F-18-Fluorodeoxyglucose positron emission tomography (FDG-PET) has been used to evaluate the metabolic response of metastatic brain tumors to treatment by comparing their tumor glucose metabolism before and after treatment. The standard analysis based on regions-of-interest has the advantage of simplicity. However, it is by definition restricted to those regions and is subject to observer variability. In addition, the observed changes in tumor metabolism are often confounded by normal changes in the tissue background, which can be heterogenous. We propose an analysis pipeline for automatically detecting the change at each voxel in the entire brain of a single subject, while adjusting for changes in the background. The complete analysis includes image registration, segmentation, a hierarchical model for background adjustment and voxelwise statistical comparisons. We demonstrate the method's ability to identify areas of tumor response and/or progression in two subjects enrolled in a clinical trial using FDG-PET to evaluate lapatinib for the treatment of brain metastases in breast cancer patients.