Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

You might also read

Related Articles

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

Sort by
Same author

Optimal dimensionality and fundamental limits of proton stopping power estimation with photon-counting CT material decomposition.

Physics in medicine and biology·2026
Same author

Trends in age-sex-specific prevalence and incidence of antidepressant dispensation in the Nordic countries: a systematic review.

British journal of clinical pharmacology·2026
Same author

Deep-learning-based spectral motion artifact correction on photon-counting cardiac CT images.

Physics in medicine and biology·2026
Same author

Syn2Real: synthesis of CT image ring artifacts for deep learning-based correction.

Physics in medicine and biology·2025
Same author

Deep learning estimation of proton stopping power with photon-counting computed tomography: a virtual study.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

Noise suppression in photon-counting computed tomography using unsupervised Poisson flow generative models.

Visual computing for industry, biomedicine, and art·2024
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: May 21, 2026

A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
10:23

A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

Published on: June 23, 2023

A framework for evaluating threshold variation compensation methods in photon counting spectral CT.

Mats Persson1, Hans Bornefalk

  • 1Department of Physics, Royal Institute of Technology, Stockholm, Sweden. mats.persson@mi.physics.kth.se

IEEE Transactions on Medical Imaging
|June 20, 2012
PubMed
Summary
This summary is machine-generated.

Photon counting spectral CT detectors face challenges with energy threshold variations causing artifacts. A new affine minimum mean square error estimator offers superior inhomogeneity compensation compared to existing methods, improving image quality.

More Related Videos

Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images
08:02

Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images

Published on: November 15, 2024

Related Experiment Videos

Last Updated: May 21, 2026

A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
10:23

A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

Published on: June 23, 2023

Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images
08:02

Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images

Published on: November 15, 2024

Area of Science:

  • Medical Imaging
  • Detector Physics

Background:

  • Photon counting spectral computed tomography (CT) detectors exhibit variations in energy thresholds across elements.
  • Uncompensated threshold variations lead to problematic ring artifacts in reconstructed CT images.

Purpose of the Study:

  • To systematically compare methods for compensating detector element inhomogeneities in photon counting CT.
  • To introduce and evaluate a novel affine minimum mean square error (AMMSE) estimator for inhomogeneity compensation.

Main Methods:

  • Developed a framework for comparing inhomogeneity compensation methods in photon counting CT.
  • Proposed an AMMSE estimator calibrated using transmission measurements of two materials.
  • Compared the AMMSE estimator against flatfielding and signal-to-thickness calibration via simulation.

Main Results:

  • The proposed AMMSE method outperformed signal-to-thickness calibration and flatfielding for most threshold variation levels.
  • Signal-to-thickness calibration was superior to flatfielding.
  • Substructuring detector elements by depth effectively mitigated threshold variation effects.

Conclusions:

  • The AMMSE estimator provides a robust solution for inhomogeneity compensation in photon counting CT.
  • The developed framework enables effective comparison of different compensation strategies.
  • Depth segmentation of detector elements is a promising approach to counter threshold variations.