Artificial Intelligence Augmented Cerebral Nuclear Imaging.
Authors
Affiliations (2)
Affiliations (2)
- School of Dentistry and Medical Sciences, Charles Sturt University, New South Wales, Australia; Department of Radiology, Baylor College of Medicine, TX. Electronic address: [email protected].
- School of Dentistry and Medical Sciences, Charles Sturt University, New South Wales, Australia; Department of Radiology, Stanford Nuclear Medicine, Stanford University, CA.
Abstract
Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significant potential to advance the capabilities of nuclear neuroimaging. The current and emerging applications of ML and DL in the processing, analysis, enhancement and interpretation of SPECT and PET imaging are explored for brain imaging. Key developments include automated image segmentation, disease classification, and radiomic feature extraction, including lower dimensionality first and second order radiomics, higher dimensionality third order radiomics and more abstract fourth order deep radiomics. DL-based reconstruction, attenuation correction using pseudo-CT generation, and denoising of low-count studies have a role in enhancing image quality. AI has a role in sustainability through applications in radioligand design and preclinical imaging while federated learning addresses data security challenges to improve research and development in nuclear cerebral imaging. There is also potential for generative AI to transform the nuclear cerebral imaging space through solutions to data limitations, image enhancement, patient-centered care, workflow efficiencies and trainee education. Innovations in ML and DL are re-engineering the nuclear neuroimaging ecosystem and reimagining tomorrow's precision medicine landscape.