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Related Experiment Video

Updated: Mar 19, 2026

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

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Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis.

P J Markiewicz¹, K Thielemans, J M Schott

¹Translational Imaging Group, CMIC, University College London, London, UK. Institute of Nuclear Medicine, University College London, London, UK.

Physics in Medicine and Biology

|June 10, 2016

Summary

This summary is machine-generated.

This study introduces a fast, scalable GPU-accelerated software for processing positron emission tomography (PET) list-mode data, enabling rapid uncertainty assessment in image reconstruction and analysis within minutes.

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Area of Science:

Medical Imaging
Computational Science
Nuclear Medicine

Background:

Positron emission tomography (PET) list-mode data processing is computationally intensive.
Efficient integration of list-mode data into image reconstruction and analysis workflows is crucial for uncertainty quantification.

Purpose of the Study:

To develop a rapid and scalable software solution for PET list-mode data processing.
To enable efficient uncertainty estimation for image statistics and processing components.
To facilitate quality control and motion detection in PET scans.

Main Methods:

Utilized graphics processing unit (GPU) for all processing tasks.
Implemented streamed and concurrent kernel execution for parallel processing.
Integrated data transfers between disk, CPU, and GPU memory for optimized performance.

Main Results:

Achieved fast generation of multiple bootstrap realisations (under five minutes per realisation).
Enabled estimation of random event data, dynamic sinograms, and variance estimates.
Produced statistics and visualizations for quality control, including count rate curves and center of mass plots.

Conclusions:

The developed software provides a rapid and scalable solution for PET list-mode data processing.
The GPU-accelerated approach significantly reduces time for uncertainty assessment in PET image analysis.
The software facilitates immediate quality control and motion detection, enhancing clinical utility.