Hyperthermia is an emerging cancer treatment modality, which involves applying heat to the malignant tumor. The heating can be delivered using electromagnetic (EM) energy, mostly in the radiofrequency (RF) or microwave range. Accurate patient-specific hyperthermia treatment planning (HTP) is essential for effective and safe treatments, in particular, for deep and loco-regional hyperthermia. An important aspect of HTP is the ability to focus microwave energy into the tumor and reduce the occurrence of hot spots in healthy tissue. This paper presents a method for optimizing the specific absorption rate (SAR) distribution for the head and neck cancer hyperthermia treatment. The SAR quantifies the rate at which localized RF or microwave energy is absorbed by the biological tissue when exposed to an EM field. A differential evolution (DE) optimization algorithm is proposed in order to improve the SAR coverage of the target region. The efficacy of the proposed algorithm is demonstrated by testing with the Erasmus MC patient dataset. DE is compared to the particle swarm optimization (PSO) method, in terms of average performance and standard deviation and across various clinical metrics, such as the hot-spot-tumor SAR quotient (HTQ), treatment quantifiers, and temperature parameters. While hot spots in the SAR distribution remain a problem with current approaches, DE enhances focusing microwave energy absorption to the target region during hyperthermia treatment. In particular, DE offers improved performance compared to the PSO algorithm currently deployed in the clinic, reporting a range of improvement of HTQ standard deviation of between 40.1-96.8% across six patients.

Clinical studies have shown that hyperthermia sensitizes tumor cells for conventional therapies. During phased-array microwave hyperthermia, an array of antennas is used to focus the electromagnetic waves at the target region. Selective heating, while preserving the healthy tissue, is a demanding challenge and currently patient specific pre-treatment planning is used to optimize the amplitudes and phases of the waves. In addition, when needed, this single optimal heat distribution is adapted using the simulations based on the feedback from thermo-sensors and the patient. In this paper, we hypothesize that sequential, i.e. 'time-multiplexed', application of multiple Pareto optimal heating patterns provides a better time-averaged treatment quality. To test the benefit of such a time-multiplexed approach, a multi-objective genetic algorithm was introduced to balance two objectives that both focus the specific absorption rate (SAR) delivered to the target region but differ in the suppressing of pre-defined hotspots. This step leads to two Pareto optimal distributions. These 'diverse' antenna settings are then applied sequentially and thermal simulations are used to evaluate the effectiveness of the time-multiplexed steering. The proposed technique is tested using treatment planning data of a representative dataset of five head and neck patients for the HYPERcollar3D. Steering dynamics are analysed and the time-multiplexed steering is compared to the current static solution used in the clinic, i.e. hotspot-target SAR quotient optimization using particle swarm optimization. Our results demonstrate that realistic steering periods of 10s suffice to stabilize temperatures within 0.04 °C and the ability to enhance target heating while reducing hotspots, i.e. 0.3 °C-1.2 °C improvement in T while reducing hotspot temperatures by 0.6 °C-1.5 °C.

Abstract:In this paper, we propose a novel statistical index for the early diagnosis of ventricular arrhythmia (VA) using the time delay phase-space reconstruction (PSR) technique, from the electrocardiogram (ECG) signal. Patients with two classes of fatal VA -with preceding ventricular premature beats (VPBs) and with no VPBs have been analysed using extensive simulations. Three subclasses of VA with VPBs viz. ventricular tachycardia (VT), ventricular fibrillation (VF) and VT followed by VF are analyzed using the proposed technique. Measures of descriptive statistics like mean (µ), standard deviation (σ), coefficient of variation (CV = σ/µ), skewness (γ) and kurtosis (β) in phase-space diagrams are studied for a sliding window of 10 beats of ECG signal using the box-counting technique. Subsequently, a hybrid prediction index which is composed of a weighted sum of CV and kurtosis has been proposed for predicting the impending arrhythmia before its actual occurrence. The early diagnosis involves crossing the upper bound of a hybrid index which is capable of predicting an impending arrhythmia 356 ECG beats, on average (with 192 beats standard deviation) before its onset when tested with 32 VA patients (both with and without VPBs). The early diagnosis result is also verified using a leave out cross-validation (LOOCV) scheme with 96.88% sensitivity, 100% specificity and 98.44% accuracy.2

Aim: To develop a statistical index based on the phase space reconstruction (PSR) of the electrocardiogram (ECG) for the accurate and timely diagnosis of ventricular tachycardia (VT) and ventricular fibrillation (VF). Methods: Thirty-two ECGs with sinus rhythm (SR) and 32 ECGs with VT/VF were analyzed using the PSR technique. Firstly, the method of time delay embedding were employed with the insertion of delay "τ" in the original time-series X(t), which produces the Y(t) = X(t − τ). Afterwards, a PSR diagram was reconstructed by plotting Y(t) against X(t). The method of box counting was applied to analyze the behavior of the PSR trajectories. Measures as mean (μ), standard deviation (σ) and coefficient of variation (CV = σ/μ), kurtosis (β) for the box counting of PSR diagrams were reported. Results: During SR, CV was always b0.05, while with the onset of arrhythmia CV increased N0.05. A similar pattern was observed with β, where b 6 was considered as the cut-off point between SR and VT/VF. Therefore, the upper threshold for SR was considered CV th = 0.05 and β th b 6. For optimisation of the accuracy, a new index (J) was proposed: J ¼ w CV CV th þ 1−w ð Þ β β th : During SR the upper limit of J was the value of 1. Furthermore CV, β and J crossed the cut-off point timely before the onset of arrhythmia (average time: 4 min 31 s; SD: 2 min 30 s); allowing sufficient time for preventive therapy. Conclusion: The J index improved ECG utility for arrhythmia monitoring and detection utility, allowing the prompt and accurate diagnosis of ventricular arrhythmias.

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