Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy
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(2020) 4:17
European Journal of Hybrid Imaging
REVIEW
Open Access
Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy Hossein Arabi1 and Habib Zaidi1,2,3,4* * Correspondence: habib.zaidi@ hcuge.ch 1 Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland 2 Geneva University Neurocenter, Geneva University, CH-1205 Geneva, Switzerland Full list of author information is available at the end of the article
Abstract This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and radiation therapy research. To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed. This review sets out to cover briefly the fundamental concepts of AI and deep learning followed by a presentation of seminal achievements and the challenges facing their adoption in clinical setting. Keywords: Molecular imaging, Radiation therapy, Artificial intelligence, Deep learning, Quantitative imaging
Introduction Artificial intelligence (AI) has attracted considerable attention during the last few years, although it has been around since a few decades. With the introduction of deep learning algorithms, research focusing on multimodality medical imaging has increased exponentially; targeting mainly applications deemed to rely on human intervention/interpretation or handcrafted data preparation/modification (Sim et al. 2020). These algorithms exhibited tremendous potential to effectively learn from data, correctly interpret the data, and successfully accomplish certain tasks following appropriate training. AI is gaining momentum in medicine in general, owing to effective handling of the data overflow, eliminating optimism bias coming from false human generalization based on the individual experiences, management of rare diseases (or frequently overlooked cases), robustness to inter- and intra-person/center variations, and the possibility of being perfectly up-to-date with minor modifications (Nensa et al. 2019). This paper sets out to discuss the conceptual basis of artificial intelligence and its potential clinical applications. The main focus is on the major applications of AI, in particular deep learning approaches, in molecular imaging and radiation therapy fields. In this regard, five generic areas where AI-based solutions have attracted attention and © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons l
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