Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images. Seminars in nuclear medicine Zaharchuk, G., Davidzon, G. 2021; 51 (2): 134–42


Artificial intelligence (AI) has recently attracted much attention for its potential use in healthcare applications. The use of AI to improve and extract more information out of medical images, given their parallels with natural images and the immense progress in the area of computer vision, has been at the forefront of these advances. This is due to a convergence of factors, including the increasing numbers of scans performed, the availability of open source AI tools, and decreases in the costs of hardware required to implement these technologies. In this article, we review the progress in the use of AI toward optimizing PET/CT and PET/MRI studies. These two methods, which combine molecular information with structural and (in the case of MRI) functional imaging, are extremely valuable for a wide range of clinical indications. They are also tremendously data-rich modalities and as such are highly amenable to data-driven technologies such as AI. The first half of the article will focus on methods to improve PET reconstruction and image quality, which has multiple benefits including faster image acquisition, image reconstruction, and lower or even "zero" radiation dose imaging. It will also address the value of AI-driven methods to perform MR-based attenuation correction. The second half will address how some of these advances can be used to perform to optimize diagnosis from the acquired images, with examples given for whole-body oncology, cardiology, and neurology indications. Overall, it is likely that the use of AI will markedly improve both the quality and safety of PET/CT and PET/MRI as well as enhance our ability to interpret the scans and follow lesions over time. This will hopefully lead to expanded clinical use cases for these valuable technologies leading to better patient care.

View details for DOI 10.1053/j.semnuclmed.2020.10.001

View details for PubMedID 33509370