PDF(Pubmed)
Abstract：
A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches\' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients\' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.
摘要：
大量研究提出从低剂量和嘈杂的计算机断层扫描（CT）扫描中获得高质量的图像，以减少辐射。然而,这些研究是针对人口水平的数据而设计的，没有考虑CT设备和个体的变化，限制当前方法的性能，尤其是超低剂量CT成像。这里,我们提出了PIMA-CT,集成无监督学习框架的物理拟人化模型，使用一种称为循环模拟和去噪(CSD)的新型深度学习技术，解决这些限制。我们首先获得了体模的成对的低剂量和标准剂量CT扫描，然后开发了两个生成神经网络：噪声模拟器和去噪器。模拟器从两个单独的图像空间中提取真实的低剂量噪声和组织特征(例如，低剂量体模模型扫描和标准剂量患者扫描）进入统一的特征空间。同时,去噪器向模拟器提供所产生噪声质量的反馈。这样,模拟器和去噪器循环交互以优化网络学习并简化去噪器以同时去除噪声和恢复组织特征。我们彻底评估了我们消除真实低剂量噪声和高斯模拟低剂量噪声的方法。结果表明，CSD优于最先进的去噪算法之一，无需使用任何标记数据（实际患者低剂量CT扫描）也无需模拟低剂量CT扫描。这项研究可能有助于在医学成像中结合物理模型，尤其是超低水平剂量CT扫描恢复。
