Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI
文献类型：期刊论文
| 作者 | Lei Xiang; Yu Qiao; Dong Nie; Le An; Weili Lin; Qian Wang; Dinggang Shen |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2017 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Positron emission tomography (PET) is an essential technique in many clinical applications such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET images, a standard-dose radioactive tracer is needed, which inevitably causes the risk of radiation exposure damage. For reducing the patient's exposure to radiation and maintaining the high quality of PET images, in this paper, we propose a deep learning architecture to estimate the high-quality standard-dose PET (SPET) image from the combination of the low-quality low-dose PET (LPET) image and the accompanying T1-weighted acquisition from magnetic resonance imaging (MRI). Specifically, we adapt the convolutional neural network (CNN) to account for the two channel inputs of LPET and T1, and directly learn the end-to-end mapping between the inputs and the SPET output. Then, we integrate multiple CNN modules following the auto-context strategy, such that the tentatively estimated SPET of an early CNN can be iteratively refined by subsequent CNNs. Validations on real human brain PET/MRI data show that our proposed method can provide competitive estimation quality of the PET images, compared to the state-of-the-art methods. Meanwhile, our method is highly efficient to test on a new subject, e.g., spending ∼2 seconds for estimating an entire SPET image in contrast to ∼16 minutes by the state-of-the-art method. The results above demonstrate the potential of our method in real clinical applications. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11564]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | NEUROCOMPUTING |
| 推荐引用方式 GB/T 7714 | Lei Xiang,Yu Qiao,Dong Nie,et al. Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI[J]. NEUROCOMPUTING,2017. |
| APA | Lei Xiang.,Yu Qiao.,Dong Nie.,Le An.,Weili Lin.,...&Dinggang Shen.(2017).Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI.NEUROCOMPUTING. |
| MLA | Lei Xiang,et al."Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI".NEUROCOMPUTING (2017). |
入库方式： OAI收割
来源：深圳先进技术研究院
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