Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:2157-2160. doi: 10.1109/EMBC48229.2022.9871269.
Recently, tau positron-emission tomography (PET) images have been widely used for the diagnosis of Alzheimer's disease (AD). However, existing semi-quantitative uptake value ratios (SUVR) calculation is usually based on group analysis or specific brain regions from existing templates, which cannot detect individual heterogeneity. In this study, we proposed a novel deep learning model; called generative adversarial networks constrained multiple loss autoencoder for tau (GANCMLAE4TAU), to extract individual regions of interest (ROIs) of tau deposition.
The basic framework of the proposed model is composed of two encoders, one decoder, and one discriminator. Tau PET images of 327 cognitive normal (CN) subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to train the model and 29 CNs from Huashan Hospital were used as an external validation group. The other 57 AD patients and 83 CNs subjects from ADNI were used in the classification task. The Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) were applied to validate the robustness of our model. In addition, we conducted a receiver operating characteristic curve (ROC) analysis for the SUVR of individual ROIs from the GANCMLAE4TAU model and compared it with SUVR of the whole brain and ROIs from the templates.
Our model achieved good SSIM (0.963±0.006), PSNR (35.960±3.458) and MSE (0.0004±0.0003). In ROC analysis, our model had the highest area under curve (AUC) (0.869, 0.809-0.929) in discriminating AD from CN subjects.
GANCMLAE4TAU could detect individual ROIs for tau PET images and had the potential to be developed as a novel diagnostic tool in the future. Clinical Relevance- This method can find individual ROIs of tau depositions, so as to achieve more accurate diagnosis of Alzheimer's disease.
最近，tau 正电子发射断层扫描（PET）图像已被广泛用于阿尔茨海默病（AD）的诊断。然而，现有的半定量摄取值比（SUVR）计算通常基于群体分析或现有模板中的特定脑区，无法检测个体异质性。在这项研究中，我们提出了一种新的深度学习模型；称为 tau 生成对抗网络约束多损失自编码器（GANCMLAE4TAU），用于提取 tau 沉积的个体感兴趣区（ROI）。
所提出模型的基本框架由两个编码器、一个解码器和一个鉴别器组成。使用来自阿尔茨海默病神经影像学倡议（ADNI）的 327 名认知正常（CN）受试者的 tau PET 图像来训练模型，来自华山医院的 29 名 CN 受试者作为外部验证组。另外 57 名 AD 患者和 83 名 ADNI 中的 CN 受试者用于分类任务。采用结构相似性（SSIM）、峰值信噪比（PSNR）和均方误差（MSE）来验证我们模型的稳健性。此外，我们对 GANCMLAE4TAU 模型的个体 ROI 的 SUVR 进行了接收者操作特征曲线（ROC）分析，并将其与全脑和模板 ROI 的 SUVR 进行了比较。
我们的模型实现了良好的 SSIM（0.963±0.006）、PSNR（35.960±3.458）和 MSE（0.0004±0.0003）。在 ROC 分析中，我们的模型在区分 AD 与 CN 受试者方面具有最高的曲线下面积（AUC）（0.869、0.809-0.929）。
GANCMLAE4TAU 可以检测 tau PET 图像的个体 ROI，并且有可能在未来成为一种新的诊断工具。
临床意义- 这种方法可以找到 tau 沉积的个体 ROI，从而实现更准确的阿尔茨海默病诊断。
