summarize multi-modality imaging metrics and longitudinal changes. In this work we demonstrate a standards-based mechanism for generating and communicating minable, quantitative Region of Interest (ROI) analysis results that can easily be integrated into clinical workflows and radiomic studies.
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