White matter characterization in regions of edema surrounding meningioma brain tumor using diffusion MRI: A comparative study of DTI and NODDI.

White matter (WM) tract detection is critical in presurgical planning of tumor resection however, standard-of-care imaging techniques including T1-weighted, T2-weighted, and diffusion tensor imaging (DTI) often fail to characterize WM tracts within regions of edema. This failure arises because edema increases the isotropic diffusion component within a voxel, reducing sensitivity to the anisotropic diffusion that reflects WM integrity and directionality. More advanced diffusion modeling techniques such as free water-corrected DTI (FW-DTI), the Standard Model of Imaging (SMI), and Neurite Orientation Dispersion and Density Imaging (NODDI) address this limitation by quantifying the diffusion signals as arising from different tissue microenvironments, allowing for separation of free water, intra-neurite, and extra-neurite contributions. These techniques better preserve directionality metrics even in the presence of edema and may enhance tractography accuracy. In this study, we use multi-shell diffusion MRI data obtained from patients with meningioma brain tumors, specifically because meningiomas typically displace rather than infiltrate the surrounding WM--allowing us to isolate the effects of edema without confounding tumor invasion. We compared fractional anisotropy (FA from DTI), FW-FA (FW-DTI), P{square}(SMI), and orientation dispersion index (ODI from NODDI) in edematous and contralateral healthy WM regions and evaluated tractography performance across models as well. Our results show that NODDI, SMI, and FW-DTI provide improved characterization of WM within edema, yielding comparable diffusion metrics across regions and greater tract coverage compared to DTI. These improvements highlight the potential of advanced diffusion models for preoperative mapping. Future work will extend these methods to gliomas, where infiltrative tumor margins complicate WM detection, and translation into surgical navigation workflows.