Design and first experimental results of an edgeless PET prototype for breast imaging: DeepBreast

[EN] BackgroundBreast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomography (PET) technology for accurate breast cancer diagnosis and staging, we propose a system design based on monolithic crystals with inherent Depth...

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Detalles Bibliográficos
Autores: Freire-López-Fando, Marta, Jiménez-Serrano, Santiago|||0000-0003-2917-6053, García-Aparisi, Francisco Brandan|||0000-0001-6569-0864, Toledo Alarcón, José Francisco|||0000-0002-9782-4510, Vidal San Sebastian, Luis Fernando|||0000-0001-8852-8560, Loignon-Houle, Francis, Alfonso Laguna, Carlos De|||0000-0002-2378-021X, Rodríguez-Álvarez, M.J.|||0000-0001-8333-8792, F Sánchez, González Martínez, Antonio Javier, Gonzalez-Montoro, Andrea, Barbera, Julio, Alamo, Jorge, Torres-Espallardo, Irene, Echegoyen-Blasco, Sara
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:dnet:riunet______::675d59bf7868d0aa3a63e8d435ef589a
Acceso en línea:https://riunet.upv.es/handle/10251/236000
Access Level:acceso abierto
Palabra clave:Breast cancer
Edgeless PET
Monolithic scintillators
Neural networks
PET
Descripción
Sumario:[EN] BackgroundBreast cancer causes the largest number of cancer-related deaths among women worldwide. With the aim of improving Positron Emission Tomography (PET) technology for accurate breast cancer diagnosis and staging, we propose a system design based on monolithic crystals with inherent Depth of Interaction (DOI) capabilities and an innovative edgeless detector ring. This approach eliminates the physical gaps between PET detectors, improving the system detection efficiency while potentially enhancing the image quality since edge effects are reduced. We have developed a dedicated breast PET system prototype (DeepBreast) to show the feasibility of this design. The system is composed of 14 curved LYSO monolithic scintillators of 12.5 mm thickness glued side-by-side with a high-refractive index compound. The useful transaxial and axial Field of View (FOV) of the system are 160 mm and 50 mm, respectively. A Neural Network technique was used for the x- and y- photon impact position estimation. The impact DOI and energy values were determined using the Voronoi calibration methodology. An initial experimental evaluation of the DeepBreast system has been performed inspired by the NEMA protocols for whole-body and small-animals PET scanners.ResultsA nearly flat spatial resolution as a function of radial position was obtained, which indicates the DOI capability of the system to mitigate parallax errors. An average spatial resolution of 1.9 +/- 0.1 mm, 1.9 +/- 0.1 mm and 1.7 +/- 0.1 mm FWHM was achieved at the center of the axial FOV for the radial, tangential, and axial directions, respectively. A maximum sensitivity value of 2% was measured at the center of the FOV. The noise equivalent count rate peak reached 15 kcps at 13.4 MBq. Moreover, percent contrast values of 27.9%, 28.8%, 56.8%, 72.5%, 87.2% and 84.2% were achieved for 4.5 mm, 6 mm, 9 mm, 12 mm, 15 mm and 20 mm cylinders of a larger dedicated IQ phantom, respectively.ConclusionsThe initial experimental results demonstrate the feasibility of the DeepBreast as an innovative PET scanner for breast cancer imaging.