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Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow
Author(s): Andrew D. Missert; Shuai Leng; Cynthia H. McCollough; Joel G. Fletcher; Lifeng Yu
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Paper Abstract

In this study we simulated the effect of reconstructing computed tomography (CT) images with different reconstruction kernels by employing a convolutional neural network (CNN) to map images produced by a fixed input kernel to images produced by different kernels. The CNN input images consisted of thin slices (0.6 mm) reconstructed with a sharpest kernel possible on the CT scanner. The network was trained using supervised learning to produce output images that simulate medium, medium-sharp, and sharp kernels. Performance was evaluated by comparing the simulated images to actual reconstructions performed on a reserved set of patient data. We found that the CNN simulated the effect of switching reconstruction kernels to a high level of accuracy, and in only a small fraction of the time that it takes to perform a full reconstruction. This application can potentially be used to streamline and simplify the clinical workflow for storing, viewing, and reconstructing CT images.

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482Y (1 March 2019); doi: 10.1117/12.2513240
Show Author Affiliations
Andrew D. Missert, Mayo Clinic (United States)
Shuai Leng, Mayo Clinic (United States)
Cynthia H. McCollough, Mayo Clinic (United States)
Joel G. Fletcher, Mayo Clinic (United States)
Lifeng Yu, Mayo Clinic (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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