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Relative Motion Analysis using Rotating Axes - Acceleration

Relative Motion Analysis using Rotating Axes - Acceleration

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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
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Relative Motion Analysis using Rotating Axes

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Related Experiment Video

Updated: Jun 11, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU.

Wai-Man Pang¹, Jing Qin, Yuqiang Lu

¹Spatial Media Group, Computer Arts Lab, University of Aizu, Aizuwakamatsu, Japan.

International Journal of Computer Assisted Radiology and Surgery

|July 1, 2010

Summary

This summary is machine-generated.

This study accelerates computed tomography (CT) reconstruction using a graphics processing unit (GPU) for faster, high-quality imaging. The new method achieves near real-time 3D CT volume presentation for physicians.

Related Experiment Videos

Last Updated: Jun 11, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Area of Science:

Medical Imaging
Computer Science
Computational Physics

Background:

Computed tomography (CT) reconstruction is computationally intensive.
The Simultaneous Algebraic Reconstruction Technique (SART) is a common algorithm for CT image reconstruction.
Motion artifacts can degrade CT image quality.

Purpose of the Study:

To accelerate SART with motion compensation for faster and higher-quality CT reconstruction.
To leverage CUDA-enabled GPUs for enhanced CT reconstruction performance.

Main Methods:

Developed a ray-driven projection with hardware trilinear interpolation for CUDA architecture.
Implemented a voxel-driven back-projection utilizing CUDA shared memory to avoid redundant computations.
Integrated motion compensation by rectifying ray directions based on a motion vector field.

Main Results:

Achieved a processing rate of nearly 100 projections/s, approximately 150 times faster than CPU-based SART.
Demonstrated faster reconstruction without compromising image quality, validated by PSNR, SNR, and MSE metrics.
Successfully eliminated motion artifacts like blurring in reconstructed volumes using a simulated dataset.

Conclusions:

The proposed GPU-accelerated SART with motion compensation offers significant speed improvements.
The method has the potential for instantaneous 3D CT volume visualization for clinical applications.