Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 28, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Comparative Evaluation of Machine Learning and Conventional Material Decomposition Algorithms for Spectral Chest

Sriharsha Marupudi¹, Bahaa Ghammraoui¹

¹Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Labs, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Scatter Removal in Photon-Counting Dual-Energy Chest X-Ray Imaging Using a Moving Block Method: A Simulation Phantom Study.

Sensors (Basel, Switzerland)·2025

Same author

Evaluation of charge summing correction in CdTe-based photon-counting detectors for breast CT: performance metrics and image quality.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach.

Biomedical physics & engineering express·2024

Same author

Characterization of mechanical stiffness using additive manufacturing and finite element analysis: potential tool for bone health assessment.

3D printing in medicine·2023

Same author

Inclusion of a GaAs detector model in the Photon Counting Toolkit software for the study of breast imaging systems.

PloS one·2023

Same author

Classification of breast microcalcifications with GaAs photon-counting spectral mammography using an inverse problem approach.

Biomedical physics & engineering express·2023

Photon-counting detectors (PCDs) in spectral radiography offer bone/soft-tissue separation. Machine learning algorithms, particularly SVR and MLP, show improved quantitative accuracy and low-contrast detectability with denser calibration and more energy thresholds.

Area of Science:

Medical Imaging Physics
Computational Imaging
Radiographic Detector Technology

Background:

Spectral chest radiography using photon-counting detectors (PCDs) allows for energy-resolved imaging, crucial for differentiating bone and soft tissues.
The quantitative accuracy of PCD-based spectral imaging is significantly influenced by detector cross-talk and the choice of material decomposition algorithms.

Purpose of the Study:

To compare the quantitative performance and low-contrast detectability of a conventional polynomial decomposition model against two machine learning (ML) algorithms (MLP and SVR) for cadmium telluride (CdTe) PCD spectral chest radiography.
To evaluate the impact of varying calibration grid densities, energy thresholds, and radiation dose levels on the performance of these decomposition methods.

Main Methods:

A physics-based simulation incorporating a Geant4-derived detector response model, charge transport, charge sharing, and Poisson noise was developed.

Keywords:

Monte Carlo Poisson statistics chest radiography low-contrast detectability machine learning material decomposition photon counting

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Related Experiment Videos

Last Updated: May 28, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Quantitative bias and RMSE were assessed using digital phantoms with known material compositions (aluminum, PMMA).

Task-based low-contrast detectability was evaluated using the exponential transformation of the free-response operating characteristic (EFROC) method.

Main Results:

Polynomial decomposition demonstrated stability with sparse calibration, while ML methods (MLP, SVR) significantly benefited from denser calibration grids and increased energy thresholds.
Support vector regression (SVR) yielded the lowest root mean square error (RMSE) under dense calibration conditions.
Multilayer perceptron (MLP) produced smoother image maps and enhanced soft-tissue detectability at low-to-intermediate radiation doses.

Conclusions:

Machine learning algorithms, especially SVR and MLP, offer superior quantitative accuracy and low-contrast detectability in PCD spectral chest radiography compared to traditional polynomial methods, particularly with optimized calibration and energy threshold settings.
Practical trade-offs exist between the calibration data requirements, achievable quantitative precision, and the detectability of subtle soft-tissue structures when implementing ML-based decomposition algorithms for PCD spectral imaging.