Hyperthermia Treatment Monitoring via Deep Learning Enhanced Microwave Imaging: A Numerical Assessment

Simple Summary Non-invasive temperature monitoring during hyperthermia cancer treatment is of paramount importance. It allows physicians to verify the therapeutic temperature is reached in the treated area. Currently, only superficial or invasive thermometry is performed on a clinical level. Magnetic resonance thermometry has been proposed as a a non-invasive alternative but its applicability is limited. Conversely, microwave imaging based thermometry is a potential low cost candidate for non-invasive temperature monitoring. This works presents a computational study in which the use of deep learning is proposed to face the challenges related to the use of microwave imaging in hyperthermia monitoring. The paper deals with the problem of monitoring temperature during hyperthermia treatments in the whole domain of interest. In particular, a physics-assisted deep learning computational framework is proposed to provide an objective assessment of the temperature in the target tissue to be treated and in the healthy one to be preserved, based on the measurements performed by a microwave imaging device. The proposed concept is assessed in-silico for the case of neck tumors achieving an accuracy above 90%. The paper results show the potential of the proposed approach and support further studies aimed at its experimental validation

Numerical Simulation in Biomechanics and Biomedical Engineering

In the first contribution, Morbiducci and co-workers discuss the theoretical and methodological bases supporting the Lagrangian- and Euler-based methods, highlighting their application to cardiovascular flows. The second contribution, by the Ansón and van Lenthe groups, proposes an automated virtual bench test for evaluating the stability of custom shoulder implants without the necessity of mechanical testing. Urdeitx and Doweidar, in the third paper, also adopt the finite element method for developing a computational model aim to study cardiac cell behavior under mechano-electric stimulation. In the fourth contribution, Ayensa-Jiménez et al. develop a methodology to approximate the multidimensional probability density function of the parametric analysis obtained developing a mathematical model of the cancer evolution. The fifth paper is oriented to the topological data analysis; the group of Cueto and Chinesta designs a predictive model capable of estimating the state of drivers using the data collected from motion sensors. In the sixth contribution, the Ohayon and Finet group uses wall shear stress-derived descriptors to study the role of recirculation in the arterial restenosis due to different malapposed and overlapping stent conditions. In the seventh contribution, the research group of Antón demonstrates that the simulation time can be reduced for cardiovascular numerical analysis considering an adequate geometry-reduction strategy applicable to truncated patient specific artery. In the eighth paper, Grasa and Calvo present a numerical model based on the finite element method for simulating extraocular muscle dynamics. The ninth paper, authored by Kahla et al., presents a mathematical mechano-pharmaco-biological model for bone remodeling. Martínez, Peña, and co-workers propose in the tenth paper a methodology to calibrate the dissection properties of aorta layer, with the aim of providing useful information for reliable numerical tools. In the eleventh contribution, Martínez-Bocanegra et al. present the structural behavior of a foot model using a detailed finite element model. The twelfth contribution is centered on the methodology to perform a finite, element-based, numerical model of a hydroxyapatite 3D printed bone scaffold. In the thirteenth paper, Talygin and Gorodkov present analytical expressions describing swirling jets for cardiovascular applications. In the fourteenth contribution, Schenkel and Halliday propose a novel non-Newtonian particle transport model for red blood cells. Finally, Zurita et al. propose a parametric numerical tool for analyzing a silicone customized 3D printable trachea-bronchial prosthesis