Enhancing Brain Flow Visualization with Automated 3D Data Processing: A Study on DCE-MRI Data from Mice with Tumors

Abstract

Enhancing the process of generating entirely automated visualization schemes of complex fluid flow patterns within brain tumors is critical for gaining insights into their movements and behaviors. This study focused on optimizing and automating the processing of 3D volumetric and vector field data sets obtained from DCE-MRI (Dynamic Contrast-Enhanced Magnetic Resonance Imaging) scans. It is crucial to maintain performance, preserve data quality and resolution, and provide an accessible platform for biomedical scientists.

In this paper, we represent an innovative approach to enhance fluid flow visualization of brain tumors through scalable visualization techniques. New techniques have been designed, benchmarked, and authenticated to produce X3D visualizations in Web3D environments using Python, and ParaView. The proposed approach does not only enhance fluid flow visualization in the context of brain tumor research but also provides a reproducible and transparent framework for future studies with both human and mouse scans.

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