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

Andrew D. Missert; Shuai Leng; Cynthia H. McCollough; Joel G. Fletcher; Lifeng Yu

doi:10.1117/12.2513240

1 March 2019 Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow

Andrew D. Missert, Shuai Leng, Cynthia H. McCollough, Joel G. Fletcher, Lifeng Yu

Author Affiliations +

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109482Y (2019) https://doi.org/10.1117/12.2513240
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Andrew D. Missert, Shuai Leng, Cynthia H. McCollough, Joel G. Fletcher, and Lifeng Yu "Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482Y (1 March 2019); https://doi.org/10.1117/12.2513240

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