Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1709.08913

K-edge subtraction vs. A-space processing for x-ray imaging of contrast agents: SNR

Robert Alvarez
View original source ↗Read source document ↗Export metadata ↓

Abstract

Purpose: To compare two methods that use x-ray spectral information to image externally administered contrast agents: K-edge subtraction and basis-function decomposition (the A-space method), Methods: The K-edge method uses narrow band x-ray spectra with energies infinitesimally below and above the contrast material K-edge energy. The A-space method uses a broad spectrum x-ray tube source and measures the transmitted spectrum with photon counting detectors with pulse height analysis. The methods are compared by their signal to noise ratio (SNR) divided by the patient dose for an imaging task to decide whether contrast material is present in a soft tissue background. The performance with iodine or gadolinium containing contrast material is evaluated as a function of object thickness and the x-ray tube voltage of the A-space method. Results: For a tube voltages above 60 kV and soft tissue thicknesses from 5 to 25 g/cm^2, the A-space method has a larger SNR per dose than the K-edge subtraction method for either iodine or gadolinium containing contrast agent. Conclusion: Even with the unrealistic spectra assumed for the K-edge method, the A-space method has a substantially larger SNR per patient dose.

Explore related subjects

physics.med-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert Alvarez. 2017-09-26. K-edge subtraction vs. A-space processing for x-ray imaging of contrast agents: SNR. https://arxiv.org/abs/1709.08913

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↗