Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a deep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view. We demonstrate the feasibility of the approach with upper-abdomen, lung, and head-and-neck computed tomography scans from three patients. Volumetric reconstruction via deep learning could be useful in image-guided interventional procedures such as radiation therapy and needle biopsy, and might help simplify the hardware of tomographic imaging systems.

中文翻译：

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通过深度学习从单个投影视图重建体积计算机断层扫描图像的患者特定。

使用穿透波的断层成像可生成活体内部解剖结构的横截面视图。对于无伪影体积成像，需要来自大量角位置的投影视图。在这里，我们展示了经过训练以将患者的投影射线照片映射到相应 3D 解剖结构的深度学习模型随后可以从单个投影视图生成患者的体积断层 X 射线图像。我们通过来自三名患者的上腹部、肺和头颈部计算机断层扫描证明了该方法的可行性。通过深度学习进行体积重建可用于图像引导的介入手术，例如放射治疗和穿刺活检，并可能有助于简化断层成像系统的硬件。