TH‐A‐BRF‐09: Integration of High‐Resolution MRSI Into Glioblastoma Treatment Planning

Abstract

Purpose:Identification of a metabolite signature that shows significant tumor cell infiltration into normal brain in regions that do not appear abnormal on standard MRI scans would be extremely useful for radiation oncologists to choose optimal regions of brain to treat, and to quantify response beyond the MacDonald criteria. We report on integration of high‐resolution magnetic resonance spectroscopic imaging (HR‐MRSI) with radiation dose escalation treatment planning to define and target regions at high risk for recurrence.Methods:We propose to supplement standard MRI with a special technique performed on an MRI scanner to measure the metabolite levels within defined volumes. Metabolite imaging was acquired using an advanced MRSI technique combining 3D echo‐planar spectroscopic imaging (EPSI) with parallel acquisition (GRAPPA) using a multichannel head coil that allows acquisition of whole brain metabolite maps with 108 μl resolution in 12 minutes implemented on a 3T MR scanner. Elevation in the ratio of two metabolites, choline (Cho, elevated in proliferating high‐grade gliomas) and N‐acetyl aspartate (NAA, a normal neuronal metabolite), was used to image infiltrating high‐grade glioma cells in vivo.Results:The metabolite images were co‐registered with standard contrast‐enhanced T1‐weighted MR images using in‐house registration software and imported into the treatment‐planning system. Regions with tumor infiltration are identified on the metabolic images and used to create adaptive IMRT plans that deliver a standard dose of 60 Gy to the standard target volume and an escalated dose of 75 Gy (or higher) to the most suspicious regions, identified as areas with elevated Cho/NAA ratio.Conclusion:We have implemented a state‐of‐the‐art HR‐MRSI technology that can generate metabolite maps of the entire brain in a clinically acceptable scan time, coupled with introduction of an imaging co‐registration/ analysis program that combines MRSI data with standard imaging studies in a clinically useful fashion.