Work

End-to-end MPI image reconstruction with dual-task generative adversarial network

Year: 2024

Type: article

Abstract: Traditional reconstruction methods, such as system matrix and x-space, are either extremely time-consuming or result in very blurry images. Here, we propose a novel dual-task generative method to realize high-quality MPI image reconstruction. In this method, the generative model simultaneously undertakes two MPI image processing tasks: reconstruction and segmentation. The main task of image reconstruction generates MPI images, while the auxiliary task of image segmentation guides the main task to focus on key areas of objects in MPI images during the generation process. Our experimental results showed that the proposed dual-task model, with its superior generalization ability, outperforms both traditional MPI reconstruction methods and single-task generative methods. Our results also suggested that the tasks of image generation and image segmentation significantly promote each other during the MPI image reconstruction.

Source: International journal on magnetic particle imaging

DOI: 10.18416/ijmpi.2025.2503071

Authors: Liu, Xinyi, Zhao, Jing, Tian, Jie, Zhang, Hui

Institutions: Beihang University, Ministry of Industry and Information Technology

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Awards: G1464784459, G6288977234