Feasibility of thermal dose painting during magnetic nanoparticle thermal therapy: a computational analysis

Spatially confined magnetic nanoparticle thermal therapy (SC-MNTT) can improve the tumor coverage index (CI) when the magnetic field-free region (FFR) is manually steered. However, static steering demands significant time and expertise from experienced control engineers during treatment planning.

This study presents a computational framework for automated, dynamic FFR steering combined with real-time tumor boundary temperature control to enhance magnetic nanoparticle thermal therapy (MNTT) for cancer therapy. The framework also supports controlled MNTT treatment planning.

We integrated the Pennes bioheat transfer equation with a proportional-integral-derivative (PID) controller to maintain the maximum tumor boundary temperature at 46 °C. The magnetic nanoparticle (MNP) heat source was divided into four subdomains to simulate SC-MNTT. A fuzzy logic controller automatically selected the FFR based on four-point temperature measurements and corresponding thermal dose, aiming to maximize CI. We examined the influence of key treatment parameters: maximum treatment temperature (60 or 80 °C), MNP distribution (uniform or Gaussian), sensor noise, and MNP position relative to the tumor center.

Automated dynamic FFR steering increased CI by ∼25-35%, depending on these parameters. The highest CI occurred at a maximum treatment temperature of 80 °C with a uniform MNP distribution. Sensor noise had minimal effect, while minimal offset between the MNP position and tumor center was essential for maximizing CI.

The proposed fuzzy logic-based dynamic FFR steering may reduce reliance on expert control engineers for SC-MNTT treatment planning. Future work will focus on pre-clinical implementation and experimental validation of the control system.