.png){kind=icon}
返回
CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
.png){kind=icon}
OA
AI
DOI：10.1109/TMI.2018.2832656.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism to enforce that the reconstructed image is consistent with the measurements. We propose a relaxed version of PGD wherein gradient descent enforces measurement consistency, while a CNN recursively projects the solution closer to the space of desired reconstruction images. We show that this algorithm is guaranteed to converge and, under certain conditions, converges to a local minimum of a non-convex inverse problem. Finally, we propose a simple scheme to train the CNN to act like a projector. Our experiments on sparse-view computed-tomography reconstruction show an improvement over total variation-based regularization, dictionary learning, and a state-of-the-art deep learning-based direct reconstruction technique.
Keyword:
Deep learning
inverse problems
biomedical image reconstruction
low-dose computed tomography
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.8
论文数:
6.3K
被引数:
3.7W
机构
引用论文
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction基于方向小波的深度卷积神经网络用于低剂量x射线CT重建.png)
MEDICAL PHYSICSOAAI
MEDICAL PHYSICSIF3.2
OAAI分享收藏A Fluorometric Assay for DNA Cleavage Reactions Characterized with BamHI Restriction EndonucleaseOAAI
OAAI分享收藏
Image denoising via sparse and redundant representations over learned dictionaries通过学习字典上的稀疏和冗余表示进行图像去噪
PREAI
分享收藏
查看更多内容