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Proceedings Paper

Generative adversarial networks based regularized image reconstruction for PET
Author(s): Zhaoheng Xie; Reheman Baikejiang; Kuang Gong; Xuezhu Zhang; Jinyi Qi
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Paper Abstract

Image reconstruction in positron emission tomography (PET), especially from low-count projection data, is challenging due to the ill-posed nature of the inverse problem. Prior information can substantially improve the quality of reconstructed PET images. Previously, a PET image reconstruction method using a convolutional neural network (CNN) representation was proposed. In this work, we replace the original network with a generative adversarial network (GAN) to improve the network performance under limited number of training data. We also introduce an additional likelihood function in the objective function, which acts as a soft constraint on the network input. Evaluation study using real patient data with artificially inserted lesions demonstrated noticeable improvements in terms of lesion contrast recovery versus background noise trade-off.

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, 110720P (28 May 2019); doi: 10.1117/12.2534842
Show Author Affiliations
Zhaoheng Xie, Univ. of California, Davis (United States)
Reheman Baikejiang, Univ. of California, Davis (United States)
Kuang Gong, Univ. of California, Davis (United States)
Xuezhu Zhang, Univ. of California, Davis (United States)
Jinyi Qi, Univ. of California, Davis (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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