The promise of artificial intelligence and deep learning in PET and SPECT imaging

This review sets out to discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. To this end, the underlying limitations/challenges of these imaging modalities are briefly discussed followed by a description of AI-based solutions proposed to address these challenges. This review will focus on mainstream generic fields, including instrumentation, image acquisition/formation, image reconstruction and low-dose/fast scanning, quantitative imaging, image interpretation (computer-aided detection/ diagnosis/prognosis), as well as internal radiation dosimetry. A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the clinic are discussed.

Automated summary

This review discusses the applications of artificial intelligence in SPECT and PET imaging, focusing on addressing challenges in instrumentation, image acquisition/formation, image reconstruction, quantitative imaging, image interpretation, and internal radiation dosimetry. The review also provides a description of deep learning algorithms and fundamental architectures used for these applications, as well as discussing the challenges and opportunities for full-scale validation and adoption of AI-based solutions for improving image quality and quantitative accuracy in PET and SPECT images.