Name
Abstract | ||
|---|---|---|
Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. This is especially true for medical image reconstruction problems, where raw data are needed. Inspired by the deep image prior framework, in this work we proposed a personalized network training method where no prior training pairs are needed, but only the patient' own prior information. The network is updated during the iterative reconstruction process using the patient specific prior information and measured data. We formulated the maximum likelihood estimation as a constrained optimization problem and solved it using the alternating direction method of multipliers (ADMM) algorithm. Magnetic resonance imaging (MRI) guided Positron emission tomography (PET) reconstruction was employed as an example to demonstrate the effectiveness of the proposed framework. Quantification results based on simulation and real data show that the proposed reconstruction framework can outperform Gaussian post-smoothing and anatomically-guided reconstructions using the kernel method or the neural network penalty. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2018.2888491 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
Image reconstruction,Training,Neural networks,Biomedical imaging,Kernel,Positron emission tomography | Kernel (linear algebra),Iterative reconstruction,Computer vision,Medical imaging,Raw data,Gaussian,Artificial intelligence,Positron emission tomography,Artificial neural network,Kernel method,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 7 | 1558-254X |
Citations | PageRank | References |
6 | 0.42 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuang Gong | 1 | 23 | 5.10 |
| Ciprian Catana | 2 | 21 | 2.75 |
| Jinyi Qi | 3 | 284 | 35.82 |
| Quanzheng Li | 4 | 181 | 32.36 |