Dynamic low-count PET image reconstruction using spatio-temporal primal dual network
Dynamic low-count PET image reconstruction using spatio-temporal primal dual network
复制标题
DOI：
10.1088/1361-6560/acde3e
复制
发表时间：
2023-06
影响因子：
3.5
通讯作者：
Rui Hu;Jianan Cui;Chenxu Li;Chengjin Yu;Yunmei Chen;Huafeng Liu
中科院分区：
文献类型：
--
作者：
Rui Hu;Jianan Cui;Chenxu Li;Chengjin Yu;Yunmei Chen;Huafeng Liu
Objective.动态正电子发射断层扫描（PET）成像，可以提供生理代谢的动态变化的信息，现在被广泛应用于临床诊断和癌症治疗。然而，从动态数据的重建是极具挑战性的，由于在单个帧中接收到的计数有限，特别是在超短帧。最近，展开的基于模型的深度学习方法在具有良好可解释性的低计数PET图像重建方面取得了令人鼓舞的结果。然而，现有的基于模型的深度学习方法主要关注空间相关性，而忽略了时间域。Approach.在本文中，启发学习原始对偶（LPD）算法，我们提出了时空原始对偶网络（STPDnet）的动态低计数PET图像重建。空间和时间相关性都是通过3D卷积算子编码的。PET的物理投影嵌入到网络的迭代学习过程中，提供了物理约束，增强了可解释性。主要结果。仿真数据和真实的大鼠扫描数据的实验结果表明，该方法在时域和空域均能有效地去除噪声，且性能优于最大似然期望最大化、时空核方法、LPD和FBPnet。意义实验结果表明，STPDnet在低计数情况下具有更好的重建性能，特别适用于要求极短帧且噪声水平较高的全身动态成像和参数PET成像。
Objective. Dynamic positron emission tomography (PET) imaging, which can provide information on dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited counts received in individual frame, especially in ultra short frames. Recently, the unrolled model-based deep learning methods have shown inspiring results for low-count PET image reconstruction with good interpretability. Nevertheless, the existing model-based deep learning methods mainly focus on the spatial correlations while ignore the temporal domain. Approach. In this paper, inspired by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network (STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability. Main results. The experiments of both simulation data and real rat scan data have shown that the proposed method can achieve substantial noise reduction in both temporal and spatial domains and outperform the maximum likelihood expectation maximization, spatio-temporal kernel method, LPD and FBPnet. Significance. Experimental results show STPDnet better reconstruction performance in the low count situation, which makes the proposed method particularly suitable in whole-body dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer from high level of noise.