Artificial Intelligence in PET: An Industry Perspective

Arkadiusz Sitek¹, Sangtae Ahn², Evren Asma³, Adam Chandler⁴, Alvin Ihsani⁵, Sven Prevrhal⁶, Arman Rahmim⁷, Babak Saboury⁸, Kris Thielemans⁹

Affiliations

¹ Sano Centre for Computational Medicine, Nawojki 11 Street, Kraków 30-072, Poland. Electronic address: a.sitek@sanoscience.org.
² GE Research, 1 Research Circle KWC-1310C, Niskayuna, NY 12309, USA.
³ Canon Medical Research, 706 N Deerpath Drive, Vernon Hills, IL 60061, USA.
⁴ Global Scientific Collaborations Group, United Imaging Healthcare, America, 9230 Kirby Drive, Houston, TX 77054, USA.
⁵ NVIDIA, 2 Technology Park Drive, Westford, MA 01886, USA.
⁶ Philips Research Europe, Röntgenstr. 22, Hamburg 22335, Germany.
⁷ Department of Radiology, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada; Department of Physics, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada.
⁸ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
⁹ Institute of Nuclear Medicine, University College London, UCL Hospital Tower 5, 235 Euston Road, London NW1 2BU, UK; Algorithms and Software Consulting Ltd, 10 Laneway, London SW15 5HX, UK.

PMID: 34353746
DOI: 10.1016/j.cpet.2021.06.006

Review

Artificial Intelligence in PET: An Industry Perspective

Arkadiusz Sitek et al. PET Clin. 2021 Oct.

. 2021 Oct;16(4):483-492.

doi: 10.1016/j.cpet.2021.06.006. Epub 2021 Aug 3.

Authors

Arkadiusz Sitek¹, Sangtae Ahn², Evren Asma³, Adam Chandler⁴, Alvin Ihsani⁵, Sven Prevrhal⁶, Arman Rahmim⁷, Babak Saboury⁸, Kris Thielemans⁹

Affiliations

¹ Sano Centre for Computational Medicine, Nawojki 11 Street, Kraków 30-072, Poland. Electronic address: a.sitek@sanoscience.org.
² GE Research, 1 Research Circle KWC-1310C, Niskayuna, NY 12309, USA.
³ Canon Medical Research, 706 N Deerpath Drive, Vernon Hills, IL 60061, USA.
⁴ Global Scientific Collaborations Group, United Imaging Healthcare, America, 9230 Kirby Drive, Houston, TX 77054, USA.
⁵ NVIDIA, 2 Technology Park Drive, Westford, MA 01886, USA.
⁶ Philips Research Europe, Röntgenstr. 22, Hamburg 22335, Germany.
⁷ Department of Radiology, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada; Department of Physics, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada.
⁸ Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
⁹ Institute of Nuclear Medicine, University College London, UCL Hospital Tower 5, 235 Euston Road, London NW1 2BU, UK; Algorithms and Software Consulting Ltd, 10 Laneway, London SW15 5HX, UK.

PMID: 34353746
DOI: 10.1016/j.cpet.2021.06.006

Abstract

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 article 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.

Keywords: AI; Ecosystem; Industry; List-mode; PET; Workflow.

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Artificial Intelligence in PET: An Industry Perspective

Affiliations

Artificial Intelligence in PET: An Industry Perspective

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Abstract

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