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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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Eliminating CT radiation for clinical PET examination using deep learning.

Qingneng Li¹, Xiaohua Zhu², Sijuan Zou²

¹Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

European Journal of Radiology

|June 29, 2022

Summary

This study introduces a deep learning method to create pseudo-CT images from PET scans, reducing radiation exposure. The AI accurately generates attenuation corrected PET and CT images, aiding clinical applications like radiotherapy.

Keywords:

Attenuation correction Clinical examination Computed tomography Deep learning Positron emission tomography

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiotherapy

Background:

Clinical PET/CT uses CT for localization and attenuation correction, increasing radiation risk.
Developing low-dose imaging techniques is crucial for patient safety.

Purpose of the Study:

To propose a deep learning framework for estimating PET attenuation maps and generating pseudo-CT images.
To reduce ionizing radiation exposure in clinical PET/CT examinations.

Main Methods:

A pix2pix deep learning model was trained on 5760 PET-CT slice pairs.
The model learned the mapping between non-attenuation corrected (NAC) PET and attenuation corrected (AC) PET images.
Generated PET attenuation maps (µ-maps) were used to synthesize CT images.

Main Results:

Synthetic AC PET images showed high quantitative performance (NRMSE: 2.20%, PSNR: 34.03 dB, SSIM: 97.90%, PCC: 98.45%).
Radiologists found the synthetic CT and AC PET images acceptable for clinical observation.
The framework successfully generated realistic CT images from PET-derived µ-maps.

Conclusions:

The deep learning framework offers a promising approach for low-dose PET/CT imaging.
This method has potential clinical applications in radiotherapy and other areas requiring reduced radiation.
Accurate pseudo-CT generation can enhance diagnostic capabilities while minimizing patient radiation dose.