Potentials and caveats of AI in Hybrid Imaging

Shiyam Sundar, Lalith Kumar, Muzik, Otto, Buvat, Irène , Bidaut, Luc and Beyer, Thomas (2020) Potentials and caveats of AI in Hybrid Imaging. Methods . ISSN 1046-2023

Full content URL: https://doi.org/10.1016/j.ymeth.2020.10.004

Potentials and caveats of AI in Hybrid Imaging
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Item Type:Article
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State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.

Keywords:Hybrid Imaging, Artificial Intelligence, Medical Imaging, PET/CT, PET/MR
Subjects:B Subjects allied to Medicine > B100 Anatomy, Physiology and Pathology
G Mathematical and Computer Sciences > G700 Artificial Intelligence
B Subjects allied to Medicine > B820 Radiology
A Medicine and Dentistry > A900 Others in Medicine and Dentistry
F Physical Sciences > F350 Medical Physics
B Subjects allied to Medicine > B800 Medical Technology
Divisions:College of Science
ID Code:42908
Deposited On:09 Nov 2020 15:20

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