Periodic Reporting for period 1 - COLONINFO (Improving COLOrectal cancer screening: Novel INverse and FOrward algorithms for a new real-time microwave endoscopy)
Período documentado: 2018-07-01 hasta 2020-06-30
Colorectal cancer (CRC) is the third leading cause of cancer-related deaths when women and men are combined. The most likely screening detection technique is the endoscopy, but its reduced optical field of vision (<180º) can miss some polyps, for example due to angulations of the colon. To overcome this limitation, the host invented a low-cost hardware pre-prototype aiming to combine colonoscopy with microwave imaging (MWI). The physical basis of MWI is the dielectric contrast of the different tissues. These properties involve the relative permittivity and the conductivity, and their differences, are mainly related to water content. Malignant tissues have a higher rate of metabolism and more blood content, resulting in a higher dielectric contrast and electromagnetic (EM) scatter.
But, in addition to equipment, medical in-body MWI needs new software that allow highlighting of tissue differentiation and precise cancer detection. However, the foundations of the software require high skills in mathematics, computer science and physics. This project aimed to develop a reconstruction software able to detect CRC in a 360º field of view, and to provide techniques studying quantification, while reducing the computational burden.
The most successful MWI applications (breast cancer and stroke monitoring) locate many microwave antennas surrounding the imaging region at a specific distance and in a non-reflection medium. Instead, inside the colon, the number of antennas is restricted by the small organ’s sizes, the medium has reflections, the antenna’s location is unknown, and new geometrical configurations are involved. These changes restrict or inability the use of existing algorithms for other MWI reconstruction.
In this project, first a software based on a tomographic EM model was developed to simulate in-silico data. Then, the best developed detection software was tested over more than two thousand MWI in-silico datasets, to optimize the controllable parameters through a sensibility analysis. Finally, quantitative linear problems were developed, and an algorithm combining an iterative solver with an algorithm commonly used in compressed sensing (for problems dealing with few data) was adapted to reconstruct the quantitative MWI.
The detection software was able to detect the angular location of the CRC tissue, with a negligible angular error below 22.5º (smaller than the separation between the antennas). The quantification software showed its performance dealing with in-body scenarios such as the colon one, but for tissues with higher percentage (around 200%) of relative permittivity differences between malignant and healthy tissues. The project served to transfer software to a new spin-off aiming to commercialize the pre-prototype.
But, in addition to equipment, medical in-body MWI needs new software that allow highlighting of tissue differentiation and precise cancer detection. However, the foundations of the software require high skills in mathematics, computer science and physics. This project aimed to develop a reconstruction software able to detect CRC in a 360º field of view, and to provide techniques studying quantification, while reducing the computational burden.
The most successful MWI applications (breast cancer and stroke monitoring) locate many microwave antennas surrounding the imaging region at a specific distance and in a non-reflection medium. Instead, inside the colon, the number of antennas is restricted by the small organ’s sizes, the medium has reflections, the antenna’s location is unknown, and new geometrical configurations are involved. These changes restrict or inability the use of existing algorithms for other MWI reconstruction.
In this project, first a software based on a tomographic EM model was developed to simulate in-silico data. Then, the best developed detection software was tested over more than two thousand MWI in-silico datasets, to optimize the controllable parameters through a sensibility analysis. Finally, quantitative linear problems were developed, and an algorithm combining an iterative solver with an algorithm commonly used in compressed sensing (for problems dealing with few data) was adapted to reconstruct the quantitative MWI.
The detection software was able to detect the angular location of the CRC tissue, with a negligible angular error below 22.5º (smaller than the separation between the antennas). The quantification software showed its performance dealing with in-body scenarios such as the colon one, but for tissues with higher percentage (around 200%) of relative permittivity differences between malignant and healthy tissues. The project served to transfer software to a new spin-off aiming to commercialize the pre-prototype.
Simulators are usually employed to optimize controllable parameters of medical devices and its software. Through the project there was the need of data for testing the MWI reconstruction algorithms, and to optimize controllable parameters such as the frequency or the configuration of the transmitter-receivers antennas needed during the CRC detection phase. In EMs, the needed frequency is highly related with the distance between the probes and the imaging region. However, this is unknown in the endoscopic applications, although the organ’s sizes are limited. Then, this needed to be studied.
This project started by developing a simulator. Simulators are usually coded in 2D scenarios to considerably decrease the computational burden resulted of considering many hardware components. Therefore, a tomographic model was developed. The model propagates the EM microwave in the (2D) coronal slice of a 3D colon scenario, introducing a plane of symmetry in the field distribution. This plane of symmetry is created by using the approximation of the transverse magnetic field with respect to the gastro-intestinal tract, and supposing that the wave can not be spread outside of the field of view by imposing numerical boundary conditions.
Once the simulator was developed, different simulations with non-controllable parameters of the scenario and controllable ones of the MWI reconstruction were studied. This study highlighted some inherent limitations to confront our algorithms, driving part of their mathematical and physics foundations. Afterwards, a review of MWI reconstruction methods, was done to develop the first algorithms to deal with the necessary requirements.
