Superhydrophilic fluorinated polymer and nanogel for high-performance 19F magnetic resonance imaging

F-19 magnetic resonance imaging ((1)9F MRI), a kind of non-invasive and non-radioactive diagnostic technique with no endogenous background signals, opens up new research avenues for accurate molecular imaging studies. However, F-19 MRI is manily limited by the performance of contrast agents. Here, for the first time, we presented the zwitterionic fluorinated polymer and nanogel as new types of superhydrophilic, sensitive and ultra-stable F-19 MRI contrast agents. The superhydrophilicity of carboxybetaine zwitterionic structure completely overcame the hydrophobic aggregation-induced signal attenuation associated with amphiphilic fluorinated polymer-based nanoprobes. In addition, the superhydrophilic contrast agent exhibited distinct advantages, including high F-19 content (19.1 wt%), superior resistance to protein adsorption, constant MR properties and F-19 MRS-based quantitative determination in complex biological fluids, and intense F-19 MRI signals in the whole-body images after intravenous injection. In combination with angiogenesis targeting ligand, the superhydrophilic contrast agent was applied for the unambiguous detection of tumor. Importantly, computational algorithm was established for the directly quantitative determination of bioavailability and tumor-to-whole body ratio (TBR) from the in vivo F-19 MRI dataset, providing real-time information with non-invasive manner. Finally, crosslinked nanogels were developed with significantly prolonged systemic circulation, of which intense F-19 MRI signals nonspecifically distributed in the aortaventralis and blood-rich organs, instead of being trapped steadily in liver as with the state-of-the-art superhydrophobic perfluocarbon nanoemulsions. Overall, this kind of superhydrophilic, zwitterionic fluorinated polymer and nanogel could be defined as a new generation of high-performance F-19 MRI contrast agents, which hold great potential for image-based unambiguous disease detection and computational quantification.