Self-supervised deep learning for joint 3D low-dose PET/CT image denoising
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- Self-supervised deep learning for joint 3D low-dose PET/CT image denoising
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A Review of deep learning methods for denoising of medical low-dose CT images
AbstractTo prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as ...
Highlights- Deep-learning methods for denoising of LDCT are comprehensively reviewed.
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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023AbstractWhile various deep learning methods have been proposed for low-dose computed tomography (CT) denoising, most of them leverage the normal-dose CT images as the ground-truth to supervise the denoising process. These methods typically ignore the ...
Self-supervised inter- and intra-slice correlation learning for low-dose CT image restoration without ground truth
AbstractTraining a convolutional neural network (CNN) to reduce noise in low-dose CT (LDCT) images typically relies on supervised learning, which requires input–target pairs of noisy LDCT and corresponding full-dose CT (FDCT) images. Although ...
Highlights- Training a CNN-based denoiser with LDCT images and without FDCT references.
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Pergamon Press, Inc.
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