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Related Concept Videos

Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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Updated: Nov 19, 2025

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
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Artificial Intelligence for Response Evaluation With PET/CT.

Lise Wei1, Issam El Naqa1

  • 1Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, MI.

Seminars in Nuclear Medicine
|January 29, 2021
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Summary
This summary is machine-generated.

Positron emission tomography (PET)/computed tomography (CT) imaging, combined with machine learning, enhances cancer staging and treatment monitoring. These advanced techniques analyze complex imaging patterns for personalized oncology radiotherapy.

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Area of Science:

  • Nuclear medicine and diagnostic imaging
  • Computational oncology and artificial intelligence

Background:

  • Positron emission tomography (PET)/computed tomography (CT) are crucial nuclear diagnostic imaging modalities for cancer staging and monitoring.
  • These techniques detect biochemical and physiological abnormalities before anatomical changes, aiding disease progression assessment and treatment planning.
  • Quantitative image analysis in nuclear imaging has evolved from simple normalization to complex pattern extraction using advanced algorithms.

Purpose of the Study:

  • To review the application of image processing and machine/deep learning techniques in PET/CT imaging.
  • To focus specifically on the oncological radiotherapy domain as a case study.
  • To highlight the current status and future potential of these technologies in cancer care.

Main Methods:

  • Review of existing literature and research on image processing and machine/deep learning applied to PET/CT.
  • Analysis of quantitative image analysis methods, including standard uptake value normalization and complex pattern extraction.
  • Case study focus on the oncological radiotherapy domain, drawing examples from published work.

Main Results:

  • PET/CT imaging, enhanced by sophisticated image processing and machine learning, offers advanced capabilities for cancer management.
  • These techniques enable more precise identification of tumor volume, disease monitoring, and outcome prediction.
  • The integration of AI and image analysis is transforming personalized treatment regimens in oncology.

Conclusions:

  • Image processing and machine/deep learning significantly enhance the utility of PET/CT in oncology, particularly in radiotherapy.
  • These advanced analytical tools are pivotal for personalized medicine, improving treatment efficacy and patient outcomes.
  • Continued research and development hold substantial promise for further advancements in oncological imaging and treatment.