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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Area of Science:

  • Neuroradiology
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Computed tomography perfusion (CTP) is crucial for stroke patient management and triage.
  • Current CTP analysis software variations may impact patient stratification and clinical outcomes.
  • A comprehensive review of AI in CTP for stroke is lacking.

Purpose of the Study:

  • To review current knowledge and future applications of AI in CTP for stroke patients.
  • To analyze the performance of CTP analysis software and its clinical implications.
  • To discuss the evolution of CTP estimation software, focusing on AI-based solutions.

Main Methods:

  • Literature review of CTP techniques and AI applications in stroke.
  • Analysis of CTP software performance and neuroradiological patient stratification.
  • Overview of AI-based CTP estimation methods and their challenges.

Main Results:

  • AI in CTP is currently mainly used for motion artifact correction.
  • Deconvolution methods remain standard for generating CTP-derived variables.
  • Drawbacks include AI's "black-box" nature, workflow integration issues, and costs.

Conclusions:

  • Future AI integration with clinical data can create patient-specific CTP maps for improved stroke treatment.
  • AI-driven CTP can overcome threshold-based decision limitations, enhancing patient selection and treatment efficiency.
  • Addressing ethical, technical, and standardization issues is vital for AI's full potential in CTP.