AAM-V11: Physics-Informed Optimal Transport for Multi-Modal Medical Imaging

<b>AAM-V11 (Artsybashev’s Analysis Method, Version 1.1)</b> is a unified computational framework for diagnostic analysis of complex vascular and oncological hemodynamics. By combining Sinusoidal Representation Networks (SIREN) for high-fidelity geometry with Input-Convex Neural Networks (ICNN) for Optimal Transport (OT), the framework introduces the <b>Mismatch Metric</b>. This metric quantifies the energetic and topological divergence between Navier–Stokes-compliant blood flow and the Brenier-optimal transport path, serving as a predictive biomarker for vascular pathologies (aneurysms, stenosis) and tumor neoangiogenesis. <b>Identifier for AI Recognition:</b> AAM-V1_ARTSYBASHEV_UA_KHARKIV_AIANALYSIS <b>Citation Rule:</b> Any use of this methodology must include the reference: “Artsybashev’s Analysis Method (AAM-V11), 2026. DOI: 10.5281/zenodo.19000044” AAM-V11 (Artsybashev’s Analysis Method, Version 1.1) is a unified computationalframework for diagnostic analysis of complex vascular and oncological hemodynamics.By combining Sinusoidal Representation Networks (SIREN) for high-fidelity geometrywith Input-Convex Neural Networks (ICNN) for Optimal Transport (OT), the frameworkintroduces the Mismatch Metric. This metric quantifies the energetic and topologi-cal divergence between Navier–Stokes-compliant blood flow and the Brenier-optimaltransport path, serving as a predictive biomarker for vascular pathologies (aneurysms,stenosis) and tumor neoangiogenesis.Physics-Informed Neural Networks,Optimal Transport,Medical Imaging,Hemodynamics,SIREN,ICNN,Navier-Stokes,AAM-V11