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This paper investigates thermal ablation of cancer tissue as a controls problem. The objective is to deliver a lethal dose of thermal energy to the cancer tissue without damaging critical nearby healthy tissue. This paper proposes a model predictive controller (MPC) to regulate thermal dose. The model predictive controller guarantees the safety of the healthy tissue surrounding the tumor. The cost function rewards killing cancer cells. Simulations comparing the proposed controller with the bang-bang controller used in practice demonstrate that the MPC controller kills more of the cancer tissue and requires less time than the bang-bang controller, while damaging less healthy tissue.

There are two classes MPI selection fields: Field Free Point (FFP) magnets and Field Free Line (FFL) magnets [1, 2, 3]. A FFL selection field has three distinct benefits over a FFP selection field, namely (a) improved spatial resolution, (b) improved speed, and (c) improved SNR which scales with the number of projections [4]. Spatial resolution improves because the FFL is isotropic and hence uses the saturation magnetization of the pole material more efficiently. For example, a high strength small animal selection field magnet built with NdFeB permanent magnets or iron would be 10 T/m × 5 T/m × 5 T/m in a FFP magnet versus 10 T/m × 10 T/m in a FFL magnet. Imaging speed for the FFL is improved because the signal is integrated along the FFL axis, allowing for acquisition of projection images more than 100-times faster. This increase in imaging speed can also be traded for sensitivity through averaging, giving the FFL an order of magnitude sensitivity improvement for a given scan time. Three-dimensional imaging can be done using projection reconstruction [4] or iterative reconstruction techniques [3].

Magnetic particle imaging (MPI) is a noninvasive imaging method that detects superparamagnetic iron oxide (SPIO) nanoparticles within the body by their nonlinear behavior in the presence of external magnetic selection field [1]. A “selection field” is used to scan the body for the nonlinear signature of particles; essentially the particles outside of the instantaneous location of the field free region are saturated and hence induce no signal in our receiver coil.

As shown in Fig. 1b, the mean magneto stimulation thresholds for the transverse field was approximately 3 times lower than the axial field (ΔBmin = 26 mT-pp vs. 78 mT-pp) for the same set of subjects, with comparable τc values (265 μs vs. 243 μs). In addition, the percentage deviation across subjects was significantly smaller for the transverse field (13%) than the axial field (28%). For both transverse and axial fields, the data trends were well modeled by the hyperbolic amplitude vs. frequency curve, as demonstrated in Fig. 1c for a single subject.

Despite the vast potential of nanoparticles in anticancer therapies, the method of evaluating nanoparticle penetration within specific tumors is neither standardized nor mainstream. Multicellular tumor spheroids have been established as a popular model for in vitro screening of nanoparticles; however, their utility would be expanded considerably by improved quantification of nanoparticle diffusivity. This paper outlines a method to accurately estimate the diffusivity of nanoparticles in multicellular tumor spheroids using an unsteady-state diffusion based simulation model. This simulation model has been shown to accurately estimate diffusivity using synthetic data. Validation of the model's use in drug-delivery studies is accomplished through the high throughput creation of consistent tumor spheroids, introduction of fluorescently-labeled nanoparticles into the engineered tumors, and image processing of spheroid samples. Once validated, this system will allow researchers to efficiently quantify diffusivity for their nanoparticle drug delivery systems.