Health & Medical Equipment Business Unit, Samsung Electronics, Suwon-si, Gyeonggi-do, Korea.
Department of Digital Media and Communications Engineering, Sungkyunkwan University, Suwon-si, Gyeonggi-do, Korea.
PLoS One. 2024 Jul 26;19(7):e0306627. doi: 10.1371/journal.pone.0306627. eCollection 2024.
Photon-counting detector (PCD)-based computed tomography (CT) offers several advantages over conventional energy-integrating detector-based CT. Among them, the ability to discriminate energy exhibits significant potential for clinical applications because it provides material-specific information. That is, material decomposition (MD) can be achieved through energy discrimination. In this study, deep learning-based material decomposition was performed using live animal data. We propose MD-Unet, which is a deep learning strategy for material decomposition based on an Unet architecture trained with data from three energy bins. To mitigate the data insufficiency, we developed a pretrained model incorporating various simulation data forms and augmentation strategies. Incorporating these approaches into model training results in enhanced precision in material decomposition, thereby enabling the identification of distinct materials at individual pixel locations. The trained network was applied to the acquired animal data to evaluate material decomposition results. Compared with conventional methods, the newly generated MD-Unet demonstrated more accurate material decomposition imaging. Moreover, the network demonstrated an improved material decomposition ability and significantly reduced noise. In addition, they can potentially offer an enhancement level similar to that of a typical contrast agent. This implies that it can acquire images of the same quality with fewer contrast agents administered to patients, thereby demonstrating its significant clinical value.
基于光子计数探测器（PCD）的计算机断层扫描（CT）相较于传统的基于能量积分探测器的 CT 具有多项优势。其中，能够区分能量具有显著的临床应用潜力，因为它提供了物质特异性信息。也就是说，通过能量分辨可以实现物质分解（MD）。在这项研究中，使用活体动物数据进行了基于深度学习的物质分解。我们提出了 MD-Unet，这是一种基于 U 型网络的物质分解深度学习策略，该网络使用来自三个能量-bin 的数据进行训练。为了减轻数据不足的问题，我们开发了一种包含各种模拟数据形式和增强策略的预训练模型。将这些方法纳入模型训练中，可提高物质分解的精度，从而能够在各个像素位置识别出不同的物质。将训练好的网络应用于获取的动物数据中，以评估物质分解的结果。与传统方法相比，新生成的 MD-Unet 实现了更准确的物质分解成像。此外，该网络还表现出更好的物质分解能力和更低的噪声。此外，它们可能提供类似于典型造影剂的增强水平。这意味着它可以使用更少的造影剂获取相同质量的图像，从而显示出其重要的临床价值。
