A denoising auto-encoder based on projection domain for low dose CT
| Autor: | Jiayu Duan, Ti Bai, Jianmei Cai, Xiaogang Chen, Xuanqin Mou |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Noise reduction Low dose Quantum noise Iterative reconstruction Image (mathematics) Domain (software engineering) Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence Denoising auto encoder business Projection (set theory) |
| Zdroj: | Medical Imaging 2018: Physics of Medical Imaging. |
| DOI: | 10.1117/12.2293585 |
| Popis: | There are growing concerns on the effect of the radiation, which can be decreased by reducing X-ray tube current. However, this manner will lead to the degraded image due to the quantum noise. In order to alleviate the problem, multiple methods have been explored both during reconstruction and in post-processing. Recently, Denoising Auto-Encoder(DAE) has drawn much attention which can generate clean images from corrupted input. Inspired by the idea of DAE, during the low dose acquisition, the noisy projection can be regarded as corrupted images. In this paper, we proposed a denoising method based on projection domain. First, the DAE is train from stimulation noisy data coupled with original data. Then utilize the DAE to correct noisy projection and get denoised image from statistical iterative reconstruction. With the implement of DAE in projection domain, the reconstructions show clearer details in soft tissue and have higher SSIM (structural similarity index) than other denoising methods in image domain. |
| Databáze: | OpenAIRE |
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