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Updated: May 22, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

A multi-thread scheduling method for 3D CT image reconstruction using multi-GPU.

Yining Zhu¹, Yunsong Zhao, Xing Zhao

¹The CT Laboratory, School of Mathematical Sciences, Capital Normal University, Beijing, China.

Journal of X-Ray Science and Technology

|May 29, 2012

Summary

This summary is machine-generated.

This study introduces a Multi-Thread Scheduling (MTS) method for 3D CT image reconstruction, optimizing computation and storage. The novel approach significantly reduces overall processing time, matching data storage duration.

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

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Last Updated: May 22, 2026

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

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

Published on: November 23, 2019

Area of Science:

Medical Imaging
Computer Science
Computational Science

Background:

3D CT image reconstruction involves computationally intensive calculations and significant data storage.
Existing methods often face bottlenecks in balancing computation and storage, leading to prolonged processing times.

Purpose of the Study:

To propose and evaluate a novel Multi-Thread Scheduling (MTS) method for optimizing the complete 3D CT image reconstruction process.
To achieve simultaneous computation and data storage, thereby reducing overall reconstruction time.

Main Methods:

Developed a Multi-Thread Scheduling (MTS) method integrating GPU computation and hard disk data storage.
Implemented multi-threads to manage GPU computations and a dedicated thread for data storage, enabling parallel processing.
Utilized a 4-channel texture within the CUDA framework to maintain symmetrical projection data, enhancing computational efficiency.

Main Results:

The MTS method allows for simultaneous execution of computation and data storage.
Numerical experiments demonstrated that the total processing time using the MTS method is comparable to the data storage time alone.
Significant reduction in calculation time was achieved through the 4-channel texture optimization in the CUDA framework.

Conclusions:

The proposed MTS method effectively balances GPU computation and hard disk storage for 3D CT image reconstruction.
This approach offers a substantial improvement in processing efficiency for CT image reconstruction tasks.
The integration of parallel processing and optimized data handling presents a viable solution for accelerating medical imaging workflows.