Journal Article

Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning

Shen, Liyue, Zhao, Wei, Xing, Lei

2019

Overview

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. A deep-learning model trained to map 2D projection views of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 59 / 639/166/985 / 692/4028/67/1059/485 / 692/700/1421/1846/2771 / 692/700/1421/2025 / Abdomen - diagnostic imaging / Anatomy / Angular position / Biomedical and Life Sciences / Biomedical Engineering/Biotechnology / Biomedicine / Biopsy / Biopsy, Needle / Computed tomography / Cone-Beam Computed Tomography - methods / Deep Learning / Head - diagnostic imaging / Humans / Image reconstruction / Imaging, Three-Dimensional - methods / Lung - diagnostic imaging / Machine learning / Medical imaging / Neck - diagnostic imaging / Projection / Radiation therapy / Radiographs / Radiography / Radiotherapy / Radiotherapy Planning, Computer-Assisted - methods / Tomography / Two dimensional models