Deep Learning methodologies for direct image reconstruction and integrated attenuation correction in brain PET/MRI

  1. Díaz Serrano, P. 1
  2. Ortuño Fisac, J. E. 2
  3. López Santiago, J 1
  4. Panetsos Petrova, F. 3
  5. Kontaxakis, G. 4
  1. 1 Universidad Carlos III de Madrid
    info

    Universidad Carlos III de Madrid

    Madrid, España

    ROR https://ror.org/03ths8210

  2. 2 Instituto de Salud Carlos III, Madrid
  3. 3 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  4. 4 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

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Libro:
CASEIB 2023. Libro de Actas del XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Contribuyendo a la salud basada en valor
  1. Joaquín Roca González (coord.)
  2. Dolores Ojados González (coord.)
  3. Juan Suardíaz Muro (coord.)

Editorial: Universidad Politécnica de Cartagena

ISBN: 978-84-17853-76-1

Año de publicación: 2023

Páginas: 31-34

Congreso: Congreso Anual de la Sociedad Española de Ingeniería Biomédica. CASEIB (41. 2023. Cartagena)

Tipo: Aportación congreso

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Resumen

This study proposes the ATTDeepPET model, a novel deep learning architecture crafted specifically for advancing positron emission tomography (PET) image reconstruction in PET/MR scanners. By incorporating magnetic resonance (MR) images into its learning process, ATTDeepPET addresses the persistent challenges associated with attenuation effects in PET/MR scanners, eliminating the need for simulated transmission scans. ATTDeepPET's performance is assessed alongside the deep learning model DeepPET, as well as established methods such as FBP, ML-EM, and ML-EMR for comparison. The findings reveal noteworthy achievements since ATTDeepPET accomplishes competitive image quality compared to FBP, MLEM, and ML-EMR when applied to brain phantoms while also demonstrating a reduction in reconstruction times. Nevertheless, when dealing with real PET images, ATTDeepPET does exhibit some performance variability, underscoring the increased complexity of real-set scenarios and the importance of employing ...

Fuente de los datos: Dialnet