Artificial Intelligence and PET Imaging, Part 1, An Issue of PET Clinics

Cover image

Title page

Table of Contents

Copyright

Contributors

Forthcoming Issues

CME Accreditation Page

Preface

A Brief History of AI: How to Prevent Another Winter (A Critical Review)

Key points

Introduction

What is artificial intelligence?

History of artificial intelligence

Summary

Disclosure

Anatomy and Physiology of Artificial Intelligence in PET Imaging

Key points

Introduction

Steps of algorithm development

Building blocks of machine learning networks

The convolutional neural network

Network training

The U-Net

Other networks and models

Summary

Artificial Intelligence in PET: An Industry Perspective

Key points

Glossary

Introduction

Challenges for commercialization

Looking into the future of AI in PET

Summary

Clinics care points

Disclosure

Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods:: Framework, Strategies, and Role of the Physician

Key points

Introduction

Framework for objective task-based assessment

Task-based evaluation of artificial intelligence methods: Role of physicians

Example evaluation studies

Discussions and summary

Summary

Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design

Key points

Introduction

Approach

Structural computational modeling

Behavioral computational modeling

Applications

Summary

Clinics care points

Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering

Key points

Introduction

How machine learning works

Identification of optimal site and strategy for labeling

Reaction optimization

Production and other concerns

Summary

Clinics care points

Additional resources

The Evolution of Image Reconstruction in PET: From Filtered Back-Projection to Artificial Intelligence

Key points

Introduction

Traditional PET image reconstruction

Deep learning–based PET image reconstruction

Summary

Clinics care points

Artificial Intelligence–Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography

Key points

Introduction

Artificial intelligence–based methods

Artificial intelligence–based attenuation correction

Artificial intelligence scatter correction

Challenges and opportunities for artificial intelligence approaches

Summary

Clinics care points

Funding

Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement

Key points

Introduction

Summary of existing methods

Emerging approaches and novel applications

Summary

Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging

Key points

Introduction

Artificial intelligence techniques for image segmentation in oncological PET imaging

Solutions to tackle limitations in annotations

Evaluation of artificial intelligence techniques

Further advancements

Summary

Clinics care points

Radiomics in PET Imaging:: A Practical Guide for Newcomers

Key points

Introduction

Checklist to design a reliable radiomic study

How to read an article reporting a radiomics study

Handcrafted radiomics

How to manage heterogeneous data

Handcrafted versus deep features

Summary

Clinics care points

Total-Body PET Kinetic Modeling and Potential Opportunities Using Deep Learning

Key points

Introduction

Total-body dynamic PET and its potential for kinetic modeling

Opportunities using deep learning for total-body kinetic modeling

Summary

Clinics care points

Technical terms

Role of Artificial Intelligence in Theranostics:: Toward Routine Personalized Radiopharmaceutical Therapies

Key points

Introduction

Image-based dosimetry in radiopharmaceutical therapies

The role of artificial intelligence in quantitative imaging

The role of artificial intelligence in image registration and segmentation

The role of artificial intelligence in time activity curve assessment and time integration of activity

The role of artificial intelligence in conversion to absorbed dose

Artificial intelligence and the future of personalized radiopharmaceutical therapy: radiomics, dosiomics, and outcome prediction

Summary

Clinics care points

Equitable Implementation of Artificial Intelligence in Medical Imaging: What Can be Learned from Implementation Science?

Key points

Introduction

The promise of artificial intelligence in medical imaging

Artificial intelligence as a complex intervention

The science of implementation

Challenges in implementing artificial intelligence

Recommendations

Summary

Authors’ contributions