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Related Concept Videos

Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the time...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Curvilinear Motion: Polar Coordinates01:27

Curvilinear Motion: Polar Coordinates

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Equation of Motion for a Rigid Body

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Related Experiment Video

Updated: May 17, 2026

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
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Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

Gradient-based rigid motion correction in CBCT via Lie algebra-constrained registration.

Haotao Jiang1, Fawei He2, Siman Huang1

  • 1Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.

Physics in Medicine and Biology
|May 15, 2026
PubMed
Summary

This study introduces a novel framework to correct patient motion during cone-beam computed tomography (CBCT) scans. The method accurately estimates and compensates for rigid motion, significantly improving image quality and diagnostic reliability.

Keywords:
CBCTLie group manifolddifferentiable projection geometrygradientrigid motion correction

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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Published on: November 23, 2019

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Last Updated: May 17, 2026

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
06:09

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Published on: March 12, 2021

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

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

Published on: November 23, 2019

Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Imaging

Background:

  • Cone-beam computed tomography (CBCT) scanning speed is limited by hardware, leading to patient motion artifacts.
  • Rigid motion during scanning degrades image quality and compromises diagnostic reliability.
  • Accurate motion estimation and compensation are crucial for high-quality CBCT reconstruction.

Purpose of the Study:

  • To develop a general framework for correcting rigid motion in CBCT imaging.
  • To improve the diagnostic reliability of CBCT by mitigating motion-induced artifacts.
  • To enhance downstream image analysis tasks through motion-compensated reconstruction.

Main Methods:

  • A differentiable 3D-2D registration approach for motion estimation.
  • A Lie group manifold-constrained motion modeling for rigid transformations.
  • Analytical derivation of the CBCT forward projection gradient and a novel motion estimation constraint.

Main Results:

  • The proposed framework achieves superior motion estimation accuracy compared to state-of-the-art methods.
  • Demonstrated effective recovery of anatomical details and substantial reduction of motion artifacts.
  • Successful image restoration even under severe rigid motion conditions.

Conclusions:

  • The developed framework provides a robust solution for rigid motion correction in CBCT.
  • This advancement supports improved diagnostic accuracy and reliability in clinical practice.
  • The method shows significant potential for enhancing CBCT image quality in challenging scenarios.