Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Myocardial perfusion imaging (MPI), utilizing SPECT or PET, is a cornerstone in cardiac diagnostics for disease detection and risk stratification.
• The MPI workflow involves multiple stages, presenting opportunities for technological enhancement.

Purpose of the Study:

• To review recent advancements in artificial intelligence (AI) applications within the myocardial perfusion imaging (MPI) workflow.
• To discuss future trends and maximize the clinical utility of AI in cardiac imaging.

Main Methods:

• Review of current literature on AI applications in MPI, covering image acquisition, reconstruction, and analysis.
• Exploration of AI's role in segmentation, attenuation correction, and risk prediction models.

Main Results:

• AI can improve image quality, reduce radiation exposure, and shorten acquisition times in MPI.
• AI facilitates motion correction, image registration, and attenuation correction during image reconstruction.
• AI aids in segmenting anatomical features and generating synthetic attenuation imaging.
• AI assists in disease diagnosis and risk prediction by integrating diverse clinical and imaging variables.

Conclusions:

• AI offers significant potential to enhance various stages of the MPI workflow, from image acquisition to clinical reporting.
• Future applications of AI in MPI promise to maximize diagnostic accuracy and prognostic capabilities, especially with hybrid imaging techniques.