Morovati Bahareh, Li Mengzhou, Han Shuo, Zhou Li, Wang Dayang, Wang Ge, Yu Hengyong
Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, United States of America.
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America.
Phys Med Biol. 2025 Feb 6;70(4):045008. doi: 10.1088/1361-6560/adaf71.
x-ray photon-counting detectors have recently gained popularity due to their capabilities in energy discrimination power, noise suppression, and resolution refinement. The latest extremity photon-counting computed tomography (PCCT) scanner leverages these advantages for tissue characterization, material decomposition, beam hardening correction, and metal artifact reduction. However, technical challenges such as charge splitting and pulse pileup can distort the energy spectrum and compromise image quality. Also, there is a clinical need to balance radiation dose and imaging speed for contrast-enhancement and other studies. This paper aims to address these challenges by developing a dual-domain correction approach to enhance PCCT reconstruction quality quantitatively and qualitatively.We propose a novel correction method that operates in both projection and image domains. In the projection domain, we employ a residual-based Wasserstein generative adversarial network to capture local and global features, suppressing pulse pileup, charge splitting, and data noise. This is facilitated with traditional filtering methods in the image domain to enhance signal-to-noise ratio while preserving texture across each energy channel. To address GPU memory constraints, our approach utilizes a patch-based volumetric refinement network.Our dual-domain correction approach demonstrates significant fidelity improvements across both projection and image domains. Experiments on simulated and real datasets reveal that the proposed model effectively suppresses noise and preserves intricate details, outperforming the state-of-the-art methods.This approach highlights the potential of dual-domain PCCT data correction to enhance image quality for clinical applications, showing promise for advancing PCCT image fidelity and applicability in preclinical/clinical environments.
X射线光子计数探测器最近因其在能量分辨能力、噪声抑制和分辨率提升方面的性能而受到青睐。最新的四肢光子计数计算机断层扫描（PCCT）扫描仪利用这些优势进行组织表征、物质分解、束硬化校正和减少金属伪影。然而，诸如电荷分裂和脉冲堆积等技术挑战会使能谱失真并损害图像质量。此外，在增强对比等研究中，临床上需要在辐射剂量和成像速度之间取得平衡。本文旨在通过开发一种双域校正方法来定量和定性地提高PCCT重建质量，以应对这些挑战。我们提出了一种在投影域和图像域都能运行的新型校正方法。在投影域，我们采用基于残差的瓦瑟斯坦生成对抗网络来捕捉局部和全局特征，抑制脉冲堆积、电荷分裂和数据噪声。在图像域借助传统滤波方法来提高信噪比，同时在每个能量通道上保留纹理。为了解决GPU内存限制问题，我们的方法利用了基于补丁的体细化网络。我们的双域校正方法在投影域和图像域都显示出显著的保真度提升。在模拟和真实数据集上的实验表明，所提出的模型有效地抑制了噪声并保留了复杂细节，优于现有方法。这种方法突出了双域PCCT数据校正对于临床应用提高图像质量的潜力，为提升PCCT图像保真度以及在临床前/临床环境中的适用性展现出前景。
