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

TL;DR

This paper introduces a topology-aware method for fusing multi-energy spectral CT data into a single volume using histograms and extremum graphs, enhancing visualization and segmentation of complex spectral datasets.

Contribution

It presents a novel fusion technique leveraging histograms and extremum graphs to combine spectral CT volumes into a single, information-rich representation.

Findings

Effective reduction of multivolume data to a single representative volume

Preservation of key structural and material features in fused volume

Improved visualization and segmentation capabilities

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.