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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep learning model for automatic image quality assessment in PET.

Haiqiong Zhang^1,2, Yu Liu¹, Yanmei Wang³

¹Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.

BMC Medical Imaging

|June 5, 2023

Summary

This summary is machine-generated.

This study developed a deep learning (DL) model to automatically assess positron emission tomography (PET) image quality. The DL tool reliably distinguishes between good and poor quality PET scans, potentially accelerating clinical research.

Keywords:

Classification Deep learning Image quality PET

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Positron emission tomography (PET) image quality can be degraded by external factors, leading to inconsistent research results.
Developing automated methods for PET image quality assessment (QA) is crucial for reliable clinical research.

Purpose of the Study:

To explore a deep learning (DL) based method for automated PET image quality assessment.
To develop a tool that can reliably differentiate between optimal and poor quality PET images.

Main Methods:

A dataset of 89 PET images was collected and graded by senior radiologists.
A Dense Convolutional Network (DenseNet) model was trained to classify image quality.
Performance was evaluated using accuracy, sensitivity, specificity, and ROC analysis with fivefold cross-validation.

Main Results:

Task 4, focusing on differentiating poor (grades 1-2) from good (grades 3-5) quality images, demonstrated the best performance.
The automated quality assessment for Task 4 achieved high accuracy (0.85), specificity (0.79), and sensitivity (0.91) on the test set.
The model achieved an Area Under the ROC Curve (AUC) of 0.91 in the test set, indicating strong discriminative ability.

Conclusions:

Deep learning models are feasible for assessing PET image quality.
This automated QA tool can assist in accelerating clinical research by providing reliable image quality assessments.