论文标题
完全3D实现端到端的深层图像先验的宠物图像重建，使用块迭代算法
Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm
论文作者
论文摘要
由于其无监督的正电子发射断层扫描（PET）图像重建，深度图像先验（DIP）最近引起了人们的关注，这不需要任何事先的培训数据集。在本文中，我们提出了第一次实施基于端到端DIP的完全3D PET图像重建方法的尝试，该方法将前向预测模型纳入损失函数。为了实现实用的完全3D PET图像重建，由于图形处理单元存储器限制无法执行，我们将浸入优化修改为块介质，并顺序学习一个有序的块纹状体序列。此外，将相对差异惩罚（RDP）项添加到损耗函数中，以提高定量PET图像精度。我们使用蒙特卡洛模拟评估了我们提出的方法，其中包括人脑的[$^{18} $ f] FDG PET数据以及一项关于猴子脑的临床前研究[$^{18} $ f] FDG PET数据。将所提出的方法与最大可能性期望最大化（EM），具有RDP的最大A-Posterior EM和基于混合DIP的PET重建方法进行了比较。仿真结果表明，与其他算法相比，该方法通过减少统计噪声并保留了脑结构和插入肿瘤的对比来提高PET图像质量。在临床前实验中，通过提出的方法获得了更精细的结构和更好的对比度恢复。这表明所提出的方法可以在没有事先培训数据集的情况下产生高质量的图像。因此，提出的方法是一种关键的促进技术，用于直接实用的基于端到端倾斜的完全3D PET图像重建。
Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function. To implement a practical fully 3D PET image reconstruction, which could not be performed due to a graphics processing unit memory limitation, we modify the DIP optimization to block-iteration and sequentially learn an ordered sequence of block sinograms. Furthermore, the relative difference penalty (RDP) term was added to the loss function to enhance the quantitative PET image accuracy. We evaluated our proposed method using Monte Carlo simulation with [$^{18}$F]FDG PET data of a human brain and a preclinical study on monkey brain [$^{18}$F]FDG PET data. The proposed method was compared with the maximum-likelihood expectation maximization (EM), maximum-a-posterior EM with RDP, and hybrid DIP-based PET reconstruction methods. The simulation results showed that the proposed method improved the PET image quality by reducing statistical noise and preserved a contrast of brain structures and inserted tumor compared with other algorithms. In the preclinical experiment, finer structures and better contrast recovery were obtained by the proposed method. This indicated that the proposed method can produce high-quality images without a prior training dataset. Thus, the proposed method is a key enabling technology for the straightforward and practical implementation of end-to-end DIP-based fully 3D PET image reconstruction.
