Unal Mehmet Ozan, Ertas Metin, Yildirim Isa
Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey.
Department of Electrical and Electronics Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey.
PeerJ Comput Sci. 2024 Feb 29;10:e1849. doi: 10.7717/peerj-cs.1849. eCollection 2024.
In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method.
在计算机断层扫描（CT）成像中，一直以来最令人担忧的问题之一就是电离辐射。人们已经提出了几种方法来降低剂量水平，同时又不影响图像质量。随着深度学习的出现，由于计算能力的不断提高和大量数据集的出现，数据驱动的方法最近受到了广泛关注。基于深度学习的方法也已被以各种方式应用于解决低剂量CT重建问题。然而，这些方法的成功很大程度上取决于标记数据的可用性。另一方面，最近的研究表明，无需标记数据集也能成功进行训练。在本研究中，定义了一种训练方案，将低剂量投影用作其自身的训练目标。自监督原理应用于投影域。通过自监督训练对去噪神经网络的参数进行了优化。结果表明，在低剂量CT成像中对解析CT体模和人体CT图像的重建中，我们的方法优于传统方法和基于压缩感知的迭代方法，以及基于深度学习的无监督方法。我们方法的重建质量也与一种著名的监督方法相当。
