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Denoising approaches by SubtlePET™ artificial intelligence in positron emission tomography (PET) for clinical routine application

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Abstract

Positron emission tomography (PET) plays an important role in the diagnosis and surveillance of neoplastic diseases. PET images may show higher noise levels than other imaging modalities, especially in a dose- or time-saving approach. Artificial Intelligence techniques can improve the signal-to-noise ratio in PET image reconstruction. Deep learning approaches have made significant advances in comprehensive data retrieval and de-noising. Artificial Intelligence de-noising in PET is a very promising approach that could allow shorter scan times or lower radiopharmaceutical dose administration. We reviewed studies about the de-noising AI-driven PET images, i.e., by SubtlePET™ AI tool, according to the following items: (1) retrieval of complete PET data acquired with reduced scan time; (2) reconstruction of PET images with low-count statistics by reducing radiopharmaceutical doses; (3) impact of artificial intelligence-based de-noising on PET radiomics. We evaluated their implementability in PET image reconstruction to increase the signal-to-noise ratio and image definition. This approach seems promising to positively impact patient healthcare—especially in pediatric patients—and overall diagnostic procedures reducing the cost of radiopharmaceuticals and increasing productivity and efficiency.

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Adapted from Jaudet et al. [9]

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Adapted from Yang and Peng [6]

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Funding

The authors report no involvement in the research by the sponsor that could have influenced the outcome of this work.

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Author notes

  1. Marco De Summa and Maria Rosaria Ruggiero have contributed equally to this work.

Authors and Affiliations

  1. Unità di Medicina Nucleare, TracerGLab GSTeP Radiopharmacy, Dipartimento di Diagnostica Per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy

    Marco De Summa, Maria Rosaria Ruggiero, Sandro Spinosa, Giulio Iachetti, Salvatore Annunziata & Daniele Antonio Pizzuto

  2. Medipass S.P.a., 40138, Bologna, Italy

    Marco De Summa, Maria Rosaria Ruggiero, Sandro Spinosa & Giulio Iachetti

  3. UOC Clinica Pediatrica, Dipartimento di Medicina e Chirurgia, Azienda Ospedaliero-Universitaria di Parma, 43126, Parma, Italy

    Susanna Esposito

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  1. Marco De Summa
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Contributions

Marco De Summa and Maria Rosaria Ruggiero have given substantial contributions to the conception or the design of the manuscript and contributed equally. Sandro Spinosa, Giulio Iachetti, Susanna Esposito gave their contribution to acquisition, analysis and interpretation of the data. All authors have participated to drafting the manuscript. Daniele Antonio Pizzuto revised it critically. All authors read and approved the final version of the manuscript.

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Correspondence to Daniele Antonio Pizzuto.

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De Summa, M., Ruggiero, M.R., Spinosa, S. et al. Denoising approaches by SubtlePET™ artificial intelligence in positron emission tomography (PET) for clinical routine application. Clin Transl Imaging (2024). https://doi.org/10.1007/s40336-024-00625-4

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