PET Rapid Image Reconstruction Challenge (PETRIC)
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910077360799744 |
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| author | da Costa-Luis, Casper Ehrhardt, Matthias J. Kolbitsch, Christoph Ovtchinnikov, Evgueni Pasca, Edoardo Thielemans, Kris Tsoumpas, Charalampos |
| author_facet | da Costa-Luis, Casper Ehrhardt, Matthias J. Kolbitsch, Christoph Ovtchinnikov, Evgueni Pasca, Edoardo Thielemans, Kris Tsoumpas, Charalampos |
| contents | Introduction: We describe the foundation of PETRIC, an image reconstruction challenge to minimise the computational runtime of related algorithms for Positron Emission Tomography (PET).
Purpose: Although several similar challenges are well-established in the field of medical imaging, there have been no prior challenges for PET image reconstruction.
Methods: Participants are provided with open-source software for implementation of their reconstruction algorithm(s). We define the objective function and reconstruct "gold standard" reference images, and provide metrics for quantifying algorithmic performance. We also received and curated phantom datasets (acquired with different scanners, radionuclides, and phantom types), which we further split into training and evaluation datasets. The automated computational framework of the challenge is released as open-source software.
Results: Four teams with nine algorithms in total participated in the challenge. Their contributions made use of various tools from optimisation theory including preconditioning, stochastic gradients, and artificial intelligence. While most of the submitted approaches appear very similar in nature, their specific implementation lead to a range of algorithmic performance.
Conclusion: As the first challenge for PET image reconstruction, PETRIC's solid foundations allow researchers to reuse its framework for evaluating new and existing image reconstruction methods on new or existing datasets. Variant versions of the challenge have and will continue to be launched in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22566 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PET Rapid Image Reconstruction Challenge (PETRIC) da Costa-Luis, Casper Ehrhardt, Matthias J. Kolbitsch, Christoph Ovtchinnikov, Evgueni Pasca, Edoardo Thielemans, Kris Tsoumpas, Charalampos Medical Physics Performance Introduction: We describe the foundation of PETRIC, an image reconstruction challenge to minimise the computational runtime of related algorithms for Positron Emission Tomography (PET). Purpose: Although several similar challenges are well-established in the field of medical imaging, there have been no prior challenges for PET image reconstruction. Methods: Participants are provided with open-source software for implementation of their reconstruction algorithm(s). We define the objective function and reconstruct "gold standard" reference images, and provide metrics for quantifying algorithmic performance. We also received and curated phantom datasets (acquired with different scanners, radionuclides, and phantom types), which we further split into training and evaluation datasets. The automated computational framework of the challenge is released as open-source software. Results: Four teams with nine algorithms in total participated in the challenge. Their contributions made use of various tools from optimisation theory including preconditioning, stochastic gradients, and artificial intelligence. While most of the submitted approaches appear very similar in nature, their specific implementation lead to a range of algorithmic performance. Conclusion: As the first challenge for PET image reconstruction, PETRIC's solid foundations allow researchers to reuse its framework for evaluating new and existing image reconstruction methods on new or existing datasets. Variant versions of the challenge have and will continue to be launched in the future. |
| title | PET Rapid Image Reconstruction Challenge (PETRIC) |
| topic | Medical Physics Performance |
| url | https://arxiv.org/abs/2511.22566 |