3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain

Leandro José Rodríguez Hernández; Humberto de Jesús Ochoa Domínguez; Osslan Osiris Vergara Villegas; Vianey Guadalupe Cruz Sánchez; Juan Humberto Sossa Azuela; Javier Polanco González

doi:10.1007/978-3-031-07750-0_9

3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain

Leandro José Rodríguez Hernández, Humberto de Jesús Ochoa Domínguez, Osslan Osiris Vergara Villegas, Vianey Guadalupe Cruz Sánchez, Juan Humberto Sossa Azuela, Javier Polanco González

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
Editors	Osslan Osiris Vergara-Villegas, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	94-104
Number of pages	11
ISBN (Print)	9783031077494
DOIs	https://doi.org/10.1007/978-3-031-07750-0_9
State	Published - 2022
Event	14th Mexican Conference on Pattern Recognition, MCPR 2022 - Ciudad Juárez, Mexico Duration: 22 Jun 2022 → 25 Jun 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13264 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Mexican Conference on Pattern Recognition, MCPR 2022
Country/Territory	Mexico
City	Ciudad Juárez
Period	22/06/22 → 25/06/22

Keywords

Convolutional neural network
Image enhancement
Positron emission tomography
Sinogram

Access to Document

10.1007/978-3-031-07750-0_9

Cite this

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 O. O. Vergara-Villegas, V. G. Cruz-Sánchez, J. H. Sossa-Azuela, J. A. Carrasco-Ochoa, J. F. Martínez-Trinidad, & J. A. Olvera-López (Eds.), Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings (pp. 94-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13264 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-07750-0_9

Rodríguez Hernández, Leandro José ; Ochoa Domínguez, Humberto de Jesús ; Vergara Villegas, Osslan Osiris et al. / 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain. Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. editor / Osslan Osiris Vergara-Villegas ; Vianey Guadalupe Cruz-Sánchez ; Juan Humberto Sossa-Azuela ; Jesús Ariel Carrasco-Ochoa ; José Francisco Martínez-Trinidad ; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2022. pp. 94-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

keywords = "Convolutional neural network, Image enhancement, Positron emission tomography, Sinogram",

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Rodríguez Hernández, LJ, Ochoa Domínguez, HDJ, Vergara Villegas, OO, Cruz Sánchez, VG, Sossa Azuela, JH & Polanco González, J 2022, 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain. in OO Vergara-Villegas, VG Cruz-Sánchez, JH Sossa-Azuela, JA Carrasco-Ochoa, JF Martínez-Trinidad & JA Olvera-López (eds), Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13264 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 94-104, 14th Mexican Conference on Pattern Recognition, MCPR 2022, Ciudad Juárez, Mexico, 22/06/22. https://doi.org/10.1007/978-3-031-07750-0_9

3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain. / Rodríguez Hernández, Leandro José; Ochoa Domínguez, Humberto de Jesús; Vergara Villegas, Osslan Osiris et al.
Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. ed. / Osslan Osiris Vergara-Villegas; Vianey Guadalupe Cruz-Sánchez; Juan Humberto Sossa-Azuela; Jesús Ariel Carrasco-Ochoa; José Francisco Martínez-Trinidad; José Arturo Olvera-López. Springer Science and Business Media Deutschland GmbH, 2022. p. 94-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13264 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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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 -

Rodríguez Hernández LJ, Ochoa Domínguez HDJ, Vergara Villegas OO, Cruz Sánchez VG, Sossa Azuela JH, Polanco González J. 3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain. In Vergara-Villegas OO, Cruz-Sánchez VG, Sossa-Azuela JH, Carrasco-Ochoa JA, Martínez-Trinidad JF, Olvera-López JA, editors, Pattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 94-104. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-07750-0_9