Elastic Registration of Geodesic Vascular Graphs
This work addresses the need for robust vascular graph registration to enable group-wise analyses in clinical settings, representing an incremental advancement in domain-specific methods.
The paper tackled the problem of co-registering vascular graphs for clinical inference by introducing an end-to-end approach that aligns networks with non-linear geometries and topological deformations using a novel overconnected geodesic formulation, achieving promising results on synthetic and real angiography data.
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference. This, however, is only feasible when graphs are co-registered together, allowing coherent multiple comparisons. The robust registration of vascular topologies stands therefore as key enabling technology for group-wise analyses. In this work, we present an end-to-end vascular graph registration approach, that aligns networks with non-linear geometries and topological deformations, by introducing a novel overconnected geodesic vascular graph formulation, and without enforcing any anatomical prior constraint. The 3D elastic graph registration is then performed with state-of-the-art graph matching methods used in computer vision. Promising results of vascular matching are found using graphs from synthetic and real angiographies. Observations and future designs are discussed towards potential clinical applications.