Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise.
Purpose: To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods: List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 2; pp. 539 - 550 |
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| Main Authors: | , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Jan2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154982382&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154982382 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Jan2022 vid: 49 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154982382 151604121 154982382 154982382 10.1007/s00259-021-05478-x 154982382 ppf: 539 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise. aug: au: Mehranian, Abolfazl Wollenweber, Scott D. Walker, Matthew D. Bradley, Kevin M. Fielding, Patrick A. Su, Kuan-Hao Johnsen, Robert Kotasidis, Fotis Jansen, Floris P. McGowan, Daniel R. affil: GE Healthcare, Big Data Institute, University of Oxford, Oxford, UK sug: subj: Oncology Radiographic Image Enhancement Fludeoxyglucose F 18 Positron-Emission Tomography Neural Networks (Computer) Utilization Noise Prevention and Control Human Algorithms Deep Learning Image Processing, Computer Assisted ab: Purpose: To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods: List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). Results: OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. Conclusion: Deep learning–based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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