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Artificial intelligence (AI) significantly enhances Single-Photon Emission Computed Tomography (SPECT) imaging by improving image quality and quantitative accuracy. This review explores AI

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Area of Science:

  • Nuclear Medicine
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Single-Photon Emission Computed Tomography (SPECT) is crucial for diagnosing cardiovascular, neurological, and oncological diseases.
  • SPECT imaging faces limitations in quantitative accuracy due to low spatial resolution and high noise.
  • These limitations challenge precise diagnosis, disease monitoring, and treatment planning.

Purpose of the Study:

  • To provide a comprehensive overview of AI-driven advancements in SPECT imaging.
  • To highlight progress in various AI techniques and their applications in SPECT.
  • To discuss challenges and future directions for AI in SPECT.

Main Methods:

  • Review of recent literature on AI, particularly deep learning (CNNs, GANs, transformers), applied to SPECT.
  • Analysis of supervised and unsupervised learning approaches, image synthesis, cross-modality learning, self-supervised, and contrastive learning.
  • Discussion of challenges such as data heterogeneity, interpretability, and computational complexity.

Main Results:

  • AI, especially deep learning, has substantially improved SPECT image reconstruction, enhancement, attenuation correction, segmentation, classification, and multimodal fusion.
  • AI enables more accurate extraction of functional and anatomical information and improved quantitative analysis.
  • AI facilitates the integration of SPECT with other imaging modalities for enhanced clinical decision-making.

Conclusions:

  • AI offers significant potential to overcome SPECT imaging limitations, leading to more accurate diagnoses and personalized treatments.
  • Addressing challenges like data heterogeneity, interpretability, and standardization is crucial for clinical adoption.
  • Future research should focus on foundation models, large language models, and adaptive AI frameworks for nuclear imaging.