DETECTION OF GLIOMA EVOLUTION ON LONGITUDINAL MRI STUDIES

Detection of millimetric brain tumor growth patterns on longitudinal MRI acquisitions remains challenging in clinical practice. A simple difference map of two longitudinal co-registered MRI volumes fails to detect specific tumor evolution, due to non-linear contrast change between the two data sets. This paper presents a novel method for detection and quantification of tumor evolution in longitudinal single-protocol MRI studies. A computational framework was designed to enable comparison of co-registered MRI volumes based on gray-scale "normalization" via midway histogram equalization and computation of difference maps. Midway-based difference maps provided very selective representations of structural modifications within pathological areas and on the surrounding structures. Quantitative tumor growth parameters between times t
₂
and t
₂
were computed on the difference maps, provided that a manual segmentation of the tumor is available at time t
₁
. The method was evaluated on longitudinal SPGR (T1-weighted) and FLAIR (T2-weighted) MRI volumes for two patients harboring a WHO grade II glioma. Results for quantification of tumor growth from midway difference maps are presented, showing sub-millimetric precision of clinical growth indices, when compared to manual tracing estimations