TY - GEN
T1 - 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain
AU - Rodríguez Hernández, Leandro José
AU - Ochoa Domínguez, Humberto de Jesús
AU - Vergara Villegas, Osslan Osiris
AU - Cruz Sánchez, Vianey Guadalupe
AU - Sossa Azuela, Juan Humberto
AU - Polanco González, Javier
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this work, we propose a three dimensional (3D) convolutional neural network (CNN) to enhance sinograms acquired from a small-animal positron emission tomography (PET) scanner. The network consists of three convolutional layers created with 3D filters of sizes 9, 3, and 5, respectively. We extracted 15250 3D patches from low- and high-count sinograms to build the low- and high-resolution pairs for training. After training and prediction, the enhanced sinogram is reconstructed using the ordered subset expectation maximization (OSEM) algorithm. The results revealed that the proposed network improved the spillover ratio and the uniformity of the standard NU4-2008 phantom up to 8% and 75%, respectively.
AB - In this work, we propose a three dimensional (3D) convolutional neural network (CNN) to enhance sinograms acquired from a small-animal positron emission tomography (PET) scanner. The network consists of three convolutional layers created with 3D filters of sizes 9, 3, and 5, respectively. We extracted 15250 3D patches from low- and high-count sinograms to build the low- and high-resolution pairs for training. After training and prediction, the enhanced sinogram is reconstructed using the ordered subset expectation maximization (OSEM) algorithm. The results revealed that the proposed network improved the spillover ratio and the uniformity of the standard NU4-2008 phantom up to 8% and 75%, respectively.
KW - Convolutional neural network
KW - Image enhancement
KW - Positron emission tomography
KW - Sinogram
UR - http://www.scopus.com/inward/record.url?scp=85132970185&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-07750-0_9
DO - 10.1007/978-3-031-07750-0_9
M3 - Contribución a la conferencia
AN - SCOPUS:85132970185
SN - 9783031077494
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 94
EP - 104
BT - Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
A2 - Vergara-Villegas, Osslan Osiris
A2 - Cruz-Sánchez, Vianey Guadalupe
A2 - Sossa-Azuela, Juan Humberto
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Olvera-López, José Arturo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th Mexican Conference on Pattern Recognition, MCPR 2022
Y2 - 22 June 2022 through 25 June 2022
ER -