Search arXivSearch

arXiv · 2505.13536

Investigating the Impact of Arterial Irregularity On Clinical Parameters Using Reduced Order CFD Models In Stenosed Coronary Artery

Abstract

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. This study introduces a novel approach that integrates patient-specific Multi-slice CT scans into CAD models, using a one-dimensional numerical framework to assess varying degrees of coronary artery stenosis. The computational analysis encompasses the entire arterial tree, with a particular focus on stenosed coronary arteries modeled analytically. Key parameters, such as area and velocity, are derived from one-dimensional characteristic equations based on forward and backward characteristic variables. A resistance model with zero reflection coefficient and realistic pressure waveform inputs is applied at the outflow and inflow, respectively. The global characteristics captured by the 1D model serve as boundary conditions for a 2D axisymmetric model that focuses on local characteristics. The numerical solvers are validated against existing literature, ensuring grid independence. Fractional Flow Reserve (FFR) and Instantaneous wave-free Ratio (iFR) are calculated using various non-Newtonian models across different stenosis severities. The study also investigates the impact of lesion irregularity in stenosed coronary arteries, finding that irregular arteries exhibit lower FFR and iFR values and higher pressure drops, indicating increased blood flow resistance. This method provides a reliable, non-invasive diagnostic tool for evaluating the functional severity of irregular coronary artery stenosis in clinical settings, effectively capturing both global and local hemodynamic characteristics.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Priyanshu Ghosh, Sayan Karmakar, Disha Mondal, Oeshee Roy, Supratim Saha. 2025-05-18. Investigating the Impact of Arterial Irregularity On Clinical Parameters Using Reduced Order CFD Models In Stenosed Coronary Artery. https://arxiv.org/abs/2505.13536

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Prediction of biological radiation effects based on ionization clusters (nanodosimetry)

This article reviews approaches that link the formation of ionization clusters in nanometric volumes to radiobiological effectiveness. The corresponding models were developed as the field of nanodosimetry developed. Some address early biological radiation effects, such as DNA damage, while most aim to predict cell survival or inactivation. The models also differ in the nanodosimetric quantities considered, with many based on the probability distribution of ionization cluster formation in a single target. Some models account for the synergistic effects of pairs of ionization clusters formed in different targets. Several models feature macroscopic aggregation frameworks based on particle fluence, which are proposed for use in radiotherapy treatment planning, particularly in ion-beam radiotherapy. The models are presented here using harmonized terminology and notation for nanodosimetric quantities. An extension of the conceptual framework of nanodosimetry is also discussed. This extension transitions from a target-centered description to a track-centered description. It also introduces nanodosimetry-based analogs of dosimetric concepts, such as dose and linear energy transfer. This paper traces and summarizes the historical development of nanodosimetry-based biological effect models and discusses conceptual aspects of the models to reveal their underlying assumptions and the extent to which they are mechanistic or merely elucidate correlations. Eventually, an attempt is made to identify the key open questions in this field that still need to be addressed.

physics.med-ph

Contextual Cellular Growth (ConCeG) of neural cells for realistic grey matter tissue generation for diffusion MRI simulations

Accurate interpretation of diffusion magnetic resonance imaging (dMRI) signals in grey matter (GM) remains challenging due to the complex, heterogeneous, and densely packed cellular environment. Numerical phantoms provide a controlled framework for investigating the relationship between microstructure and diffusion signals, yet existing approaches often lack the morphological realism and multi-cellular organisation required to faithfully represent GM tissue. In this work, we introduce Contextual Cellular Growth (ConCeG), a generative framework for creating individual cells or constructing dense, three-dimensional, multi-cellular GM substrates informed by real neuronal and glial morphologies. The method combines topological neuron synthesis with a spatially constrained growth network, allowing for the controlled generation of heterogeneous cellular environments with realistic intra- and extracellular compartments. Synthetic cells are generated using morphological and topological characteristics derived from biological reconstructions. We validate the framework through comparisons of structural features with real cellular data, demonstrating strong agreement in branch order, length, angle, and tortuosity distributions. Power spectrum analysis further shows that both intracellular compartments reproduce the spatial correlations observed in biological tissue. Together, these results show ConCeG provides a biologically grounded framework for generating grey matter substrates suitable for large scale diffusion MRI simulation.

physics.med-ph

Magnetic Field of Firing Neuron in Humans: Measurable by Quantum Sensing MRI?

Firing neurons generate action potentials that propagate along axons to transmit signals supporting cognitive functions. These electrical currents generate magnetic fields, yet direct detection of these neuronal magnetic fields by MRI remains elusive. This Mini Review investigates why this goal has been proven difficult to achieve and whether an emerging approach, quantum sensing MRI, can overcome the challenge.

physics.med-ph