Tensor convolutional neural network architecture for spectral CT reconstruction

Yongyi Shi; Yongfeng Gao; Xuanqin Mou; Zhengrong Liang

doi:10.1117/12.2549289

16 March 2020 Tensor convolutional neural network architecture for spectral CT reconstruction

Yongyi Shi, Yongfeng Gao, Xuanqin Mou, Zhengrong Liang

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 1131221 (2020) https://doi.org/10.1117/12.2549289
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Photon-counting spectral computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among all channels. In addition, reconstruction of each energychannel image suffers photon count starving problem. To make full use of the correlation among different channels to suppress the data noise and enhance the tissue texture in reconstructing each energy-channel image, this paper proposed a tensor convolutional neural network (TCNN) architecture to learn a tissue-specific texture prior for PCCT reconstruction. Specifically, we first model the spatial texture prior information in each individual channel using a convolution neural network, and then extract the correlation information among different energy channels by merging the multi-channel networks. Finally, we integrate the TCNN as a prior into Bayesian reconstruction framework. To evaluate the tissue texture preserving performance of the proposed method for each channel, a vivid clinical phantom which can simulate the real tissue textures was employed. The improvement associated with TCNN is remarkable relative to simultaneous algebraic reconstruction technique (SART) and tensor dictionary learning (TDL) based reconstruction. The proposed method produced promising results in terms of not only preserving texture feature but also suppressing image noise in each channel. The proposed method outperforms the competing methods in both visual inspection and quantitative indexes of root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity (SSIM) and feature similarity (FSIM).

Conference Presentation

Citation Download Citation

Yongyi Shi, Yongfeng Gao, Xuanqin Mou, and Zhengrong Liang "Tensor convolutional neural network architecture for spectral CT reconstruction", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131221 (16 March 2020); https://doi.org/10.1117/12.2549289

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