Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends

SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncological diseases. However, SPECT imaging suffers from limited quantitative accuracy due to low spatial resolution and high noise levels, posing significant challenges for precise diagnosis, disease monitoring, and treatment planning. Recent advances in artificial intelligence (AI), in particular deep learning-based techniques such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, have led to substantial improvements in SPECT image reconstruction, enhancement, attenuation correction, segmentation, disease classification, and multimodal fusion. These AI approaches have enabled more accurate extraction of functional and anatomical information, improved quantitative analysis, and facilitated the integration of SPECT with other imaging modalities to enhance clinical decision-making. This review provides a comprehensive overview of AI-driven developments in SPECT imaging, highlighting progress in both supervised and unsupervised learning approaches, innovations in image synthesis and cross-modality learning, and the potential of self-supervised and contrastive learning strategies for improving model robustness. Additionally, we discuss key challenges, including data heterogeneity, model interpretability, and computational complexity, which continue to limit the clinical adoption of AI methods. The need for standardized evaluation metrics, large-scale multimodal datasets, and clinically validated AI models remains a crucial factor in ensuring the reliability and generalizability of AI approaches. Future research directions include the exploration of foundation models and large language models for knowledge-driven image analysis, as well as the development of more adaptive and personalized AI frameworks tailored for nuclear imaging applications.

SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncological diseases. However, SPECT imaging suffers from limited quantitative accuracy due to low spatial resolution and high noise levels, posing significant challenges for precise diagnosis, disease monitoring, and treatment planning. Recent advances in artificial intelligence (AI), in particular deep learning-based techniques such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, have led to substantial improvements in SPECT image reconstruction, enhancement, attenuation correction, segmentation, disease classification, and multimodal fusion. These AI approaches have enabled more accurate extraction of functional and anatomical information, improved quantitative analysis, and facilitated the integration of SPECT with other imaging modalities to enhance clinical decision-making. This review provides a comprehensive overview of AI-driven developments in SPECT imaging, highlighting progress in both supervised and unsupervised learning approaches, innovations in image synthesis and cross-modality learning, and the potential of self-supervised and contrastive learning strategies for improving model robustness. Additionally, we discuss key challenges, including data heterogeneity, model interpretability, and computational complexity, which continue to limit the clinical adoption of AI methods. The need for standardized evaluation metrics, large-scale multimodal datasets, and clinically validated AI models remains a crucial factor in ensuring the reliability and generalizability of AI approaches. Future research directions include the exploration of foundation models and large language models for knowledge-driven image analysis, as well as the development of more adaptive and personalized AI frameworks tailored for nuclear imaging applications.