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    A new method, Wasserstein distance-based Locally Linear Embedding (W-LLE), estimates depth of interaction (DOI) in PET detectors using only light distribution. This data-driven approach improves DOI estimation accuracy, crucial for advanced detector designs.

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    Area of Science:

    • Medical Physics
    • Nuclear Instrumentation
    • Machine Learning

    Background:

    • Monolithic crystal Positron Emission Tomography (PET) detectors utilize depth of interaction (DOI) encoding for improved performance.
    • Neural networks (NNs) are effective for estimating DOI from light distribution (LD), but require paired LD-DOI datasets.
    • Acquiring LD-DOI paired data is difficult with perpendicular irradiation only.

    Purpose of the Study:

    • To introduce a novel data-driven method, Wasserstein distance-based Locally Linear Embedding (W-LLE), for estimating DOI from LD.
    • To enable DOI estimation without requiring paired LD-DOI datasets, addressing a key experimental challenge.
    • To evaluate the effectiveness of W-LLE for row-column sum signal readout in PET detectors.

    Main Methods:

    • Developed and applied the Wasserstein distance-based Locally Linear Embedding (W-LLE) method.
    • Utilized simulated and experimental data for W-LLE evaluation.
    • Compared W-LLE performance against a model-based method and original Locally Linear Embedding (LLE).
    • Assessed DOI resolution indirectly using NNs trained with DOI estimates from W-LLE and the model-based method.

    Main Results:

    • W-LLE demonstrated superior performance compared to the model-based method in simulations.
    • W-LLE achieved DOI accuracy comparable to LLE in individual readout setups.
    • NNs trained with W-LLE-derived DOI estimates outperformed those trained with model-based estimates.
    • Achieved a DOI resolution of up to 1.8 mm Full Width at Half Maximum (FWHM) in experimental configurations.

    Conclusions:

    • W-LLE is an effective data-driven method for estimating DOI from light distribution in monolithic crystal PET detectors.
    • The method overcomes the challenge of acquiring paired LD-DOI datasets.
    • W-LLE shows significant potential for improving DOI resolution and overall PET detector performance.