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Article

On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model

1
The Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
2
The Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
3
The Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Kenny H. Cha and Emilio Quaia
Tomography 2022, 8(5), 2129-2152; https://doi.org/10.3390/tomography8050179
Received: 23 May 2022 / Revised: 17 August 2022 / Accepted: 20 August 2022 / Published: 24 August 2022
(This article belongs to the Special Issue Advance in CT Imaging Using Deep Learning)
Ultra-sparse-view computed tomography (CT) algorithms can reduce radiation exposure for patients, but these algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra-low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likelihood. We applied X2CT-FLOW for the reconstruction of 3D chest CT images from biplanar projection images without noise contamination (assuming a standard-dose protocol) and with strong noise contamination (assuming an ultra-low-dose protocol). We simulated an ultra-low-dose protocol. With the standard-dose protocol, our images reconstructed from 2D projected images and 3D ground-truth CT images showed good agreement in terms of structural similarity (SSIM, 0.7675 on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean absolute error (MAE, 0.02364 on average), and normalized root mean square error (NRMSE, 0.05731 on average). Moreover, with the ultra-low-dose protocol, our images reconstructed from 2D projected images and the 3D ground-truth CT images also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB on average), MAE (0.02991 on average), and NRMSE (0.07349 on average). View Full-Text
Keywords: computed tomography; deep learning; image reconstruction; maximum a posteriori; unsupervised learning; X-rays computed tomography; deep learning; image reconstruction; maximum a posteriori; unsupervised learning; X-rays
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MDPI and ACS Style

Shibata, H.; Hanaoka, S.; Nomura, Y.; Nakao, T.; Takenaga, T.; Hayashi, N.; Abe, O. On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model. Tomography 2022, 8, 2129-2152. https://doi.org/10.3390/tomography8050179

AMA Style

Shibata H, Hanaoka S, Nomura Y, Nakao T, Takenaga T, Hayashi N, Abe O. On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model. Tomography. 2022; 8(5):2129-2152. https://doi.org/10.3390/tomography8050179

Chicago/Turabian Style

Shibata, Hisaichi, Shouhei Hanaoka, Yukihiro Nomura, Takahiro Nakao, Tomomi Takenaga, Naoto Hayashi, and Osamu Abe. 2022. "On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model" Tomography 8, no. 5: 2129-2152. https://doi.org/10.3390/tomography8050179

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