Enhancing Consistency Estimation and Boundary Reconstruction in MRE-Based Pituitary Tumor Assessment Using Adaptive Wavelet Filtering

Motivation: Preoperative assessment of pituitary adenoma (PA) consistency and boundary information is critical for determining surgical approaches. Goal(s): This study evaluates Adaptive Wavelet Filtering (AWF) to enhance MRE image quality for accurate PA consistency and boundary reconstruction. Approach: We applied AWF with algebraic inversion of the differential equation (AIDE) and local frequency estimation (LFE) algorithms to denoise wavefield images, improving parameter mapping. Results: AWF-AIDE showed the highest contrast-to-noise ratio and Dice coefficient, providing superior consistency estimation and boundary delineation. Findings align with intraoperative assessments, supporting AWF's clinical value in surgical planning. Impact: The proposed method significantly improves wavefield image quality, enhancing the accuracy of MRE-based pituitary tumor consistency estimation and boundary reconstruction. This advancement provides precise lesion details, aiding in optimal surgical planning and entry strategy selection in clinical practice.