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Positron Emission Tomography01:29

Positron Emission Tomography

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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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Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

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Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
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Related Experiment Video

Updated: Jun 25, 2025

Radiosynthesis, Quality Control, and Small Animal Positron Emission Tomography Imaging of 68Ga-Labelled Nano Molecules
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Radiosynthesis, Quality Control, and Small Animal Positron Emission Tomography Imaging of 68Ga-Labelled Nano Molecules

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Exploring and Exploiting Multi-Modality Uncertainty for Tumor Segmentation on PET/CT.

Susu Kang, Yixiong Kang, Shan Tan

    IEEE Journal of Biomedical and Health Informatics
    |May 22, 2024
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    Summary
    This summary is machine-generated.

    This study explores multi-modality uncertainties for PET/CT tumor segmentation, introducing a novel loss function. The new method improves segmentation accuracy and reduces prediction uncertainty, enhancing clinical safety.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computational Biology

    Background:

    • Deep learning excels in multi-modality segmentation but often yields deterministic outputs, lacking crucial uncertainty estimation.
    • Over-confident predictions from deterministic models pose risks in safety-critical clinical settings like tumor segmentation.
    • Existing uncertainty estimation methods predominantly focus on single-modality networks, leaving multi-modality uncertainties underexplored.

    Purpose of the Study:

    • To investigate multi-modality uncertainties in PET/CT tumor segmentation.
    • To assess the benefits and characteristics of multi-modality uncertainties compared to single-modality approaches.
    • To develop a novel uncertainty-driven loss function for improved multi-modal tumor segmentation.

    Main Methods:

    • Assessed four established uncertainty estimation approaches for multi-modality PET/CT tumor segmentation.
    • Analyzed segmentation performance, uncertainty quality, and correlation with inter-modality information.
    • Introduced and evaluated a novel uncertainty-driven loss function to leverage complementary modal information.

    Main Results:

    • Gained insights into the benefits and information captured by multi-modality uncertainties.
    • Demonstrated that the proposed uncertainty-driven loss improved segmentation performance (4.53% and 2.92% Dice increase on two datasets).
    • The novel approach achieved superior segmentation accuracy while simultaneously reducing prediction uncertainty.

    Conclusions:

    • Multi-modality uncertainties offer valuable insights for PET/CT tumor segmentation.
    • Incorporating multi-modality uncertainties into segmentation networks can significantly enhance performance and reliability.
    • The developed uncertainty-driven loss function effectively utilizes complementary information between PET and CT modalities.