计算机科学
卷积神经网络
人工智能
迭代重建
正规化(语言学)
稳健性(进化)
深度学习
图像质量
模式识别(心理学)
图像(数学)
计算机视觉
生物化学
化学
基因
作者
Qianxue Shan,Junwu Wang,Dong Liu
出处
期刊:IEEE transactions on radiation and plasma medical sciences
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
被引量:1
标识
DOI:10.1109/trpms.2023.3280674
摘要
In this paper, we propose an unsupervised deep learning method for positron emission tomography reconstruction (PET) from incomplete data. This method utilizes the so-called deep image prior (DIP) as an untrained deep convolutional neural network (CNN) to generate object reconstructions. The main idea is to re-parameterize the image reconstruction problem as a neural network optimization problem. We show that the proposed method effectively addresses the incomplete data reconstruction problem, which otherwise degrades the image resolution and quality of standard reconstruction algorithms. Meanwhile, the proposed method does not require any pre-training procedures, i.e., it is not biased toward any particular dataset. Hence, it has the potential to be used in clinical situations, where training data would be infeasible or prohibitively expensive. The performance of the proposed approach is evaluated with noisy synthetic data based on shepp-logan and brainweb phantoms, and clinical naive rat data. In addition, robustness studies of the approach with respect to regularization parameters are also carried out. We showcase that the proposed method considerably outperforms the state-of-the-art methods, leading to flexible reconstruction from incomplete PET data.
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