Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3843-3848. doi: 10.1109/EMBC48229.2022.9871757.
Computed tomography (CT) is an effective med-ical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, including generation of thin slice multi planar cross-sectional body imaging and 3D reconstructions. However, this involves patients being exposed to a considerable dose of ionising radiation. Excessive ionising radiation can lead to deterministic and harmful effects on the body. This paper proposes a Deep Learning model that learns to reconstruct CT projections from a few or even a single-view X-ray. This is based on a novel architecture that builds from neural radiance fields, which learns a continuous representation of CT scans by disentangling the shape and volumetric depth of surface and internal anatomical structures from 2D images. Our model is trained on chest and knee datasets, and we demonstrate qual-itative and quantitative high-fidelity renderings and compare our approach to other recent radiance field-based methods. Our code and link to our datasets are available at https://qithub.com/abrilcf/mednerf Clinical relevance- Our model is able to infer the anatomical 3D structure from a few or a single-view X-ray showing future potential for reduced ionising radiation exposure during the imaging process.
计算机断层扫描（CT）是一种有效的医学成像方式，在临床医学领域广泛用于诊断各种病变。多排 CT 成像技术的进步使其具有了更多的功能，包括生成薄片多平面横断面身体成像和 3D 重建。然而，这涉及到患者暴露在相当大剂量的电离辐射下。过量的电离辐射会对身体造成确定性和有害的影响。本文提出了一种深度学习模型，该模型可以从少数甚至单个 X 射线视图中学习重建 CT 投影。这是基于一种新颖的架构，该架构基于神经辐射场构建，通过从 2D 图像中分离表面和内部解剖结构的形状和体积深度，学习 CT 扫描的连续表示。我们的模型在胸部和膝盖数据集上进行了训练，并展示了高质量的高保真渲染效果，并将我们的方法与其他最近的基于辐射场的方法进行了比较。我们的代码和数据集的链接在 https://qithub.com/abrilcf/mednerf 上。临床意义——我们的模型能够从少数或单个 X 射线视图中推断出解剖 3D 结构，这表明在成像过程中减少电离辐射暴露具有未来潜力。
