Deep Learning Based Estimation of Synthetic Tau PET from Amyloid PET

Abstract

AbstractBackgroundAlzheimer’s disease (AD) is the most common pathological substrate for dementia. Both tau and amyloid positron emission tomography (PET) scans are valuable for evaluating innovative treatments and studying AD progression. However, tau‐PET is extremely expensive and not universally available. Estimating tau‐PET images from amyloid‐PET would mitigate these concerns. In this study, we proposed a novel application of the Conditional Generative Adversarial Network (cGAN) framework to generate high quality tau‐PET (18F‐flortaucipir) scans from the corresponding amyloid‐PET (18F‐florbetapir) scans.MethodA dataset of 475 individuals who underwent both 18F‐florbetapir and 18F‐flortaucipir scans with different clinical diagnosis (Table 1) were used in our study from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) online depository. Our cGAN model was trained for 50 epochs using real, corresponding amyloid‐tau PET pairs. The resulting model takes real amyloid‐PET images as input and outputs synthetic tau‐PET images (Figure 1.a). The similarity between synthetic and real tau‐PET images was evaluated via the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and normalized root mean squared error (NRMSE). Furthermore, we extracted regional standard uptake value ratios (SUVRs) and meta‐region of interest (ROI) SUVRs from the real tau‐PET scans and synthetic tau‐PET scans. The utility of the synthetic tau‐PET meta‐ROI SUVR for identifying real tau‐PET scans that are tau positive was evaluated in terms of true positive rate (TPR), false positive rate (FPR), and the area under the receiver operating characteristic (ROC) curve (AUC).ResultOur cGAN model yielded a SSIM of 0.917, a PSNR of 27.04, and a NRSME of 0.18 on the test set. The ROC curve analysis (Figure 2) resulted in an AUC of 0.84%. By applying a cut‐off threshold corresponding to the model optimal operating point, a TPR of 90% and FPR of 28% were achieved. The SUVR analysis yielded a mean absolute percentage error of 8.3% ± 6.5% on the test dataset.ConclusionThe proposed cGAN model generated high quality 18F‐flortaucipir PET images from 18F‐florbetapir PET images (Figure 1.b). This could be highly useful in a screening paradigm to use amyloid‐PET to identify those individuals most likely to be tau positive.