Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Guang-Hong Chen, Taly Gilat Schmidt
PublisherSPIE
ISBN (Electronic)9781510625433
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Physics of Medical Imaging - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10948
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Physics of Medical Imaging
CountryUnited States
CitySan Diego
Period2/17/192/20/19

Fingerprint

Workflow
Tomography
tomography
X-Ray Computed Tomography Scanners
Neural networks
simulation
Supervised learning
Learning
learning
scanners
output
Datasets

Keywords

  • Computed tomography (CT)
  • Deep learning
  • Image formation
  • Image processing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Missert, A. D., Leng, S., McCollough, C. H., Fletcher, J. G., & Yu, L. (2019). Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow. In H. Bosmans, G-H. Chen, & T. G. Schmidt (Eds.), Medical Imaging 2019: Physics of Medical Imaging [109482Y] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948). SPIE. https://doi.org/10.1117/12.2513240

Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow. / Missert, Andrew D.; Leng, Shuai; McCollough, Cynthia H; Fletcher, Joel Garland; Yu, Lifeng.

Medical Imaging 2019: Physics of Medical Imaging. ed. / Hilde Bosmans; Guang-Hong Chen; Taly Gilat Schmidt. SPIE, 2019. 109482Y (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Missert, AD, Leng, S, McCollough, CH, Fletcher, JG & Yu, L 2019, Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow. in H Bosmans, G-H Chen & TG Schmidt (eds), Medical Imaging 2019: Physics of Medical Imaging., 109482Y, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10948, SPIE, Medical Imaging 2019: Physics of Medical Imaging, San Diego, United States, 2/17/19. https://doi.org/10.1117/12.2513240
Missert AD, Leng S, McCollough CH, Fletcher JG, Yu L. Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow. In Bosmans H, Chen G-H, Schmidt TG, editors, Medical Imaging 2019: Physics of Medical Imaging. SPIE. 2019. 109482Y. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2513240
Missert, Andrew D. ; Leng, Shuai ; McCollough, Cynthia H ; Fletcher, Joel Garland ; Yu, Lifeng. / Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow. Medical Imaging 2019: Physics of Medical Imaging. editor / Hilde Bosmans ; Guang-Hong Chen ; Taly Gilat Schmidt. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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