Computational-efficient cascaded neural network for CT image reconstruction

Dufan Wu; Kyungsang Kim; Georges El Fakhri; Quanzheng Li

doi:10.1117/12.2511526

1 March 2019 Computational-efficient cascaded neural network for CT image reconstruction

Dufan Wu, Kyungsang Kim, Georges El Fakhri, Quanzheng Li

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109485Z (2019) https://doi.org/10.1117/12.2511526
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

In computed tomographic (CT) image reconstruction, image prior design and parameter tuning are important to improving the image reconstruction quality from noisy or undersampled projections. In recent years, the development of deep learning in medical image reconstruction made it possible to automatically find both suitable image priors and hyperparameters. By unrolling reconstruction algorithm to finite iterations and parameterizing prior functions and hyperparameters with deep artificial neural networks, all the parameters can be learned end-to-end to reduce the difference between reconstructed images and the training ground truth. Despite of its superior performance, the unrolling scheme suffers from huge memory consumption and computational cost in the training phase, made it hard to apply to 3 dimensional applications in CT, such as cone-beam CT, helical CT, tomosynthesis, etc. In this paper, we proposed a training-time computational-efficient cascaded neural network for CT image reconstruction, which had several sequentially trained cascades of networks for image quality improvement, connected with data fidelity correction steps. Each cascade was trained purely in the image domain, so that image patches could be utilized for training, which would significantly accelerate the training process and reduce memory consumption. The proposed method was fully scalable to 3D data with current hardware. Simulation of sparse-view sampling were done and demonstrated that the proposed method could achieve similar image quality compared to the state-of-the-art unrolled networks.

Citation Download Citation

Dufan Wu, Kyungsang Kim, Georges El Fakhri, and Quanzheng Li "Computational-efficient cascaded neural network for CT image reconstruction", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109485Z (1 March 2019); https://doi.org/10.1117/12.2511526

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available