LCR-MPI measured dataset (ISBI-2026)

Dataset Sourse The proposed challenge aims to advance the development of robust and high-quality reconstruction algorithms tailored for magnetic particle imaging (MPI) under low tracer concentration conditions. MPI is an emerging noninvasive imaging modality that enablesreal-time visualization of superparamagnetic iron oxide nanoparticles, offering significant potential for applications in oncology, cardiovascular imaging, and stem cell tracking. However, the reconstruction process in MPI is inherently ill-conditioned, particularly at low concentrations, where signal-to-noise ratios degrade, leading to artifacts, reduced resolution, and inaccurate quantification. This challenge focuses on field free line MPI (FFL-MPI) setups, which have been increasingly adopted for human-scale systems and clinical translation due to their ability to provide potential high sensitivity. By soliciting innovative signal processing and machine learning-based solutions, the challenge seeks to address critical barriers in clinical MPI adoption, such as reducing biological toxicity and meeting the demands of cellular tracking applications. Participants will be tasked with reconstructing high-fidelity images from simulated and real low concentration FFL MPI datasets, evaluating metrics like structural similarity, peak signal-to-noise ratio, and quantitative accuracy. Dataset Introduction Please refer to the information provided on our official website for details about the dataset. Please note that currently, this project only provides a test dataset. LCR-MPI website: LCR-MPI-Challenge ISBI-2026 website: ISBI 2026 – International Symposium on Biomedical Imaging 2026 ISBI-2026 challenge website: Challenges – ISBI 2026 !! Data Usage and Copyright Statement !! This dataset is protected by copyright and intellectual property rights owned by the data collectors. Any use of this dataset for academic publication or commercial purposes requires prior written authorization from the data owners. Unauthorized use, reproduction, modification, or distribution of this dataset or any derivative works for academic publication is strictly prohibited. If you intend to use this dataset for academic research and publication,you must: 1. Contact the authors in advance 2. Submit a formal data usage application 3. Acknowledge the data source appropriately in your publications Violation of this statement may result in academic consequences and legal liability.