Medical imaging plays a crucial role in clinical practice, offering invaluable insights into internal structures and pathologies. Various imaging modalities, such as X-rays, CT scans, MRI, and PET are routinely used for patient examination. However, some of these modalities have notable drawbacks, including high costs, prolonged examination times, and exposure to ionizing radiation. For instance, contrast-enhanced computed tomography (CE-CT) scans often require contrast agents and prolonged X-ray exposure, which can impose a significant burden on patients. Addressing these limitations is essential for improving diagnostic efficiency and patient safety.

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Deep Learning Models for Medical Image Translation

  • Yen-Wei Chen,
  • Lanfen Lin,
  • Rahul Kumar Jain

摘要

Medical imaging plays a crucial role in clinical practice, offering invaluable insights into internal structures and pathologies. Various imaging modalities, such as X-rays, CT scans, MRI, and PET are routinely used for patient examination. However, some of these modalities have notable drawbacks, including high costs, prolonged examination times, and exposure to ionizing radiation. For instance, contrast-enhanced computed tomography (CE-CT) scans often require contrast agents and prolonged X-ray exposure, which can impose a significant burden on patients. Addressing these limitations is essential for improving diagnostic efficiency and patient safety.