Contrast agents have been widely used in medicine to enhance contrast in magnetic resonance imaging (MRI). Among them, super paramagnetic iron oxide nanoparticles (SPION) have been reported to have low risk in clinical use. In our study, F127-Folate coated SPION was fabricated in order to efficiently target tumors and provide imaging contrast in MRI. SPION alone have an average core size of 15 nm. After stabilizing with Pluronic F127, the nanoparticles reached a hydrodynamic size of 180 nm and dispersed well in various kinds of media. The F127-Folate coated SPION were shown to specifically target folate receptor expressing cancer cells by flow cytometry analysis, confocal laser scanning microscope, as well as in vitro MRI. Furthermore, in vivo MRI images have shown the enhanced negative contrast from the F127-Folate coated SPION in tumor-bearing mice. In conclusion, our F127-Folate coated SPION have shown great potential as a contrast agent in MRI, as well as in the combination with drug delivery for cancer therapy.

A Hessian-based state-to-state optimal control method in Liouville space is presented to mitigate previously undesirable polynomial scaling of Hessian computation time. This method, an improvement to the state-of-the-art Newton-Raphson gradient ascent pulse engineering (GRAPE) method, is derived with respect to two exact time-propagator derivative calculation techniques, auxiliary matrix and efficient spin control using analytical Lie algebraic derivatives (ESCALADE) methods. We observed that compared to the best current implementation of the Newton-Raphson GRAPE method, for an ensemble of two-level systems, with realistic conditions, our auxiliary matrix and ESCALADE Hessians can be 4–200 and 70–600 times faster, respectively. Additionally, the Newton-Raphson GRAPE method using ESCALADE is presented in a Liouville space for higher-level systems and with the derivation of $\mathrm{x}$-, $\mathrm{y}$-, and $\mathrm{z}$-control propagator derivatives, also extending the application of ESCALADE and the recent quantum optimal control by adaptive low-cost algorithm (QOALA) method for coupled systems.