Abstract #2380

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

Shuai Wang^1,2,3, Chenyue Liu^1,2,3, Tianjiao Chen^1,2,3, Kai Ai⁴, Zhen Jia^1,2,3, Chao Jin^1,2,3, Xianjun Li^1,2,3, Jiayue Dai^1,2,3, and Jian Yang^1,2,3

¹Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, XIAN, China, ²Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, XIAN, China, ³Xi’an Key Laboratory of Medical Computational Imaging, XIAN, China, ⁴Department of Clinical and Technical Support, Philips Healthcare, XIAN, China

Synopsis

Keywords: Tumors (Pre-Treatment), Neuro

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.

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