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000276157 1001_ $$00000-0002-9536-6691$$aLopes, Leonor$$b0
000276157 245__ $$aDopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.
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000276157 520__ $$aDopamine transporter imaging is routinely used in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) diagnosis. While [11C]CFT PET is prevalent in Asia with a large APS database, Europe relies on [123I]FP-CIT SPECT with limited APS data. Our aim was to develop a deep learning-based method to convert [11C]CFT PET images to [123I]FP-CIT SPECT images, facilitating multicenter studies and overcoming data scarcity to promote Artificial Intelligence (AI) advancements.A CycleGAN was trained on [11C]CFT PET (n = 602, 72%PD) and [123I]FP-CIT SPECT (n = 1152, 85%PD) images from PD and non-parkinsonian control (NC) subjects. The model generated synthetic SPECT images from a real PET test set (n = 67, 75%PD). Synthetic images were quantitatively and visually evaluated.Fréchet Inception Distance indicated higher similarity between synthetic and real SPECT than between synthetic SPECT and real PET. A deep learning classification model trained on synthetic SPECT achieved sensitivity of 97.2% and specificity of 90.0% on real SPECT images. Striatal specific binding ratios of synthetic SPECT were not significantly different from real SPECT. The striatal left-right differences and putamen binding ratio were significantly different only in the PD cohort. Real PET and real SPECT had higher contrast-to-noise ratio compared to synthetic SPECT. Visual grading analysis scores showed no significant differences between real and synthetic SPECT, although reduced diagnostic performance on synthetic images was observed.CycleGAN generated synthetic SPECT images visually indistinguishable from real ones and retained disease-specific information, demonstrating the feasibility of translating [11C]CFT PET to [123I]FP-CIT SPECT. This cross-modality synthesis could enhance further AI classification accuracy, supporting the diagnosis of PD and APS.
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000276157 650_7 $$2Other$$aCFT PET
000276157 650_7 $$2Other$$aCycle GAN
000276157 650_7 $$2Other$$aDomain adaptation
000276157 650_7 $$2Other$$aFP-CIT SPECT
000276157 650_7 $$2Other$$aParkinson’s disease
000276157 650_7 $$0VTD58H1Z2X$$2NLM Chemicals$$aDopamine
000276157 650_7 $$2NLM Chemicals$$aDopamine Plasma Membrane Transport Proteins
000276157 650_7 $$0155797-99-2$$2NLM Chemicals$$a2-carbomethoxy-8-(3-fluoropropyl)-3-(4-iodophenyl)tropane
000276157 650_7 $$2NLM Chemicals$$aTropanes
000276157 650_2 $$2MeSH$$aHumans
000276157 650_2 $$2MeSH$$aTomography, Emission-Computed, Single-Photon: methods
000276157 650_2 $$2MeSH$$aPositron-Emission Tomography: methods
000276157 650_2 $$2MeSH$$aParkinson Disease: diagnostic imaging
000276157 650_2 $$2MeSH$$aParkinson Disease: metabolism
000276157 650_2 $$2MeSH$$aImage Processing, Computer-Assisted: methods
000276157 650_2 $$2MeSH$$aMale
000276157 650_2 $$2MeSH$$aFemale
000276157 650_2 $$2MeSH$$aDopamine: metabolism
000276157 650_2 $$2MeSH$$aDeep Learning
000276157 650_2 $$2MeSH$$aMiddle Aged
000276157 650_2 $$2MeSH$$aDopamine Plasma Membrane Transport Proteins: metabolism
000276157 650_2 $$2MeSH$$aAged
000276157 650_2 $$2MeSH$$aTropanes
000276157 7001_ $$aJiao, Fangyang$$b1
000276157 7001_ $$aXue, Song$$b2
000276157 7001_ $$aPyka, Thomas$$b3
000276157 7001_ $$aKrieger, Korbinian$$b4
000276157 7001_ $$aGe, Jingjie$$b5
000276157 7001_ $$aXu, Qian$$b6
000276157 7001_ $$aFahmi, Rachid$$b7
000276157 7001_ $$aSpottiswoode, Bruce$$b8
000276157 7001_ $$aSoliman, Ahmed$$b9
000276157 7001_ $$aBuchert, Ralph$$b10
000276157 7001_ $$0P:(DE-2719)9001539$$aBrendel, Matthias$$b11$$udzne
000276157 7001_ $$aHong, Jimin$$b12
000276157 7001_ $$aGuan, Yihui$$b13
000276157 7001_ $$aBassetti, Claudio L A$$b14
000276157 7001_ $$aRominger, Axel$$b15
000276157 7001_ $$aZuo, Chuantao$$b16
000276157 7001_ $$aShi, Kuangyu$$b17
000276157 7001_ $$aWu, Ping$$b18
000276157 773__ $$0PERI:(DE-600)2098375-X$$a10.1007/s00259-024-06961-x$$gVol. 52, no. 3, p. 851 - 863$$n3$$p851 - 863$$tEuropean journal of nuclear medicine and molecular imaging$$v52$$x1619-7070$$y2025
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