Open Access
CC BY-NC-ND 4.0 · Nuklearmedizin 2023; 62(06): 334-342
DOI: 10.1055/a-2198-0358
Review

Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions

Künstliche Intelligenz und Deep Learning für die Weiterentwicklung der PET-Bildrekonstruktion: Stand der Technik und zukünftige Perspektiven
1   Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
2   Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
3   Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
,
Nils Constantin Hellwig
1   Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
3   Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
,
Steven Boehner
1   Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
2   Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
3   Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
,
Timo Fuchs
1   Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
2   Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
3   Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
,
Regina Fischer
1   Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
2   Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
3   Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
,
1   Department of Nuclear Medicine, University Hospital Regensburg, Regensburg, Germany (Ringgold ID: RIN39070)
› Author Affiliations

Supported by: Bavarian Center for Cancer Research (BZKF) ZB-001
Supported by: Bundesministerium für Bildung und Forschung ABIDE_MI: 01ZZ2061T, DIFUTURE: 01ZZ1804H, NUM: 01KX2121
Preview

Abstract

Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline fundamentals of PET imaging as well as state-of-the-art in AI-based PET IR with its typical algorithms and DL architectures. Advances improve resolution and contrast recovery, reduce noise, and remove artifacts via inferred attenuation and scatter correction, sinogram inpainting, denoising, and super-resolution refinement. Kernel-priors support list-mode reconstruction, motion correction, and parametric imaging. Hybrid approaches combine AI with conventional IR. Challenges of AI-assisted PET IR include availability of training data, cross-scanner compatibility, and the risk of hallucinated lesions. The need for rigorous evaluations, including quantitative phantom validation and visual comparison of diagnostic accuracy against conventional IR, is highlighted along with regulatory issues. First approved AI-based applications are clinically available, and its impact is foreseeable. Emerging trends, such as the integration of multimodal imaging and the use of data from previous imaging visits, highlight future potentials. Continued collaborative research promises significant improvements in image quality, quantitative accuracy, and diagnostic performance, ultimately leading to the integration of AI-based IR into routine PET imaging protocols.



Publication History

Received: 28 August 2023

Accepted after revision: 12 October 2023

Article published online:
23 November 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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