Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2411.05287

In-Silico Analysis of Curve Fitting in Angiographic Parametric Imaging in Intracranial Aneurysms

Parmita Mondal·Allison Shields·Mohammad Mahdi Shiraz Bhurwani·Kyle A Williams·Ciprian N Ionita
View original source ↗Read source document ↗Export metadata ↓

Abstract

In Angiographic Parametric Imaging (API), accurate estimation of parameters from Time Density Curves (TDC) is crucial. However, these estimations are often marred by errors arising from factors such as patient motion, procedural preferences, image noise, and injection variability. While fitting methods like gamma-variate fitting offer a solution to recover incomplete or corrupted TDC data, they might also introduce unforeseen biases. This study investigates the trade-offs and benefits of employing gamma-variate fitting on virtual angiograms to enhance the precision of API biomarkers. Utilizing Computational Fluid Dynamics (CFD) in patient specific 3D geometries, we generated a series of high-definition virtual angiograms at distinct inlet velocities: 0.25m/s, 0.35m/s, and 0.45m/s. These velocities were investigated across injection durations ranging from 0.5s to 2.0s. From these angiograms, TDCs for aneurysms and their corresponding inlets were constructed. To emulate typical clinical challenges, we introduced noise, simulated patient motion, and generated temporally incomplete data sets. These modified TDCs underwent gamma-variate fitting. We quantified both the original and fitted TDC curves using standard angiography metrics such as Cross-Correlation (Cor), Time to Peak (TTP), Mean Transit Time (MTT), Peak Height (PH), Area Under the Curve (AUC), and Maximum Gradient (Max-Gr) for a comprehensive comparison. TDCs enhanced by gamma-variate fitting exhibited a robust correlation with vascular flow dynamics. Our results affirm that gamma-variate fitting can adeptly restore TDCs from fragmentary sequences, elevating the precision of derived API parameters.

Explore related subjects

physics.med-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Parmita Mondal, Allison Shields, Mohammad Mahdi Shiraz Bhurwani, Kyle A Williams, Ciprian N Ionita. 2024-11-18. In-Silico Analysis of Curve Fitting in Angiographic Parametric Imaging in Intracranial Aneurysms. https://doi.org/10.1117/12.3006497

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗
arXiv2026

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↗