LMap: Shape-Preserving Local Mappings for Biomedical Visualization

TL;DR

LMap is a novel visualization technique that locally deforms complex biomedical surfaces to resolve occlusions while maintaining the overall shape, enhancing clarity without losing geometric context.

Contribution

The paper introduces a shape-preserving local mapping algorithm using extrinsic Ricci flow for biomedical surface visualization, addressing occlusion issues while preserving geometric integrity.

Findings

Effective in multimodal brain visualization

Improves virtual colonoscopy coverage

Enhances molecular surface visualization

Abstract

Visualization of medical organs and biological structures is a challenging task because of their complex geometry and the resultant occlusions. Global spherical and planar mapping techniques simplify the complex geometry and resolve the occlusions to aid in visualization. However, while resolving the occlusions these techniques do not preserve the geometric context, making them less suitable for mission-critical biomedical visualization tasks. In this paper, we present a shape-preserving local mapping technique for resolving occlusions locally while preserving the overall geometric context. More specifically, we present a novel visualization algorithm, LMap, for conformally parameterizing and deforming a selected local region-of-interest (ROI) on an arbitrary surface. The resultant shape-preserving local mappings help to visualize complex surfaces while preserving the overall geometric context. The algorithm is based on the robust and efficient extrinsic Ricci flow technique, and uses the dynamic Ricci flow algorithm to guarantee the existence of a local map for a selected ROI on an arbitrary surface. We show the effectiveness and efficacy of our method in three challenging use cases: (1) multimodal brain visualization, (2) optimal coverage of virtual colonoscopy centerline flythrough, and (3) molecular surface visualization.