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High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

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Published on: December 16, 2022

FIRST: Fast Iterative Reconstruction Software for (PET) tomography.

J L Herraiz¹, S España, J J Vaquero

¹Dpto. Física Atómica, Molecular y Nuclear, Universidad Complutense de Madrid, Spain.

Physics in Medicine and Biology

|September 6, 2006

Summary

Iterative reconstruction for small animal PET imaging is enhanced by optimizing the system response matrix (SRM) storage. This approach reduces memory needs, enabling faster, high-resolution image reconstruction on standard computers.

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Area of Science:

Medical Imaging
Computational Science

Background:

Iterative reconstruction methods like OSEM offer superior image quality for 3D-PET compared to analytical methods.
High computational cost and large system response matrix (SRM) memory requirements hinder the performance of iterative algorithms in high-resolution PET scanners.

Purpose of the Study:

To develop a method for reducing the memory footprint of the system response matrix (SRM) in high-resolution small animal PET scanners.
To enable near peak computing performance for iterative image reconstruction algorithms on standard hardware.

Main Methods:

Exploited axial, in-plane, and quasi-symmetries to reduce SRM storage requirements below 1 GB.
Stored SRM elements as cubic spline profiles, matched to voxel size during reconstruction ('on-the-fly' calculation).
Combined the benefits of 'on-the-fly' calculation with a fully stored SRM approach.

Main Results:

Reduced SRM memory requirements to below 1 GB, allowing the entire matrix to reside in RAM.
Achieved near peak computing performance for the iterative reconstruction algorithm.
The 'on-the-fly' SRM calculation added 10-30% to reconstruction time, depending on voxel count.

Conclusions:

The proposed technique effectively reduces memory demands for high-resolution PET SRMs.
This method allows iterative reconstruction algorithms to operate at near-peak performance on standard computers.
The approach is a feasible solution for improving image quality and reconstruction speed in small animal PET imaging.