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Positron Emission Tomography

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...

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Related Experiment Video

Updated: Oct 4, 2025

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

A Basic Positron Emission Tomography System Constructed to Locate a Radioactive Source in a Bi-dimensional Space

Published on: February 1, 2016

Deep-learning-based fast TOF-PET image reconstruction using direction information.

Kibo Ote¹, Fumio Hashimoto²

¹Central Research Laboratory, Hamamatsu Photonics K.K, Hamamatsu, 434-8601, Japan.

Radiological Physics and Technology

|February 8, 2022

Summary

This summary is machine-generated.

This study introduces a new deep learning method for time-of-flight positron emission tomography (TOF-PET) image reconstruction. The novel approach enhances image quality and speeds up reconstruction by utilizing event direction information.

Keywords:

Deep learning Direction information Image reconstruction Positron emission tomography Time-of-flight

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

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

Published on: December 16, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Nuclear Medicine

Background:

Deep learning shows promise for Positron Emission Tomography (PET) image reconstruction.
Current methods require further improvements in image quality.

Purpose of the Study:

To develop a novel Convolutional Neural Network (CNN)-based method for Time-of-Flight PET (TOF-PET) image reconstruction.
To leverage the direction information of coincidence events for enhanced reconstruction.

Main Methods:

A 3D CNN model was developed to process view-grouped histo-images as multi-channel input.
The method incorporates event direction information directly into the reconstruction process.
Evaluation was performed using Monte Carlo simulation data from a digital brain phantom.

Main Results:

The proposed method demonstrated improvements in Peak Signal-to-Noise Ratio (PSNR) by 1.2 dB and Structural Similarity (SSIM) by 0.02 at a 300 ps coincidence time resolution.
Calculation times were significantly reduced compared to conventional iterative reconstruction methods.
Enhanced image quality and faster reconstruction were achieved by utilizing event direction information.

Conclusions:

The novel CNN-based TOF-PET reconstruction method effectively improves both image quality and reconstruction speed.
This approach offers a promising advancement for TOF-PET imaging applications.