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pro vyhledávání: '"Xia, Wenjun"'
Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality. To ad
Externí odkaz:
http://arxiv.org/abs/2310.06949
Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate percept
Externí odkaz:
http://arxiv.org/abs/2310.03118
Computed tomography (CT) reconstructs volumetric images using X-ray projection data acquired from multiple angles around an object. For low-dose or sparse-view CT scans, the classic image reconstruction algorithms often produce severe noise and artif
Externí odkaz:
http://arxiv.org/abs/2306.08610
Autor:
Xia, Wenjun, Tseng, Hsin Wu, Niu, Chuang, Cong, Wenxiang, Zhang, Xiaohua, Liu, Shaohua, Ning, Ruola, Vedantham, Srinivasan, Wang, Ge
Breast cancer is the most prevalent cancer among women worldwide, and early detection is crucial for reducing its mortality rate and improving quality of life. Dedicated breast computed tomography (CT) scanners offer better image quality than mammogr
Externí odkaz:
http://arxiv.org/abs/2303.12861
Deep learning methods have impacted almost every research field, demonstrating notable successes in medical imaging tasks such as denoising and super-resolution. However, the prerequisite for deep learning is data at scale, but data sharing is expens
Externí odkaz:
http://arxiv.org/abs/2301.06604
Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. Recently, the deep learning based method for sparse-view CT reconstruction has attracted a major attention. However, neural
Externí odkaz:
http://arxiv.org/abs/2211.10388
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromis
Externí odkaz:
http://arxiv.org/abs/2209.15136
Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4D form. Compared with traditional CT based on energy-integrating
Externí odkaz:
http://arxiv.org/abs/2207.12639
Computed tomography (CT) is of great importance in clinical practice due to its powerful ability to provide patients' anatomical information without any invasive inspection, but its potential radiation risk is raising people's concerns. Deep learning
Externí odkaz:
http://arxiv.org/abs/2206.03709
Publikováno v:
IEEE Signal Processing Magazine, 40(2), 89-100, 2023
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks oft
Externí odkaz:
http://arxiv.org/abs/2203.15725