^99mTc-TRODAT-1 SPECT is effective for the early detection of Parkinson's disease (PD). However, SPECT images suffer from severe partial volume effect, which impairs tissue boundary clarity and subsequent quantification accuracy. This work proposes an anatomical prior- and segmentation-free deep learning (DL)-based partial volume correction (PVC) method using an attentionbased conditional generative adversarial network (Att-cGAN) for ^99mTc-TRODAT-1 SPECT. A population of 454 digital brain phantoms modelling anatomical and ^99mTc-TRODAT activity variations in different PD categories are used to generate realistic SPECT projections using the SIMIND Monte Carlo code, and then reconstructed using ordered subset expectation maximization algorithm. The dataset is split into 320, 44 and 90 used for training, validation, and testing. Att-cGAN, cGAN and U-Net are implemented based on simulated data, then directly tested on 100 retrospectively collected clinical ^99mTc-TRODAT data, with same acquisition and reconstruction parameters as in simulations. Non-DL PVC methods of Van-Cittert and iterative Yang are implemented for comparison. Physical and clinical metrics, as well as a no-gold standard technique (NGST) are applied to evaluate different PVC methods in the absence of clinical ground truth. Att-cGAN yields superior PVC performance in simulations as compared to other methods in physical and clinical evaluations. NGST assessment is generally consistent with the clinical metric evaluation. For the clinical study, Att-cGAN also obtains better NGST result than others striatal compartments can be discriminated on DLbased processed images. DL-PVC method is feasible for clinical PD SPECT using highly realistic simulated data.

^99mTc-TRODAT-1 SPECT 对帕金森病（PD）的早期检测非常有效。然而，SPECT 图像会受到严重的部分容积效应的影响，这会影响组织边界的清晰度和后续量化准确性。本文提出了一种基于注意力机制的条件生成对抗网络 (Att-cGAN) 的解剖先验信息和分割无关的深度学习（DL）方法来进行^99mTc-TRODAT-1 SPECT的部分容积校正(PVC)。使用了454个模拟大脑体模的数据，这些体模描绘了不同PD分类中解剖结构和^99mTc-TRODAT 活性变化的情况，并利用SIMIND蒙特卡洛代码生成真实的SPECT投影图像，然后通过有序子集期望最大化算法进行重建。数据被分为320、44和90用于训练、验证和测试。基于模拟数据实施了Att-cGAN、cGAN 和U-Net方法，然后直接在100个回顾性收集的临床^99mTc-TRODAT 数据上进行测试，其采集和重建参数与模拟时相同。为了比较，还实现了Van-Cittert 和迭代Yang非DL PVC 方法。通过物理和临床指标以及无金标准技术（NGST）来评估不同PVC方法在没有临床真实值的情况下表现如何。Att-cGAN 在物理和临床评价中的仿真中均表现出优于其他方法的PVC性能。NGST 评估通常与临床指标评价一致。对于临床研究，Att-cGAN也获得了比其他方法更好的NGST结果，DL处理后的图像可以区分纹状体各部分。使用高度逼真的模拟数据进行临床PD SPECT 的DL-PVC 方法是可行的。