The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature.

ABSTRACT: Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape i

SUBMITTER: Khabaz K 

PROVIDER: S-EPMC10866512 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature