Electromagnetic thermotherapy for deep organ ablation by using a needle array under a synchronized-coil system

Liver tumor ablation enhancement by induction-heating system with bitter-like deep magnetic field coil

The heated metal needle used for tumor thermotherapy is considered crucial for enhancing the practicality of cauterization using electromagnetic induction-heating techniques. In this study, a novel coil capable of producing a deep magnetic field is designed. In the proposed design, the coil structure is improved to enhance the intensity of the coil's deep magnetic field and its suitability for deep-tissue cauterization. Furthermore, a series of experiments are conducted using a single and consistent input current. The heating experiments are conducted at varying depths by placing the needle beneath the coil. The proposed coil significantly increases the induction-heating temperature and provides a solution to the long-standing problem of insufficient needle temperature. This research has also improved the usability of the induction-heating equipment in the field of deep tumor ablation.

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.