Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization

Purpose: Dopamine transporter imaging is crucial for assessing presynaptic dopaminergic neurons in Parkinson's disease (PD) and related parkinsonian disorders. While ¹⁸F-FP-CIT PET offers advantages in spatial resolution and sensitivity over ¹²³I-β-CIT or ¹²³I-FP-CIT SPECT imaging, accurate quantification remains essential. This study presents a novel automatic quantification method for ¹⁸F-FP-CIT PET images, utilizing an artificial intelligence (AI)-based robust PET spatial normalization (SN) technology that eliminates the need for anatomical images.

Methods: The proposed SN engine consists of convolutional neural networks, trained using 213 paired datasets of ¹⁸F-FP-CIT PET and 3D structural MRI. Remarkably, only PET images are required as input during inference. A cyclic training strategy enables backward deformation from template to individual space. An additional 89 paired ¹⁸F-FP-CIT PET and 3D MRI datasets were used to evaluate the accuracy of striatal activity quantification. MRI-based PET quantification using FIRST software was also conducted for comparison. The proposed method was also validated using 135 external datasets.

Results: The proposed AI-based method successfully generated spatially normalized ¹⁸F-FP-CIT PET images, obviating the need for CT or MRI. The striatal PET activity determined by proposed PET-only method and MRI-based PET quantification using FIRST algorithm were highly correlated, with R ² and slope ranging 0.96-0.99 and 0.98-1.02 in both internal and external datasets.

Conclusion: Our AI-based SN method enables accurate automatic quantification of striatal activity in ¹⁸F-FP-CIT brain PET images without MRI support. This approach holds promise for evaluating presynaptic dopaminergic function in PD and related parkinsonian disorders.