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

Related Concept Videos

Computed Tomography

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

Deep Learning Super-Resolution from Normal to Ultra-High Resolution CT: Conditional Diffusion Model Development and Performance Evaluation in Trabecular Bone Radiomics.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same author

A Conditional Generative Diffusion Model of Trabecular Bone with Tunable Microstructure.

Proceedings of SPIE--the International Society for Optical Engineering·2025

Same author

Robot-Assisted Reduction of the Ankle Joint via Multi-Body 3D-2D Image Registration.

IEEE transactions on medical robotics and bionics·2025

Same author

Effects of non-stationary blur on texture biomarkers of bone using Ultra-High Resolution CT.

Proceedings of SPIE--the International Society for Optical Engineering·2024

Same author

Performance assessment of surgical tracking systems based on statistical process control and longitudinal QA.

Computer assisted surgery (Abingdon, England)·2023

Same author

Multi-Stage Adaptive Spline Autofocus (MASA) with a Learned Metric for Deformable Motion Compensation in Interventional Cone-Beam CT.

Proceedings of SPIE--the International Society for Optical Engineering·2023

Search research articles

Related Experiment Video

Updated: Mar 5, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion.

A Sisniega¹, J W Stayman¹, J Yorkston²

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, United States of America.

Physics in Medicine and Biology

|March 23, 2017

Summary

This study introduces an image-based method to reduce motion blur in cone-beam CT (CBCT) for musculoskeletal imaging. The technique significantly improves image quality, enabling detailed bone microarchitecture assessment even with patient movement.

More Related Videos

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Related Experiment Videos

Last Updated: Mar 5, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Area of Science:

Medical Imaging
Biomedical Engineering
Radiology

Background:

Involuntary patient motion degrades Cone-beam CT (CBCT) image quality, particularly for high-resolution musculoskeletal imaging.
Subtle motion blur can hinder quantitative assessment of bone microarchitecture, crucial for diagnosing conditions like osteoporosis.
Existing motion compensation methods often require external hardware or prior imaging, limiting their applicability.

Purpose of the Study:

To develop and evaluate a purely image-based motion compensation method for musculoskeletal CBCT.
To reduce motion artifacts and blurring without fiducials, tracking hardware, or prior images.
To enable high-resolution quantitative imaging of bone microarchitecture in the presence of patient motion.

Main Methods:

A statistical optimization algorithm (CMA-ES) estimates motion trajectories by optimizing an objective function.
The objective function combines image sharpness (gradient variance) and motion trajectory regularization.
The method uses volumes of interest (VOIs) for rigid motion estimation, adaptable for complex motions with multiple VOIs.

Main Results:

Significant reduction in motion-induced artifacts and blurring across motion amplitudes from 0.5 mm to 10 mm.
Structure Similarity Index (SSIM) improved from 0.86 to 0.97 for 0.5 mm motion and 0.52 to 0.87 for 10 mm motion in simulations.
Experimental and clinical data showed dramatic artifact reduction, improved delineation of tissue boundaries, and trabecular structures.

Conclusions:

The proposed image-based motion compensation method effectively reduces motion artifacts in musculoskeletal CBCT.
This technique supports advanced applications like quantitative assessment of subchondral bone architecture and imaging under load.
It offers a practical solution for motion management in extremity CBCT where immobilization is challenging.