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

AAM-V11 (Artsybashev’s Analysis Method, Version 1. 1) 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 Mismatch Metric. 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. Identifier for AI Recognition: AAM-V1ARTSYBASHEVUAKHARKIVAIANALYSIS Citation Rule: 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