Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/37234
Title: | 3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body 18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans | Authors: | de Vries, Bart M. Golla, Sandeep S., V Zwezerijnen, Gerben J. C. Hoekstra, Otto S. Jauw, Yvonne W. S. Huisman, Marc C. van Dongen, Guus A. M. S. Van Oordt, Willemien C. Menke-van der Houven Zijlstra-Baalbergen, Josee J. M. MESOTTEN, Liesbet Boellaard, Ronald Yaqub, Maqsood |
Issue Date: | 2022 | Publisher: | MDPI | Source: | DIAGNOSTICS, 12 (3) , (Art N° 596) | Abstract: | Acquisition time and injected activity of F-18-fluorodeoxyglucose (F-18-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, Zr-89-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 F-18-FDG and Zr-89-antibody PET. Super-low-count, low-count and full-count F-18-FDG PET scans from 60 primary lung cancer patients and full-count Zr-89-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 F-18-FDG and Zr-89-rituximab PET. The CNNs improved the SNR of low-count F-18-FDG and Zr-89-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. | Notes: | de Vries, BM (corresponding author), Vrije Univ Amsterdam, Canc Ctr Amsterdam, Dept Radiol & Nucl Med, Amsterdam UMC, De Boelelaan 1117, NL-1081 HV Amsterdam, Netherlands. b.devries1@amsterdamumc.nl; s.golla@amsterdamumc.nl; g.zwezerijnen@amsterdamumc.nl; os.hoekstra@amsterdamumc.nl; yws.jauw@amsterdamumc.nl; m.huisman@amsterdamumc.nl; gams.vandongen@amsterdamumc.nl; c.menke@amsterdamumc.nl; j.zijlstra@amsterdamumc.nl; liesbet.mesotten@zol.be; r.boellaard@amsterdamumc.nl; maqsood.yaqub@amsterdamumc.nl |
Keywords: | low-count; CNN; denoising; F-18-FDG; Zr-89-antibody | Document URI: | http://hdl.handle.net/1942/37234 | e-ISSN: | 2075-4418 | DOI: | 10.3390/diagnostics12030596 | ISI #: | WOS:000775694100001 | Rights: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
diagnostics-12-00596-v3.pdf | Published version | 14.48 MB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
1
checked on Oct 14, 2024
Page view(s)
20
checked on Jun 14, 2022
Download(s)
4
checked on Jun 14, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.