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

Desarrollo de una herramienta para la gestión de las órdenes de trabajo del servicio técnico de una empresa de equipamiento electromédico. Caso práctico: Electromedical, S.L

[ES] A lo largo de los años, las empresas del mundo han ido incorporando las nuevas tecnologías a la gestión de sus compañías. Con la llegada de los teléfonos inteligentes o smartphones al mercado, estos se han incluido como herramienta de trabajo, complementando a los ordenadores. Como consecuencia, muchas empresas han desarrollado software propio tanto para sus clientes, que pueden acceder rápidamente a los servicios sin tener que estar en casa, como para sus empleados, que pueden tener más movilidad y optimizar su trabajo. La estancia del alumno, haciendo prácticas de empresa, en una sociedad del sector biomédico llamada Electromedical S.L., ha propiciado el desarrollo de este Trabajo Fin de Grado (TFG). En este se ha desarrollado una herramienta de software para el sistema operativo Android, creada con el entorno MIT App Inventor, junto con una base de datos en la nube (tablas dinámicas de Google Drive) con la intención de que sean utilizadas por los empleados de la compañía para solucionar una problemática concreta: Electromedical S.L. es un distribuidor de equipos médicos que también ofrece servicio de asistencia técnica (SAT). Mientras que la distribución es llevada por el departamento de ventas el SAT es gestionado por el departamento técnico. Se pretende que la aplicación móvil creada se utilice como sustituto de unos formularios en papel llamados órdenes de trabajo. Estos papeles se rellenan para que el cliente y la empresa tengan constancia del servicio recibido y prestado, respectivamente. Además, por el tipo de equipos que la empresa distribuye (equipos de rayos X), esta debe presentar anualmente un informe al Consejo de Seguridad Nuclear (CSN) con un registro de todas las reparaciones llevadas a cabo. Por esta razón, es importante asegurar la integridad de la información. Con esta digitalización de las órdenes de trabajo y posterior subida de documentación a la nube se asegura que la empresa cumpla con sus obligaciones con el CSN, al evitar posibles pérdidas de información por mala cumplimentación de las órdenes o extravío de las mismas. Destacar que se les brinda a los técnicos la comodidad de no tener que llevar órdenes de trabajo impresas consigo, hecho importante si se tiene en cuenta que su trabajo incluye muchos desplazamientos. Adicionalmente, con este desarrollo se reduce ligeramente el consumo de papel por parte de la empresa, esto reduce sus gastos y contribuye al cuidado del medio ambiente.[CA] Al llarg dels anys les empreses del món han anat incorporant les noves tecnologies a la gestió de les seues companyies. Amb l’arribada dels telèfons intel·ligents o smartphones al mercat, aquests s’han inclòs com una eina de treball, complementant als ordinadors. Com a conseqüència, moltes empreses han desenvolupat software propi tant per als seus clients, que poden accedir ràpidament als serveis sense tindre que estar a casa, com per als seus empleats, que poden tindre més mobilitat i optimitzen el seu treball. La estància de l’alumne, fent pràctiques de empresa, a una societat del sector biomèdic anomenada Electromedical S.L., ha propiciat el desenvolupament d’aquest Treball de Final de Grau (TFG). En aquest, s’ha desenvolupat una eina de software per al sistema operatiu Android, creada amb l’entorn MIT App Inventor, junt a una base de dades al núvol (taules dinàmiques de Google Drive) amb la intenció de que siguen utilitzades per els empleats de la companyia y així solucionar una problemàtica concreta: Electromedical S.L. és un distribuïdor d’equips mèdics que també ofereix servici d’assistència tècnica (SAT). Mentre que la distribució està gestionada pel departament de vendes, el SAT està gestionat pel departament tècnic. Es pretén que l’aplicació mòbil creada s’utilitze com a substitut d’uns formularis en paper anomenats ordres de treball. Aquest papers s’omplin per a que el client y l’empresa tinguen constància del servei rebut i prestat, respectivament. A més, pel tipus d’equips que l’empresa distribueix (equips de rajos X), esta deu presentar anualment un informe al Consell de Seguretat Nuclear (CSN) amb un registre de totes les reparacions portades a terme. Per aquest motiu és important assegurar la integritat de la informació. Amb aquesta digitalització de les ordres de treball i posterior pujada de la informació al núvol, s’assegura que l’empresa compleix amb les seues obligacions amb el CSN, al evitar possibles pèrdues d’informació per un mal emplenat de les ordres o pèrdua d’aquestes. Destacar que es dona comoditat als tècnics, ja que no deuen portar ordres de treball impreses amb ells, fet important si es té en compte que el seu treball inclou molts desplaçaments. Addicionalment, amb aquest desenvolupament, es redueix lleugerament el consum de paper per part de la empresa, la qual cosa redueix les seues despeses i contribueix a cuidar el medi ambient.