论文标题
MEDNERF：用于重建来自单个X射线的3D感知CT项目的医学神经辐射场
MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray
论文作者
论文摘要
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理。多探测器CT成像技术的进步已经实现了其他功能，包括生成薄片多平台横截面体成像和3D重建。但是，这涉及患者暴露于相当剂量的电离辐射。过度的电离辐射会导致对身体的确定性和有害影响。本文提出了一个深度学习模型，该模型学会从几个甚至单视X射线中重建CT投影。这是基于一种从神经辐射场构建的新型体系结构，该结构通过将表面和内部解剖结构与2D图像的形状和体积深度解开，从而了解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，我们展示了定性和定量的高保真效果图，并将我们的方法与其他最近基于辐射的方法进行比较。我们的代码和数据集链接可在https://github.com/abrilcf/mednerf上找到
Computed tomography (CT) is an effective medical 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 multiplanar 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 qualitative 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://github.com/abrilcf/mednerf
