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Potentials and caveats of AI in hybrid imaging. | LitMetric

Potentials and caveats of AI in hybrid imaging.

Methods

QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria. Electronic address:

Published: April 2021

AI Article Synopsis

  • Hybrid imaging techniques like PET/MRI and PET/CT integrate anatomical and functional data in one exam, enhancing patient management.
  • The rise of these advanced imaging modalities leads to challenges in data overload, necessitating new methods for extracting meaningful clinical insights.
  • AI technology plays a key role in addressing these challenges by improving image analysis and processing while also presenting its own set of pitfalls that must be navigated for effective, reproducible research.

Article Abstract

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.

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Source
http://dx.doi.org/10.1016/j.ymeth.2020.10.004DOI Listing

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