[EN] Companies in the world are incorporating new technologies to the management of their corporations. Smartphones are now a work tool that complements the computers. Consequently, many companies have developed their own software, for their own clients to allow them to get to their services without being at home, and for their employees too, to let them have more mobility and to optimize their job. The internship made by the student in a company related to the biomedical sector, called Electromedical S.L., has promoted the development of this Trabajo Fin de Grado (TFG). In this project, a software tool has been developed to the Android operating system, made with the environment of MIT App Inventor and with a database (dynamic tables of Google Drive) that has to be used by the employees of the company to solve a problem: Electromedical S.L. is a distributor of medical devices that offers technical assistance too (SAT). While the sales department handles the distribution, the technical department manages the SAT. The mobile application is aimed at substitute some forms called work orders. The technician must fill in these forms to make the client know what they have made. Besides, due to the devices that are distributed by the company, it must draw up a report to the Consejo de Seguridad Nuclear (CSN) specifying all the reparations they have made. That is why it is important to have clear information. Digitalizing the work orders and having the information in the cloud, ensures that the company fulfil their legal obligations with the CSN, because it is not possible to lose some information missing papers. Furthermore, it makes the technicians to make their job comfortably, because they do not have to bring printed work orders with them. This is especially important because their job includes many displacements. Additionally, with this new application, paper’s consume is reduced by the company, so it will reduce company’s expense and help to preserve the environment.Yago Ruiz, Á. (2016). Desarrollo de una herramienta para la gestión de las órdenes de trabajo del servicio técnico de una empresa de equipamiento electromédico. Caso práctico: Electromedical, S.L. http://hdl.handle.net/10251/67517.TFG

An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications

Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, which explicitly provides an estimate of the targets shapes and implicitly encodes information in their contrast values. In the second step, the images obtained in the previous step are fed into a neural network (U-Net), whose duty is retrieving the exact shape of the target and its contrast value. This task is cast as an image segmentation one, where each pixel is classified into a discrete set of permittivity values within a given range. The use of a reduced number of possible permittivities facilitates the training stage by limiting its scope. The approach was tested with synthetic data and validated with experimental data taken from the Fresnel database to allow a fair comparison with the literature. Finally, its potential for biomedical imaging is demonstrated with a numerical example related to microwave brain stroke diagnosis

An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications

Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, which explicitly provides an estimate of the targets shapes and implicitly encodes information in their contrast values. In the second step, the images obtained in the previous step are fed into a neural network (U-Net), whose duty is retrieving the exact shape of the target and its contrast value. This task is cast as an image segmentation one, where each pixel is classified into a discrete set of permittivity values within a given range. The use of a reduced number of possible permittivities facilitates the training stage by limiting its scope. The approach was tested with synthetic data and validated with experimental data taken from the Fresnel database to allow a fair comparison with the literature. Finally, its potential for biomedical imaging is demonstrated with a numerical example related to microwave brain stroke diagnosis