Duan Xiaoman, Ding Xiao Fan, Khoz Samira, Chen Xiongbiao, Zhu Ning
Division of Biomedical Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
Biomed Phys Eng Express. 2024 Dec 26;11(1). doi: 10.1088/2057-1976/ad9f66.
. Propagation-based imaging computed tomography (PBI-CT) has been recently emerging for visualizing low-density materials due to its excellent image contrast and high resolution. Based on this, PBI-CT with a helical acquisition mode (PBI-HCT) offers superior imaging quality (e.g., fewer ring artifacts) and dose uniformity, making it ideal for biomedical imaging applications. However, the excessive radiation dose associated with high-resolution PBI-HCT may potentially harm objects or hosts being imaged, especially in live animal imaging, raising a great need to reduce radiation dose.. In this study, we strategically integrated Sparse2Noise (a deep learning approach) with PBI-HCT imaging to reduce radiation dose without compromising image quality. Sparse2Noise uses paired low-dose noisy images with different photon fluxes and projection numbers for high-quality reconstruction via a convolutional neural network (CNN). Then, we examined the imaging quality and radiation dose of PBI-HCT imaging using Sparse2Noise, as compared to when Sparse2Noise was used in low-dose PBI-CT imaging (circular scanning mode). Furthermore, we conducted a comparison study on the use of Sparse2Noise versus two other state-of-the-art low-dose imaging algorithms (i.e., Noise2Noise and Noise2Inverse) for imaging low-density materials using PBI-HCT at equivalent dose levels.. Sparse2Noise allowed for a 90% dose reduction in PBI-HCT imaging while maintaining high image quality. As compared to PBI-CT imaging, the use of Sparse2Noise in PBI-HCT imaging shows more effective by reducing additional radiation dose (30%-36%). Furthermore, helical scanning mode also enhances the performance of existing low-dose algorithms (Noise2Noise and Noise2Inverse); nevertheless, Sparse2Noise shows significantly higher signal-to-noise ratio (SNR) value compared to Noise2Noise and Noise2Inverse at the same radiation dose level.. Our proposed low-dose imaging strategy Sparse2Noise can be effectively applied to PBI-HCT imaging technique and requires lower dose for acceptable quality imaging. This would represent a significant advance imaging for low-density materials imaging and for future live animals imaging applications.
基于传播的成像计算机断层扫描（PBI-CT）由于其出色的图像对比度和高分辨率，最近在可视化低密度材料方面崭露头角。基于此，具有螺旋采集模式的PBI-CT（PBI-HCT）提供了卓越的成像质量（例如，更少的环形伪影）和剂量均匀性，使其成为生物医学成像应用的理想选择。然而，与高分辨率PBI-HCT相关的过量辐射剂量可能会对被成像的物体或主体造成潜在伤害，尤其是在活体动物成像中，因此迫切需要降低辐射剂量。在本研究中，我们将Sparse2Noise（一种深度学习方法）与PBI-HCT成像进行了策略性整合，以在不影响图像质量的情况下降低辐射剂量。Sparse2Noise通过卷积神经网络（CNN）使用具有不同光子通量和投影数的配对低剂量噪声图像进行高质量重建。然后，我们研究了使用Sparse2Noise的PBI-HCT成像的成像质量和辐射剂量，并与在低剂量PBI-CT成像（圆形扫描模式）中使用Sparse2Noise时进行了比较。此外，我们在等效剂量水平下，对使用Sparse2Noise与其他两种最先进的低剂量成像算法（即Noise2Noise和Noise2Inverse）用于PBI-HCT成像低密度材料进行了比较研究。Sparse2Noise在PBI-HCT成像中可实现90%的剂量降低，同时保持高图像质量。与PBI-CT成像相比，在PBI-HCT成像中使用Sparse2Noise通过减少额外的辐射剂量（30%-36%）显示出更有效的效果。此外，螺旋扫描模式也增强了现有低剂量算法（Noise2Noise和Noise2Inverse）的性能；然而，在相同辐射剂量水平下，Sparse2Noise显示出比Noise2Noise和Noise2Inverse显著更高的信噪比（SNR）值。我们提出的低剂量成像策略Sparse2Noise可以有效地应用于PBI-HCT成像技术，并且对于可接受质量的成像需要更低的剂量。这将代表在低密度材料成像以及未来活体动物成像应用方面的重大进展。
