Joint frequency-image domain network for image restoration in magnetic particle imaging.
Authors
Affiliations (5)
Affiliations (5)
- School of Biological Science and Medical Engineering, Beihang University, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China, Beijing, 100091, CHINA.
- Beihang University, School of Engineering Medicine & School of Biological Science and Medical Engineering, Beijing, Beijing, 100091, CHINA.
- Beihang University, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China, Beijing, Beijing, 100091, CHINA.
- Beihang University, School of Engineering Medicine, Beihang University, Beijing 100191, People's Republic of China, Beijing, Beijing, 100091, CHINA.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, Beijing, 100091, CHINA.
Abstract
<i>Objective.</i>Magnetic Particle Imaging (MPI) is a promising medical imaging technique that has been widely applied in preclinical stages. However, when expanding to human body scanning, cases often arise where superparamagnetic iron oxide nanoparticles (SPIOs) are located outside the field of view (FOV). In such cases, signal contributions from SPIOs outside the FOV generate boundary artifacts in the reconstructed images, compromising image accuracy. Therefore, restoring the affected images is crucial for the clinical translation of the MPI technology. Existing methods, such as overlapping scanning trajectories or joint reconstruction, effectively mitigate boundary artifacts but may still require further improvements in real-time imaging capabilities.<i>Approach.</i>In this study, we explore and utilize the spectral differences between SPIO signals inside and outside the FOV to design a dual-domain joint learning network for accurate restoration of MPI images. The network simultaneously takes as input both the affected images and their corresponding time-frequency map. Through feature extraction and adaptive weighted fusion, the network enhances its own ability to restore images.<i>Main results.</i>Our proposed Joint Frequency-Image Domain Network (JFI-Net) outperforms existing methods on the publicly available OpenMPI dataset and simulation datasets. Additionally, the network is applied to an in-house handheld MPI system, improving its imaging accuracy for large-sized vessel phantoms. Ablation experiments confirm the effectiveness of the proposed feature extraction and feature fusion modules within the network.<i>Significance.</i>This study presents an innovative solution to overcome boundary artifacts in MPI, significantly enhancing its quantitative accuracy for clinical applications. The proposed JFI-Net offers an efficient image restoration method that can contribute to the application of MPI technology in clinical practice.