Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach

Lopes, Leonor ; Jiao, Fangyang; Xue, Song; Pyka, Thomas; Krieger, Korbinian; Ge, Jingjie; Xu, Qian; Fahmi, Rachid; Spottiswoode, Bruce; Soliman, Ahmed; Buchert, Ralph; Brendel, Matthias ; Hong, Jimin; Guan, Yihui; Bassetti, Claudio L. A.; Rominger, Axel; Zuo, Chuantao; Shi, Kuangyu und Wu, Ping (2024): Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach. In: European Journal of Nuclear Medicine and Molecular Imaging [Forthcoming]

Abstract

Purpose : Dopamine transporter imaging is routinely used in Parkinson’s disease (PD) and atypical parkinsonian syndromes (APS) diagnosis. While [11C]CFT PET is prevalent in Asia with a large APS database, Europe relies on [123I]FP-CIT SPECT with limited APS data. Our aim was to develop a deep learning-based method to convert [11C]CFT PET images to [123I]FP-CIT SPECT images, facilitating multicenter studies and overcoming data scarcity to promote Artificial Intelligence (AI) advancements. Methods : A CycleGAN was trained on [11C]CFT PET (n = 602, 72%PD) and [123I]FP-CIT SPECT (n = 1152, 85%PD) images from PD and non-parkinsonian control (NC) subjects. The model generated synthetic SPECT images from a real PET test set (n = 67, 75%PD). Synthetic images were quantitatively and visually evaluated. Results : Fréchet Inception Distance indicated higher similarity between synthetic and real SPECT than between synthetic SPECT and real PET. A deep learning classification model trained on synthetic SPECT achieved sensitivity of 97.2% and specificity of 90.0% on real SPECT images. Striatal specific binding ratios of synthetic SPECT were not significantly different from real SPECT. The striatal left-right differences and putamen binding ratio were significantly different only in the PD cohort. Real PET and real SPECT had higher contrast-to-noise ratio compared to synthetic SPECT. Visual grading analysis scores showed no significant differences between real and synthetic SPECT, although reduced diagnostic performance on synthetic images was observed. Conclusion : CycleGAN generated synthetic SPECT images visually indistinguishable from real ones and retained disease-specific information, demonstrating the feasibility of translating [11C]CFT PET to [123I]FP-CIT SPECT. This cross-modality synthesis could enhance further AI classification accuracy, supporting the diagnosis of PD and APS.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Munich Cluster for Systems Neurology (SyNergy) ; Medizin > Institut für Schlaganfall- und Demenzforschung (ISD) ; Medizin > Klinikum der LMU München > Klinik und Poliklinik für Nuklearmedizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1619-7070
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198