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

By Liyue Shen, Wei Zhao, Lei Xing. Nature Biomedical Engineering, 2019

302 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Radiation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d18a9ad2bfc1b5a437a7fda2a977c83a613a9a67

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Citations in the corpus

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models (2021, methodology): “With the rise of machine learning, many methods (Zhu et al., 2018; Mardani et al., 2017; Shen et al., 2019; Würﬂ et al., 2018; Ghani & Karl, 2018; Wei et al., 2020) have been proposed for medical image reconstruction using a small number of measurements.”
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction (2021, background): “[9] proposed a deep network with a transformation module in high-dimensional feature space to learn the mapping from 2D projection radiographs to 3D anatomy for reconstructing volumetric CT images from sparse views.”
D-NeRF: Neural Radiance Fields for Dynamic Scenes (2020, background): “More recently, deep learning based techniques [36, 16, 10, 9, 25] are able to learn a neural volumetric representation from a set of sparse images.”
CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (2019, background): “The dataset with lesion masks and dataset with DR & DME grading labels may have domain shifts, and generative adversarial networks [57–60] will be beneficial for this task.”