VolMoVis: Real-Time Volume Generation and Motion Visualization with Dynamic Tomographic Reconstruction

Keywords

Neural Representations, 4D Reconstruction, Dynamic Volume Visualization, Real-Time Generation, Volume Rendering

Abstract

We present VolMoVis, a method for dynamic tomographic reconstruction that supports real-time volume generation and volumetric motion visualization from 2D projections. Visualizing the motion of 3D anatomical structures, such as organs and tumors, is critical for computer-aided interventions. However, conventional 4D volumetric reconstruction methods typically produce a limited set of volumes at discrete phases, suffering from low temporal resolution. Moreover, it often requires extensive segmentation of 3D structures or regions for visualizing volumetric data, making it challenging to segment and visualize dynamic volumes in real-time. To address these challenges, VolMoVis framework employs a continuous implicit neural representation that decomposes the dynamic volumetric data into a static reference volume and a continuous deformation field. This decomposition, along with an efficient deformation network, enables our framework to achieve real-time volume generation and volumetric visualization of continuous anatomical motions. We evaluate VolMoVis on both 4D digital phantoms and real patient datasets, demonstrating its effectiveness for accurate anatomical reconstruction and motion tracking. Furthermore, we highlight its capabilities in real-time simultaneous volume generation and tumor segmentation for visualizing dynamic volumes and 4D tumor tracking, showcasing its potential in image-guided radiation therapy.