Artificial intelligence in four-dimensional imaging for motion management in radiation therapy

Area of Science:

• Medical Imaging
• Radiation Oncology
• Artificial Intelligence

Background:

• Four-dimensional imaging (4D-imaging) is crucial for precise motion management in radiation therapy.
• Current 4D-imaging methods face challenges including long scan times, poor image quality, and inaccurate motion estimation.

Purpose of the Study:

• To provide a comprehensive review of artificial intelligence (AI) applications in 4D-imaging for radiation therapy.
• To explore how AI can overcome existing limitations and enhance motion management.
• To discuss future directions and unresolved challenges in AI-driven 4D-imaging.

Main Methods:

• Review of current literature on AI and 4D-imaging in radiation therapy.
• Analysis of AI techniques, particularly deep learning, applied to overcome 4D-imaging challenges.
• Discussion of various 4D-imaging modalities and their integration with AI.

Main Results:

• AI demonstrates significant potential to improve accuracy and efficiency in 4D-imaging.
• Deep learning methods can address issues like long imaging times and suboptimal image quality.
• AI offers a path to enhanced motion management without requiring hardware modifications.

Conclusions:

• AI-based methods are poised to revolutionize 4D-imaging for radiation therapy.
• Further research is needed to address remaining challenges and fully realize AI's potential in this field.
• AI integration promises more precise and efficient cancer treatment through improved motion management.