ISSN 1004-4140
CN 11-3017/P
朱元正, 吕启闻, 官瑜, 等. 基于深度能量模型的低剂量CT重建[J]. CT理论与应用研究, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077.
引用本文: 朱元正, 吕启闻, 官瑜, 等. 基于深度能量模型的低剂量CT重建[J]. CT理论与应用研究, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077.
ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction based on deep energy models[J]. CT Theory and Applications, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077. (in Chinese).
Citation: ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction based on deep energy models[J]. CT Theory and Applications, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077. (in Chinese).

基于深度能量模型的低剂量CT重建

Low-dose CT Reconstruction Based on Deep Energy Models

  • 摘要: 降低计算机断层扫描(CT)的剂量对于降低临床应用中的辐射风险至关重要,深度学习的快速发展和广泛应用为低剂量CT成像算法的发展带来了新的方向。与大多数受益于手动设计的先验函数或有监督学习方案的现有先验驱动算法不同,本文使用基于深度能量模型来学习正常剂量CT的先验知识,然后在迭代重建阶段,将数据一致性作为条件项集成到低剂量CT的迭代生成模型中,通过郎之万动力学迭代更新训练的先验,实现低剂量CT重建。实验比较,证明所提方法的降噪和细节保留能力优良。

     

    Abstract: Reducing the dose of computed tomography (CT) is essential for reducing the radiation risk in clinical applications. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Unlike most existing prior-driven algorithms that benefit from manually designed prior functions or supervised learning schemes, in this paper, we use an energy-based deep model to learn the prior knowledge of normal-dose CT, and then in the iterative reconstruction phase, we integrate data consistency as a conditional item into the iterative generation model of low-dose CT, and realize the low-dose CT reconstruction through the prior experience of iterative updating training of Langevin dynamics. The experimental results show that the proposed method hold excellent noise reduction and detail retention capabilities.

     

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