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Quality-guided deep reinforcement learning for parameter tuning in iterative CT reconstruction
Author(s): Chenyang Shen; Min-Yu Tsai; Yesenia Gonzalez; Liyuan Chen; Steve B. Jiang; Xun Jia
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Paper Abstract

Tuning parameters in a reconstruction model is of central importance to iterative CT reconstruction, since it critically affects the resulting image quality. Manual parameter tuning is not only tedious, but becomes impractical when there exits a number of parameters. In this paper, we develop a novel deep reinforcement learning (DRL) framework to train a parameter-tuning policy network (PTPN) to automatically adjust parameters in a human-like manner. A quality assessment network (QAN) is trained together with PTPN to learn how to judge CT image quality, serving as a reward function to guide the reinforcement learning. We demonstrate our idea in an iterative CT reconstruction problem with pixel-wise total-variation regularization. Experimental results demonstrates the effectiveness of both PTPN and QAN, in terms of tuning parameter and evaluating image quality, respectively.

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, 1107203 (28 May 2019); doi: 10.1117/12.2534948
Show Author Affiliations
Chenyang Shen, Univ. of Texas Southwestern Medical Ctr. (United States)
Min-Yu Tsai, Univ. of Texas Southwestern Medical Ctr. (United States)
Yesenia Gonzalez, Univ. of Texas Southwestern Medical Ctr. (United States)
Liyuan Chen, Univ. of Texas Southwestern Medical Ctr. (United States)
Steve B. Jiang, Univ. of Texas Southwestern Medical Ctr. (United States)
Xun Jia, Univ. of Texas Southwestern Medical Ctr. (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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