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Volume 12
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10.3390/diagnostics12030596
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Article

3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans

by
Bart M. de Vries
1,*,
Sandeep S. V. Golla
1,
Gerben J. C. Zwezerijnen
1,
Otto S. Hoekstra
1,
Yvonne W. S. Jauw
1,2,
Marc C. Huisman
1,
Guus A. M. S. van Dongen
1,
Willemien C. Menke-van der Houven van Oordt
3,
Josée J. M. Zijlstra-Baalbergen
1,2,
Liesbet Mesotten
4,5,
Ronald Boellaard
1 and
Maqsood Yaqub
1
1
Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
2
Cancer Center Amsterdam, Department of Hematology, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
3
Cancer Center Amsterdam, Department of Medical Oncology, Vrije Universiteit Amsterdam, Amsterdam UMC, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
4
Faculty of Medicine and Life Sciences, Hasselt University, Agoralaan Building D, B-3590 Diepenbeek, Belgium
5
Department of Nuclear Medicine, Ziekenhuis Oost Limburg, Schiepse Bos 6, B-3600 Genk, Belgium
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(3), 596; https://doi.org/10.3390/diagnostics12030596
Submission received: 20 January 2022 / Revised: 22 February 2022 / Accepted: 24 February 2022 / Published: 25 February 2022
(This article belongs to the Topic Artificial Intelligence in Cancer Diagnosis and Therapy)
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Abstract

Acquisition time and injected activity of 18F-fluorodeoxyglucose (18F-FDG) PET should ideally be reduced. However, this decreases the signal-to-noise ratio (SNR), which impairs the diagnostic value of these PET scans. In addition, 89Zr-antibody PET is known to have a low SNR. To improve the diagnostic value of these scans, a Convolutional Neural Network (CNN) denoising method is proposed. The aim of this study was therefore to develop CNNs to increase SNR for low-count 18F-FDG and 89Zr-antibody PET. Super-low-count, low-count and full-count 18F-FDG PET scans from 60 primary lung cancer patients and full-count 89Zr-rituximab PET scans from five patients with non-Hodgkin lymphoma were acquired. CNNs were built to capture the features and to denoise the PET scans. Additionally, Gaussian smoothing (GS) and Bilateral filtering (BF) were evaluated. The performance of the denoising approaches was assessed based on the tumour recovery coefficient (TRC), coefficient of variance (COV; level of noise), and a qualitative assessment by two nuclear medicine physicians. The CNNs had a higher TRC and comparable or lower COV to GS and BF and was also the preferred method of the two observers for both 18F-FDG and 89Zr-rituximab PET. The CNNs improved the SNR of low-count 18F-FDG and 89Zr-rituximab PET, with almost similar or better clinical performance than the full-count PET, respectively. Additionally, the CNNs showed better performance than GS and BF.
Keywords: low-count; CNN; denoising; 18F-FDG; 89Zr-antibody low-count; CNN; denoising; 18F-FDG; 89Zr-antibody

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MDPI and ACS Style

de Vries, B.M.; Golla, S.S.V.; Zwezerijnen, G.J.C.; Hoekstra, O.S.; Jauw, Y.W.S.; Huisman, M.C.; van Dongen, G.A.M.S.; Menke-van der Houven van Oordt, W.C.; Zijlstra-Baalbergen, J.J.M.; Mesotten, L.; et al. 3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans. Diagnostics 2022, 12, 596. https://doi.org/10.3390/diagnostics12030596

AMA Style

de Vries BM, Golla SSV, Zwezerijnen GJC, Hoekstra OS, Jauw YWS, Huisman MC, van Dongen GAMS, Menke-van der Houven van Oordt WC, Zijlstra-Baalbergen JJM, Mesotten L, et al. 3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans. Diagnostics. 2022; 12(3):596. https://doi.org/10.3390/diagnostics12030596

Chicago/Turabian Style

de Vries, Bart M., Sandeep S. V. Golla, Gerben J. C. Zwezerijnen, Otto S. Hoekstra, Yvonne W. S. Jauw, Marc C. Huisman, Guus A. M. S. van Dongen, Willemien C. Menke-van der Houven van Oordt, Josée J. M. Zijlstra-Baalbergen, Liesbet Mesotten, and et al. 2022. "3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans" Diagnostics 12, no. 3: 596. https://doi.org/10.3390/diagnostics12030596

APA Style

de Vries, B. M., Golla, S. S. V., Zwezerijnen, G. J. C., Hoekstra, O. S., Jauw, Y. W. S., Huisman, M. C., van Dongen, G. A. M. S., Menke-van der Houven van Oordt, W. C., Zijlstra-Baalbergen, J. J. M., Mesotten, L., Boellaard, R., & Yaqub, M. (2022). 3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans. Diagnostics, 12(3), 596. https://doi.org/10.3390/diagnostics12030596

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