Alzheimer's Disease (AD) is the most common form of dementia, characterised
by cognitive decline and biomarkers such as tau-proteins. Tau-positron emission
tomography (tau-PET), which employs a radiotracer to selectively bind, detect,
and visualise tau protein aggregates within the brain, is valuable for early AD
diagnosis but is less accessible due to high costs, limited availability, and
its invasive nature. Image synthesis with neural networks enables the
generation of tau-PET images from more accessible T1-weighted magnetic
resonance imaging (MRI) images. To ensure high-quality image synthesis, we
propose a cyclic 2.5D perceptual loss combined with mean squared error and
structural similarity index measure (SSIM) losses. The cyclic 2.5D perceptual
loss sequentially calculates the axial 2D average perceptual loss for a
specified number of epochs, followed by the coronal and sagittal planes for the
same number of epochs. This sequence is cyclically performed, with intervals
reducing as the cycles repeat. We conduct supervised synthesis of tau-PET
images from T1w MRI images using 516 paired T1w MRI and tau-PET 3D images from
the ADNI database. For the collected data, we perform preprocessing, including
intensity standardisation for tau-PET images from each manufacturer. The
proposed loss, applied to generative 3D U-Net and its variants, outperformed
those with 2.5D and 3D perceptual losses in SSIM and peak signal-to-noise ratio
(PSNR). In addition, including the cyclic 2.5D perceptual loss to the original
losses of GAN-based image synthesis models such as CycleGAN and Pix2Pix
improves SSIM and PSNR by at least 2% and 3%. Furthermore, by-manufacturer PET
standardisation helps the models in synthesising high-quality images than
min-max PET normalisation.

阿尔茨海默病（AD）是最常见的痴呆症，其特征是认知能力下降和 tau 蛋白等生物标志物。使用神经网络进行图像合成，可以从易获取的 T1 加权磁共振成像（MRI）图像生成 tau-PET 图像。通过使用 2.5D 感知损失和均方差与结构相似性指数度量的损失结合，能够保证高质量的图像合成。