Online feedback focusing algorithm for hyperthermia cancer treatment

• Published in: International journal of hyperthermia Vol. 23; no. 7; pp. 539 - 554
• Main Authors: Cheng, Kung-Shan, Stakhursky, Vadim, Stauffer, Paul, Dewhirst, Mark, Das, Shiva K.
• Format: Journal Article
• Language: English
• Published: London Informa UK Ltd 01.11.2007; Taylor & Francis
• Subjects: Algorithms; Analgesics; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; automatic feedback control; Biological and medical sciences; Clinical death. Palliative care. Organ gift and preservation; Computer Simulation; effective perfusion; eigenvector; Feedback; feedback focusing; Humans; hyperthermia; Hyperthermia, Induced - methods; Hyperthermia, Induced - statistics & numerical data; Intensive care medicine; Magnetic Resonance Imaging; Medical sciences; Models, Statistical; Neoplasms - physiopathology; Neoplasms - therapy; Neuropharmacology; Online Systems; Phantoms, Imaging; Pharmacology. Drug treatments; Phased array optimization; power deposition; Temperature; Intensive care; Treatment; Malignant tumor; Algorithm; Hyperthermia; Resuscitation; Cancer
• ISSN: 0265-6736; 1464-5157
• DOI: 10.1080/02656730701678877

Purpose: Magnetic resonance (MR) imaging is increasingly being utilized to visualize the 3D temperature distribution in patients during treatment with hyperthermia or thermal ablation therapy. The goal of this work is to lay the foundation for improving the localization of heat in tumors with an online focusing algorithm that uses MR images as feedback to iteratively steer and focus heat into the target. Methods: The algorithm iteratively updates the model that quantifies the relationship between the source (antenna) settings and resulting tissue temperature distribution. At each step in the iterative process, optimal settings of power and relative phase of each antenna are computed to maximize averaged tumor temperature in the model. The MR-measured thermal distribution is then used to update/correct the model. This iterative procedure is repeated until convergence, i.e. until the model prediction and MR thermal image are in agreement. A human thigh tumor model heated in a 140 MHz four-antenna cylindrical mini-annular phased array is used for numerical validation of the proposed algorithm. Numerically simulated temperatures are used during the iterative process as surrogates for MR thermal images. Gaussian white noise with a standard deviation of 0.3°C and zero mean is added to simulate MRI measurement uncertainty. The algorithm is validated for cases where the source settings for the first iteration are based on erroneous models: (1) tissue property variability, (2) patient position mismatch, (3) a simple idealized patient model built from CT-based actual geometry, and (4) antenna excitation uncertainty due to load dependent impedance mismatch and antenna cross-coupling. Choices of starting heating vector are also validated. Results: The algorithm successfully steers and focuses a tumor when there is no antenna excitation uncertainty. Temperature is raised to ≥43°C for more than about 90% of tumor volume, accompanied by less than about 20% of normal tissue volume being raised to a temperature ≥41°C. However, when there is antenna excitation uncertainty, about 40% to 80% of normal tissue volume is raised to a temperature ≥41°C. No significant tumor heating improvement is observed in all simulations after about 25 iteration steps. Conclusions: A feedback control algorithm is presented and shown to be successful in iteratively improving the focus of tissue heating within a four-antenna cylindrical phased array hyperthermia applicator. This algorithm appears to be robust in the presence of errors in assumed tissue properties, including realistic deviations of tissue properties and patient position in applicator. Only moderate robustness was achieved in the presence of misaligned applicator/tumor positioning and antenna excitation errors resulting from load mismatch or antenna cross coupling.