@inproceedings{5e57d2f3ed514440a65f6e09c9893836,
title = "Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow",
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.",
keywords = "Computed tomography (CT), Deep learning, Image formation, Image processing",
author = "Missert, {Andrew D.} and Shuai Leng and McCollough, {Cynthia H.} and Fletcher, {Joel G.} and Lifeng Yu",
note = "Publisher Copyright: {\textcopyright} SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2019: Physics of Medical Imaging ; Conference date: 17-02-2019 Through 20-02-2019",
year = "2019",
doi = "10.1117/12.2513240",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Schmidt, {Taly Gilat} and Guang-Hong Chen and Hilde Bosmans",
booktitle = "Medical Imaging 2019",
}