Published 2023 | Version v1
Journal article

A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information

  • 1. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva (Switzerland)
  • 2. Persian Gulf Nuclear Medicine Research Center, Department of Molecular Imaging and Radionuclide Therapy (MIRT), Bushehr Medical University Hospital, Faculty of Medicine, Bushehr University of Medical Sciences, Bushehr (Iran, Islamic Republic of)
  • 3. Department of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Schoepfstr. 41, Innsbruck (Austria)
  • 4. Department of Basic Neuroscience, Geneva University, Geneva (Switzerland)
  • 5. Department of Psychiatry, Geneva University, Geneva (Switzerland)
  • 6. Geneva University Neurocenter, University of Geneva, Geneva (Switzerland)
  • 7. Department of Nuclear Medicine, University of Southern Denmark, Odense (Denmark)
  • 8. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen (Netherlands)

Description

Partial volume effect (PVE) is a consequence of the limited spatial resolution of PET scanners. PVE can cause the intensity values of a particular voxel to be underestimated or overestimated due to the effect of surrounding tracer uptake. We propose a novel partial volume correction (PVC) technique to overcome the adverse effects of PVE on PET images. Two hundred and twelve clinical brain PET scans, including 50 18F-Fluorodeoxyglucose (18F-FDG), 50 18F-Flortaucipir, 36 18F-Flutemetamol, and 76 18F-FluoroDOPA, and their corresponding T1-weighted MR images were enrolled in this study. The Iterative Yang technique was used for PVC as a reference or surrogate of the ground truth for evaluation. A cycle-consistent adversarial network (CycleGAN) was trained to directly map non-PVC PET images to PVC PET images. Quantitative analysis using various metrics, including structural similarity index (SSIM), root mean squared error (RMSE), and peak signal-to-noise ratio (PSNR), was performed. Furthermore, voxel-wise and region-wise-based correlations of activity concentration between the predicted and reference images were evaluated through joint histogram and Bland and Altman analysis. In addition, radiomic analysis was performed by calculating 20 radiomic features within 83 brain regions. Finally, a voxel-wise two-sample t-test was used to compare the predicted PVC PET images with reference PVC images for each radiotracer. The Bland and Altman analysis showed the largest and smallest variance for 18F-FDG (95% CI: - 0.29, + 0.33 SUV, mean = 0.02 SUV) and 18F-Flutemetamol (95% CI: - 0.26, + 0.24 SUV, mean = - 0.01 SUV), respectively. The PSNR was lowest (29.64 ± 1.13 dB) for 18F-FDG and highest (36.01 ± 3.26 dB) for 18F-Flutemetamol. The smallest and largest SSIM were achieved for 18F-FDG (0.93 ± 0.01) and 18F-Flutemetamol (0.97 ± 0.01), respectively. The average relative error for the kurtosis radiomic feature was 3.32%, 9.39%, 4.17%, and 4.55%, while it was 4.74%, 8.80%, 7.27%, and 6.81% for NGLDM_contrast feature for 18F-Flutemetamol, 18F-FluoroDOPA, 18F-FDG, and 18F-Flortaucipir, respectively. An end-to-end CycleGAN PVC method was developed and evaluated. Our model generates PVC images from the original non-PVC PET images without requiring additional anatomical information, such as MRI or CT. Our model eliminates the need for accurate registration or segmentation or PET scanner system response characterization. In addition, no assumptions regarding anatomical structure size, homogeneity, boundary, or background level are required.

Availability note (English)

Available from: http://dx.doi.org/10.1007/s00259-023-06152-0

Additional details

Identifiers

Publishing Information

Journal Title
European Journal of Nuclear Medicine and Molecular Imaging
Journal Volume
50
Journal Issue
7
Journal Page Range
p. 1881-1896
ISSN
1619-7070
CODEN
EJNMA6

Optional Information

Notes
Oncology #En Dash# Genitourinary