Abstract
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.
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Acknowledgment
L. J. Rodríguez thanks the UACJ for the support provided and the CONACYT for the scholarship granted to pursue his doctoral studies.
H. Sossa thanks CONACYT and IPN for the economical support under funds: FORDECYT-PRONACES 6005 and SIP 20220226.
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Rodríguez Hernández, L.J., Ochoa Domínguez, H.d.J., Vergara Villegas, O.O., Cruz Sánchez, V.G., Sossa Azuela, J.H., Polanco González, J. (2022). 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_9
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DOI: https://doi.org/10.1007/978-3-031-07750-0_9
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