Learned primal-dual reconstruction for dual energy computed tomography with reduced dose

Dufan Wu; Kyungsang Kim; Mannudeep K. Kalra; Bruno De Man; Quanzheng Li

doi:10.1117/12.2534943

28 May 2019 Learned primal-dual reconstruction for dual energy computed tomography with reduced dose

Dufan Wu, Kyungsang Kim, Mannudeep K. Kalra, Bruno De Man, Quanzheng Li

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107206 (2019) https://doi.org/10.1117/12.2534943
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

Dual energy computed tomography (DECT) usually uses 80kVp and 140kVp for patient scans. Due to high attenuation, the 80kVp image may become too noisy for reduced photon flux scenarios such as low-dose protocols or large-sized patients, further leading to unacceptable decomposed image quality. In this paper, we proposed a deep-neural-network-based reconstruction approach to compensate for the increased noise in low-dose DECT scan. The learned primal-dual network structure was used in this study, where the input and output of the network consisted of both low- and high-energy data. The network was trained on 30 patients who went through normal-dose chest DECT scans with simulated noises inserted into the raw data. It was further evaluated on another 10 patients undergoing half-dose chest DECT scans. Improved image quality close to the normal-dose scan was achieved and no significant bias was found on Hounsfield units (HU) values or iodine concentration.

Citation Download Citation

Dufan Wu, Kyungsang Kim, Mannudeep K. Kalra, Bruno De Man, and Quanzheng Li "Learned primal-dual reconstruction for dual energy computed tomography with reduced dose", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107206 (28 May 2019); https://doi.org/10.1117/12.2534943

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