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Showing results 1 to 7 of 7

1
A Feasibility Study of Low-Dose Single-Scan Dual-Energy Cone-Beam CT in Many-View Under-Sampling Framework

Lee, Donghyeon; Lee, Jiseoc; Kim, Hoyi; Lee, Taewon; Soh, Jeongtae; Park, Miran; Kim, Changhwan; et al, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.36, no.12, pp.2578 - 2587, 2017-12

2
A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images

Kim, Byeongjoon; Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.46, no.9, pp.3906 - 3923, 2019-09

3
Cycle-consistent adversarial denoising network for multiphase coronary CT angiography

Kang, Eunhee; Koo, Hyun Jung; Yang, Dong Hyun; Seo, Joon Bum; Ye, Jong Chul, MEDICAL PHYSICS, v.46, no.2, pp.550 - 562, 2019-02

4
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network

Kang, Eunhee; Chang, Won; Yoo, Jaejun; Ye, Jong Chul, IEEE TRANSACTIONS ON MEDICAL IMAGING, v.37, no.6, pp.1358 - 1369, 2018-06

5
Low-dose CT denoising via convolutional neural network with an observer loss function

Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.48, no.10, pp.5727 - 5742, 2021-10

6
Super-sparsely view-sampled cone-beam CT by incorporating prior data

Abbas, Sajid; Min, Jonghwan; Cho, Seungryong, JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, v.21, no.1, pp.71 - 83, 2013

7
Utilization of an attentive map to preserve anatomical features for training convolutional neural-network-based low-dose CT denoiser

Han, Minah; Shim, Hyunjung; Baek, Jongduk, MEDICAL PHYSICS, v.50, no.5, pp.2787 - 2804, 2023-05

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