NIMG-48. MULTI-ECHO SPIN-AND-GRADIENT ECHO (SAGE) PERFUSION MRI TO EVALUATE BRAIN TUMOR MICROSTRUCTURE AND MICROVASCULATURE

Abstract

Abstract BACKGROUND In a classic DSC perfusion sequence, T1 and T2* leakage effects compete in determining the post-bolus signal, and are influenced by tissue geometry (i.e. cell density, cell size). Different DSC sequences can be variously influenced by these effects, depending on the different degree of T1 weighting and the pre-bolus administration. Multi-echo spin-and-gradient echo (SAGE) perfusion MRI enables to disentangle T2* and T1 components that contribute to the classic DSC curve, and to simultaneously compute DSC and DCE perfusion metrics. METHODS We retrospectively selected patients with: diagnosis of primary brain tumor, availability of SAGE-based DSC perfusion and DWI datasets, availability of histopathological images from targeted biopsies located within contrast-enhancing tissue. Post-processing allowed to distinguish the pure T2* component from the pure T1 component, and to perform a DCE analysis on the latter. Quantitative perfusion MRI measurements were then evaluated with respect to underlying histopathology. RESULTS Histopathological images were available for 22 targeted biopsies (across 10 patients) meeting the inclusion criteria. The following novel MRI quantitative maps were successfully computed voxelwise: ΔR2* at steady state (reflecting T2* leakage effects), ΔR1 at steady state (reflecting T1 leakage effects), transverse relaxivity at tracer equilibrium (TRATE, reflecting the combination of T2* and T1 leakage effects). In addition, Ve and ktrans were computed from the DCE analysis, and the percentage of signal recovery (PSR) was computed from the second echo of the multi-echo DSC (comparable to a classic single-echo DSC sequence). CONCLUSIONS Histopathological validation will assess the usefulness of these novel multi-echo derived quantitative maps for the non-invasive prediction of tumor microstructure. This would be particularly relevant for: 1) differential diagnosis between brain tumors with different cell size and cell density (e.g. lymphoma vs glioblastoma); 2) treatment response assessment (as pre-existing studies proved that cell shrinkage is an early event in treatment response).