Topology-Aware Volume Fusion for Spectral Computed Tomography via Histograms and Extremum Graph

2025-08-20

AI Summary

Photonic-counting CT (PCCT) generates high-dimensional multi-energy volumetric data that pose significant challenges for direct volume rendering and segmentation. To address this, we propose a structure-aware multi-channel fusion method. Our approach introduces extremum graphs to model the topological distribution of multi-volume data and employs 2D histogram analysis to select complementary energy channels, thereby jointly preserving structural and material features across channels. Through topology-guided feature extraction and mesh-based projection, we reduce the high-dimensional multi-energy data into a single, information-rich, and topologically consistent scalar volume. Experimental results demonstrate that the fused volume substantially reduces computational complexity for subsequent rendering and segmentation tasks, while outperforming conventional weighted averaging and principal component analysis in preserving critical anatomical structures and material discriminability. This work establishes a novel paradigm for clinical visualization and quantitative analysis of PCCT data.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Photon-Counting Computed Tomography (PCCT) is a novel imaging modality that simultaneously acquires volumetric data at multiple X-ray energy levels, generating separate volumes that capture energy-dependent attenuation properties. Attenuation refers to the reduction in X-ray intensity as it passes through different tissues or materials. This spectral information enhances tissue and material differentiation, enabling more accurate diagnosis and analysis. However, the resulting multivolume datasets are often complex and redundant, making visualization and interpretation challenging. To address these challenges, we propose a method for fusing spectral PCCT data into a single representative volume that enables direct volume rendering and segmentation by leveraging both shared and complementary information across different channels. Our approach starts by computing 2D histograms between pairs of volumes to identify those that exhibit prominent structural features. These histograms reveal relationships and variations that may be difficult to discern from individual volumes alone. Next, we construct an extremum graph from the 2D histogram of two minimally correlated yet complementary volumes-selected to capture both shared and distinct features-thereby maximizing the information content. The graph captures the topological distribution of histogram extrema. By extracting prominent structure within this graph and projecting each grid point in histogram space onto it, we reduce the dimensionality to one, producing a unified volume. This representative volume retains key structural and material characteristics from the original spectral data while significantly reducing the analysis scope from multiple volumes to one. The result is a topology-aware, information-rich fusion of multi-energy CT datasets that facilitates more effective visualization and segmentation.

Problem

• Fusing spectral PCCT multi-volume datasets into single representative volume
• Reducing complexity and redundancy in multi-energy CT visualization
• Enabling direct rendering and segmentation via topological feature fusion

Innovation

• Topology-aware fusion via histograms and extremum graph
• Dimensionality reduction to unified representative volume
• Retains structural and material characteristics from spectral data