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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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Related Experiment Video

Updated: May 5, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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Physically-Based Inverse Rendering Framework for PET Image Reconstruction.

Yixin Li, Soroush Shabani Sichani, Zipai Wang

    Arxiv
    |September 5, 2025
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    Summary
    This summary is machine-generated.

    We introduce a novel inverse rendering (IR) framework for Positron Emission Tomography (PET) image reconstruction. This physically-based approach enhances image quality and diagnostic accuracy, outperforming existing methods in phantom and clinical studies.

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    Area of Science:

    • Medical Imaging
    • Computer Graphics
    • Computational Physics

    Background:

    • Differentiable rendering is crucial for inverse problems in computer graphics.
    • PET image reconstruction requires accurate modeling of photon transport.
    • Existing methods may lack physical interpretability and optimization efficiency.

    Purpose of the Study:

    • To develop a physically-based inverse rendering (IR) framework for PET image reconstruction.
    • To leverage differentiable rendering principles for enhanced PET image analysis.
    • To improve signal-to-noise ratio and tissue contrast in PET imaging.

    Main Methods:

    • Integrated Monte Carlo sampling with an analytical projector for forward rendering.
    • Employed automatic differentiation to obtain voxel-wise gradients for optimization.
    • Utilized the Dr.Jit platform for efficient gradient computation.
    • Implemented the Maximum Likelihood Expectation Maximization (MLEM) algorithm.

    Main Results:

    • The IR framework achieved higher signal-to-noise ratio (SNR) and improved image quality compared to CASToR.
    • Clinical evaluation showed higher hippocampal standardized uptake value ratios (SUVR) and gray-to-white matter ratios (GWR).
    • Demonstrated enhanced tissue contrast for potential improvements in Alzheimer's disease assessment.

    Conclusions:

    • The proposed IR framework provides a physically interpretable and extensible platform for high-fidelity PET image reconstruction.
    • The method shows strong performance in both phantom and clinical brain PET data.
    • Offers potential for more accurate disease staging and localization in neurodegenerative disorders.