Two detection software were developed. The second one indicated that a common continuous sequential selection of different pairs of transmitter and receiver antennas to perform the transmission of the microwaves and the detection of the scatters, can miss polyps. Mathematical tools were used to solve it. The resultant software was tested with more than two thousand different in-silico data. It detected the malignant tissues with a negligible error below 22.5º (smaller than the separation between the antennas).
Similarly, two quantification linear models were developed. The second one, reducing the unknowns respect to the first one, resulted still very unstable (meaning that noise or artifact in the data impacted negatively on the solution). To stabilize it and reduce the inherent uncertainties of the problem, an algorithm previously used in other biomedical setting was adapted and tested. The latter deals with systems with few measurement data to reconstruct many spatio-temporal unknowns of a sparse image (a goal shared here). This allowed to provide the first quantification images for in-body systems with a few antennas circularly distributed and enclosed by the imaging region.
Then, we confronted it to tissues with different percentage changes between relative permittivity of malignant and healthy tissues. The algorithm differentiated tissues with 200% percentage differences (% equivalent to breast cancer) but had difficulties to deal with lower percentage differences.
This project started by developing a simulator. Simulators are usually coded in 2D scenarios to considerably decrease the computational burden resulted of considering many hardware components. Therefore, a tomographic model was developed. The model propagates the EM microwave in the (2D) coronal slice of a 3D colon scenario, introducing a plane of symmetry in the field distribution. This plane of symmetry is created by using the approximation of the transverse magnetic field with respect to the gastro-intestinal tract, and supposing that the wave can not be spread outside of the field of view by imposing numerical boundary conditions.
Once the simulator was developed, different simulations with non-controllable parameters of the scenario and controllable ones of the MWI reconstruction were studied. This study highlighted some inherent limitations to confront our algorithms, driving part of their mathematical and physics foundations. Afterwards, a review of MWI reconstruction methods, was done to develop the first algorithms to deal with the necessary requirements.
Two detection software were developed. The second one indicated that a common continuous sequential selection of different pairs of transmitter and receiver antennas to perform the transmission of the microwaves and the detection of the scatters, can miss polyps. Mathematical tools were used to solve it. The resultant software was tested with more than two thousand different in-silico data. It detected the malignant tissues with a negligible error below 22.5º (smaller than the separation between the antennas).
Similarly, two quantification linear models were developed. The second one, reducing the unknowns respect to the first one, resulted still very unstable (meaning that noise or artifact in the data impacted negatively on the solution). To stabilize it and reduce the inherent uncertainties of the problem, an algorithm previously used in other biomedical setting was adapted and tested. The latter deals with systems with few measurement data to reconstruct many spatio-temporal unknowns of a sparse image (a goal shared here). This allowed to provide the first quantification images for in-body systems with a few antennas circularly distributed and enclosed by the imaging region.
Then, we confronted it to tissues with different percentage changes between relative permittivity of malignant and healthy tissues. The algorithm differentiated tissues with 200% percentage differences (% equivalent to breast cancer) but had difficulties to deal with lower percentage differences.
This project provided the foundations of the necessary qualitative and quantitative software of in-body applications with probes circularly distributed and enclosed by the imaging region. The objective was to provide the software to a new pre-prototype for CRC detection, and it was transferred to a new spin-off aiming to commercialize the device. This can have a first socio-economic impact.
However, the developed software goes one step beyond since can provide the foundations for other MWI applications. In terms of medical applications, the MWI is an emerging tool due to its low cost. The first microwave-powered ultrasounds are introduced, and there are several ongoing pre-clinical studies for monitoring liver ablation, myocardial infarction, bone imaging, or symptomatic uterine fibroids. In addition of the medical applications, MWI is also employed to geophysics, to satellite applications, to detect explosives, or to identify defects in non-metallic materials.
Therefore, the algorithms developed here could be extended to diverse fields such as detecting uterine cancer or defects in non-metallic narrow pipes, or even to reduce the number of antennas of other applications.
Sharing the simulator and publishing high impact manuscripts could have a broader impact through research and innovation.
Additionally, this project had also a socio impact through different scientific and gender dimension communications in primary and secondary schools, as well as media.
However, the developed software goes one step beyond since can provide the foundations for other MWI applications. In terms of medical applications, the MWI is an emerging tool due to its low cost. The first microwave-powered ultrasounds are introduced, and there are several ongoing pre-clinical studies for monitoring liver ablation, myocardial infarction, bone imaging, or symptomatic uterine fibroids. In addition of the medical applications, MWI is also employed to geophysics, to satellite applications, to detect explosives, or to identify defects in non-metallic materials.
Therefore, the algorithms developed here could be extended to diverse fields such as detecting uterine cancer or defects in non-metallic narrow pipes, or even to reduce the number of antennas of other applications.
Sharing the simulator and publishing high impact manuscripts could have a broader impact through research and innovation.
Additionally, this project had also a socio impact through different scientific and gender dimension communications in primary and secondary schools, as well as media.