Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
نویسندگان
چکیده
منابع مشابه
Wavelet Residual Network for Low-Dose CT via Deep Convolutional Framelets
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed the world-first deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture were not fully recovered. To cope with this problem, here we propose ...
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Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally complex because of the repeated use of the forward and backward projection. Inspired by this success of deep learning in computer vision applications, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challeng...
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In order to effectively reduce the risk of radiation exposure, low-dose CT using fewer projection views is becoming more and more preferred recently although it suffers significantly from poor image quality. The deep learning approach has demonstrated its effectiveness in natural image field. However, its application in medical imaging is still lacking. In this paper, we present a novel deep re...
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Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CTassociated x-ray radiation carries health risks for patients. The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in lo...
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ژورنال
عنوان ژورنال: IEEE Transactions on Medical Imaging
سال: 2018
ISSN: 0278-0062,1558-254X
DOI: 10.1109/tmi.2018.2823756