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Learned primal-dual reconstruction for dual energy computed tomography with reduced dose
Author(s): Dufan Wu; Kyungsang Kim; Mannudeep K. Kalra; Bruno De Man; Quanzheng Li
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Paper 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.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107206 (28 May 2019); doi: 10.1117/12.2534943
Show Author Affiliations
Dufan Wu, Massachusetts General Hospital and Harvard Medical School (United States)
Kyungsang Kim, Massachusetts General Hospital and Harvard Medical School (United States)
Mannudeep K. Kalra, Massachusetts General Hospital and Harvard Medical School (United States)
Bruno De Man, GE Global Research (United States)
Quanzheng Li, Massachusetts General Hospital and Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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