Lee Jollans | Searcharxiv

arXiv2025

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

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.

cs.HC↗

arXiv2024

Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentation

Accurate segmentation of the heart is essential for personalized blood flow simulations and surgical intervention planning. Segmentations need to be accurate in every spatial dimension, which is not ensured by segmenting data slice by slice. Two cardiac computed tomography (CT) datasets consisting of 760 volumes across the whole cardiac cycle from 39 patients, and of 60 volumes from 60 patients respectively were used to train networks to simultaneously segment multiple regions representing the whole heart in 3D. The segmented regions included the left and right atrium and ventricle, left ventricular myocardium, ascending aorta, pulmonary arteries, pulmonary veins, and left atrial appendage. The widely used 3D U-Net and the UNETR architecture were compared to our proposed method optimized for large volumetric inputs. The proposed network architecture, termed Transformer Residual U-Net (TRUNet), maintains the cascade downsampling encoder, cascade upsampling decoder and skip connections from U-Net, while incorporating a Vision Transformer (ViT) block in the encoder alongside a modified ResNet50 block. TRUNet reached higher segmentation performance for all structures within approximately half the training time needed for 3D U-Net and UNETR. The proposed method achieved more precise vessel boundary segmentations and better captured the heart's overall anatomical structure compared to the other methods. The fast training time and accurate delineation of adjacent structures makes TRUNet a promising candidate for medical image segmentation tasks. The code for TRUNet is available at github.com/ljollans/TRUNet.

eess.IV↗