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

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 rad...

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Published in	Nature biomedical engineering Vol. 3; no. 11; pp. 880 - 888
Main Authors	Shen, Liyue, Zhao, Wei, Xing, Lei
Format	Journal Article
Language	English
Published	London Nature Publishing Group UK 01.11.2019 Nature Publishing Group
Subjects	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
Online Access	Get full text
ISSN	2157-846X 2157-846X
DOI	10.1038/s41551-019-0466-4

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Abstract	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.
AbstractList	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. 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. 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.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. Tomographic imaging via 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. 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.
Author	Shen, Liyue Zhao, Wei Xing, Lei
AuthorAffiliation	2 Department of Electrical Engineering, Stanford University, Stanford, USA, 94305 1 Department of Radiation Oncology, Stanford University, Stanford, USA, 94305
AuthorAffiliation_xml	– name: 2 Department of Electrical Engineering, Stanford University, Stanford, USA, 94305 – name: 1 Department of Radiation Oncology, Stanford University, Stanford, USA, 94305
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 L.X. proposed the original notion of single-view reconstruction for tomographic imaging and supervised the research, L.S. designed and implemented the algorithm. W.Z. designed the experiments and implemented the data generation process. L.S. and W.Z. carried out experimental work. L.X., L.S. and W.Z. wrote the manuscript. All the authors reviewed the manuscript. These authors contributed equally Author contributions
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Snippet	Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging,... Tomographic imaging via penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging,...
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SubjectTerms	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
Title	Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning
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