Glioblastoma (GBM), the most aggressive primary brain tumor, is primarily diagnosed and monitored using gadoliniumenhanced T1-weighted and T2-weighted (T2W) magnetic resonance imaging (MRI). Hyperintensity on T2W images is understood to correspond with vasogenic edema and infiltrating tumor cells. GBM’s inherent heterogeneity and resulting non-specific MRI image features complicate assessing treatment response. To better understand treatment response, we propose creating a patient-specific untreated virtual imaging control (UVIC), which represents an individual tumor’s growth if it had not been treated, for comparison with actual post-treatment images. We generated a T2W MRI UVIC by combining a patient-specific mathematical model of tumor growth with a multi-compartmental MRI signal equation. GBM growth was mathematically modeled using the previously developed Proliferation-Invasion-Hypoxia-Necrosis- Angiogenesis-Edema (PIHNA-E) model, which simulated tumor as being comprised of three cellular phenotypes: normoxic, hypoxic and necrotic cells interacting with a vasculature species, angiogenic factors and extracellular fluid. Within the PIHNA-E model, both hypoxic and normoxic cells emitted angiogenic factors, which recruited additional vessels and caused the vessels to leak, allowing fluid, or edema, to escape into the extracellular space. The model’s output was spatial volume fraction maps for each glioma cell type and edema/extracellular space. Volume fraction maps and corresponding T2 values were then incorporated into a multi-compartmental Bloch signal equation to create simulated T2W images. T2 values for individual compartments were estimated from the literature and a normal volunteer. T2 maps calculated from simulated images had normal white matter, normal gray matter, and tumor tissue T2 values within range of literature values.