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Artificial Intelligence in PET: an Industry Perspective

arXiv 2107.06747 ↗First posted 14 Jul 2021cs.CV cs.AIcited by 0

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Abstract (arXiv)

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This paper provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI, and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom designed data processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

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References · 8

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  3. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 496
  4. 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 212
  5. 2019Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systemscited by 153
  6. 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 95
  7. 2020The Case for Bayesian Deep Learningcited by 69
  8. 2020Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis during CCTA Evaluation of Chest-Pain in the Emergency Department: Preparing an Application for Real-World Usecited by 2

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