1
|
Peng Z, Yin L, Sun Z, Liang Q, Ma X, An Y, Tian J, Du Y. DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging. Phys Med Biol 2023; 69:015002. [PMID: 38064750 DOI: 10.1088/1361-6560/ad13cf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023]
Abstract
Objective. Magnetic particle imaging (MPI) shows potential for contributing to biomedical research and clinical practice. However, MPI images are effectively affected by noise in the signal as its reconstruction is an ill-posed inverse problem. Thus, effective reconstruction method is required to reduce the impact of the noise while mapping signals to MPI images. Traditional methods rely on the hand-crafted data-consistency (DC) term and regularization term based on spatial priors to achieve noise-reducing and reconstruction. While these methods alleviate the ill-posedness and reduce noise effects, they may be difficult to fully capture spatial features.Approach. In this study, we propose a deep neural network for end-to-end reconstruction (DERnet) in MPI that emulates the DC term and regularization term using the feature mapping subnetwork and post-processing subnetwork, respectively, but in a data-driven manner. By doing so, DERnet can better capture signal and spatial features without relying on hand-crafted priors and strategies, thereby effectively reducing noise interference and achieving superior reconstruction quality.Main results. Our data-driven method outperforms the state-of-the-art algorithms with an improvement of 0.9-8.8 dB in terms of peak signal-to-noise ratio under various noise levels. The result demonstrates the advantages of our approach in suppressing noise interference. Furthermore, DERnet can be employed for measured data reconstruction with improved fidelity and reduced noise. In conclusion, our proposed method offers performance benefits in reducing noise interference and enhancing reconstruction quality by effectively capturing signal and spatial features.Significance. DERnet is a promising candidate method to improve MPI reconstruction performance and facilitate its more in-depth biomedical application.
Collapse
Affiliation(s)
- Zhengyao Peng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Lin Yin
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Zewen Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Qian Liang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, Shandon, People's Republic of China
| | - Yu An
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, People's Republic of China
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, People's Republic of China
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| |
Collapse
|
2
|
Hashimoto F, Onishi Y, Ote K, Tashima H, Yamaya T. Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm. Phys Med Biol 2023; 68:155009. [PMID: 37406637 DOI: 10.1088/1361-6560/ace49c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/05/2023] [Indexed: 07/07/2023]
Abstract
Objective. Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction method, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function.Approach. A practical implementation of a fully 3D PET image reconstruction could not be performed at present because of a graphics processing unit memory limitation. Consequently, we modify the DIP optimization to a block iteration and sequential learning of an ordered sequence of block sinograms. Furthermore, the relative difference penalty (RDP) term is added to the loss function to enhance the quantitative accuracy of the PET image.Main results. We evaluated our proposed method using Monte Carlo simulation with [18F]FDG PET data of a human brain and a preclinical study on monkey-brain [18F]FDG PET data. The proposed method was compared with the maximum-likelihood expectation maximization (EM), maximuma posterioriEM with RDP, and hybrid DIP-based PET reconstruction methods. The simulation results showed that, compared with other algorithms, the proposed method improved the PET image quality by reducing statistical noise and better preserved the contrast of brain structures and inserted tumors. In the preclinical experiment, finer structures and better contrast recovery were obtained with the proposed method.Significance.The results indicated that the proposed method could produce high-quality images without a prior training dataset. Thus, the proposed method could be a key enabling technology for the straightforward and practical implementation of end-to-end DIP-based fully 3D PET image reconstruction.
Collapse
Affiliation(s)
- Fumio Hashimoto
- Central Research Laboratory, Hamamatsu Photonics K. K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu 434-8601, Japan
- Graduate School of Science and Engineering, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Japan
- National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba, 263-8555, Japan
| | - Yuya Onishi
- Central Research Laboratory, Hamamatsu Photonics K. K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu 434-8601, Japan
| | - Kibo Ote
- Central Research Laboratory, Hamamatsu Photonics K. K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu 434-8601, Japan
| | - Hideaki Tashima
- National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba, 263-8555, Japan
| | - Taiga Yamaya
- Graduate School of Science and Engineering, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Japan
- National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba, 263-8555, Japan
| |
Collapse
|