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Related Concept Videos

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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Updated: Aug 4, 2025

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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List-Mode PET Image Reconstruction Using Deep Image Prior.

Kibo Ote, Fumio Hashimoto, Yuya Onishi

    IEEE Transactions on Medical Imaging
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    We developed a novel deep learning method for list-mode positron emission tomography (PET) image reconstruction. This new approach enhances image quality, offering sharper details and improved contrast-noise trade-offs for quantitative PET imaging.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence in Healthcare
    • Nuclear Medicine

    Background:

    • List-mode positron emission tomography (PET) image reconstruction is crucial for advanced PET scanners.
    • Deep learning offers potential for enhancing PET image quality but faces challenges with list-mode data.
    • Existing methods struggle to process the sequential bit-code nature of list-mode data for convolutional neural networks (CNNs).

    Purpose of the Study:

    • To introduce a novel deep learning method for list-mode PET image reconstruction.
    • To overcome the limitations of applying CNNs to list-mode PET data.
    • To improve the quality and quantitative accuracy of PET images, especially with limited event data.

    Main Methods:

    • Proposed a novel unsupervised CNN method: list-mode deep image prior reconstruction (LM-DIPRecon).
    • Integrated LM-DIPRecon with the regularized list-mode dynamic row action maximum likelihood algorithm (LM-DRAMA) using an alternating direction method of multipliers.
    • Utilized magnetic resonance imaging conditioned DIP (MR-DIP) within the iterative reconstruction process.

    Main Results:

    • LM-DIPRecon produced sharper images compared to LM-DRAMA, MR-DIP, and sinogram-based DIPRecon.
    • Achieved superior trade-off curves between image contrast and noise.
    • Demonstrated effectiveness in quantitative PET imaging with limited events while preserving raw data integrity.

    Conclusions:

    • LM-DIPRecon is a valuable tool for quantitative PET imaging, particularly when dealing with limited data.
    • The method effectively maintains accurate raw data information.
    • The fine temporal resolution of list-mode data makes LM-DIPRecon promising for 4D PET imaging and motion correction.