A public projection imaging dataset for fieldfree line magnetic particle imaging

Magnetic particle imaging (MPI) is an emerging biomedical imaging modality. In recent years, human-scale MPI systems based on field-free line MPI (FFL-MPI) projection imaging have been increasingly advanced. However, the lack of publicly available FFL-MPI datasets has hindered algorithm development and system performance optimization. To address this issue, this paper presents a dataset based on the FFL-MPI system, named FFL-MPI-Data, through simulation studies and data acquisition from a real-world system.

The FFL-MPI-Data contains both simulated and measured data spanning multiple domains and harmonics. The measured dataset was acquired using a self-developed scanner, which collected raw voltage signals, sinograms, and reconstructed images from 100 physical phantoms. The simulated dataset was generated via a custom-built simulation framework that replicates the key parameters of the experimental scanner, with the MNIST dataset employed as digital phantoms. The simulation framework is validated by evaluating the characteristics of harmonic images output from numerical phantoms. Furthermore, the utility and reliability of the dataset were verified by evaluating the performance of a deep learning model (FBPConvNet) on both sparse-angle and limited-angle reconstruction tasks.

The FFL-MPI-Data repository adopts the Hierarchical Data Format (HDF5) for data storage, which contains both phantom data and associated metadata. The dataset is publicly available on the Zenodo platform. Furthermore, we provide open-source codes for data processing, image reconstruction, simulation implementation, and deep learning demonstrations at https://github.com/BUAALGH/FFL-MPI-Data. Users can readily access and utilize the FFL-MPI-Data dataset via the provided example code.

FFL-MPI-Data represents a publicly accessible dataset for FFL-MPI, addressing the scarcity of available data for projection imaging research in this domain. This dataset is expected to facilitate the development and validation of image reconstruction algorithms, including investigations into signal processing, multi-domain reconstruction, and multi-harmonic reconstruction for specific tasks such as sparse-angle and limited-angle reconstruction.