Details

Autor(en) / Beteiligte

• 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.

Titel

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise

Ist Teil von

• European journal of nuclear medicine and molecular imaging, 2022-01, Vol.49 (2), p.539-549

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• Purpose To enhance the image quality of oncology [ 18 F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods List-mode data from 277 [ 18 F]-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 SUV max , 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.