Topology-Aware Volume Fusion for Spectral Computed Tomography via Histograms and Extremum Graph
Mohit Sharma, Emma Nilsson, Martin Falk, Talha Bin Masood, Lee Jollans, Anders Persson, Tino Ebbers, Ingrid Hotz

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…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Topological and Geometric Data Analysis · Digital Image Processing Techniques
