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T.I. van Velden
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Accurate knowledge of the conductivity and permittivity of tissue is vital in the diagnosis of many diseases. Magnetic resonance electrical property tomography (MR-EPT) reconstructs these using measurements of the electromagnetic fields inside the MR-scanner. These properties
re reconstructed using two-dimensional contrast source inversion, due to a significant complexity reduction in comparison to its three-dimensional counterpart, allowing for reconstructions in a reasonable amount of time. Data acquisition is usually performed using a shielded
irdcage coil. Current methods, however, disregard the presence of this shielding, only accounting for it using a rough first-order approximation, as properly accounting for it would lead to an intractable computational load. This thesis shows a method of analytically describing this
hielding in the Greens functions and its efficient implementation in the corresponding Greens operators for CSI-EPT, exploiting the Greens functions being degenerate. This results in a significant increase in the reconstruction performance both qualitatively and quantitatively, at the cost of a slight increase of the computational load. The inversion problem being ill-posed leads to poor reconstruction performance on noisy data. This thesis presents a method for quantization of the tissue parameters, by enforcing a multi-modal distribution for these parameters.
through simulation, the quantization method shows a significant increase in performance for noisy data, at the cost of losing smaller details in the image. This improved model was tested on synthetic E-polarized data, and realistic three-dimensional data, showing a more robust, and
accurate reconstruction of the electrical properties for both. ...
re reconstructed using two-dimensional contrast source inversion, due to a significant complexity reduction in comparison to its three-dimensional counterpart, allowing for reconstructions in a reasonable amount of time. Data acquisition is usually performed using a shielded
irdcage coil. Current methods, however, disregard the presence of this shielding, only accounting for it using a rough first-order approximation, as properly accounting for it would lead to an intractable computational load. This thesis shows a method of analytically describing this
hielding in the Greens functions and its efficient implementation in the corresponding Greens operators for CSI-EPT, exploiting the Greens functions being degenerate. This results in a significant increase in the reconstruction performance both qualitatively and quantitatively, at the cost of a slight increase of the computational load. The inversion problem being ill-posed leads to poor reconstruction performance on noisy data. This thesis presents a method for quantization of the tissue parameters, by enforcing a multi-modal distribution for these parameters.
through simulation, the quantization method shows a significant increase in performance for noisy data, at the cost of losing smaller details in the image. This improved model was tested on synthetic E-polarized data, and realistic three-dimensional data, showing a more robust, and
accurate reconstruction of the electrical properties for both. ...
Accurate knowledge of the conductivity and permittivity of tissue is vital in the diagnosis of many diseases. Magnetic resonance electrical property tomography (MR-EPT) reconstructs these using measurements of the electromagnetic fields inside the MR-scanner. These properties
re reconstructed using two-dimensional contrast source inversion, due to a significant complexity reduction in comparison to its three-dimensional counterpart, allowing for reconstructions in a reasonable amount of time. Data acquisition is usually performed using a shielded
irdcage coil. Current methods, however, disregard the presence of this shielding, only accounting for it using a rough first-order approximation, as properly accounting for it would lead to an intractable computational load. This thesis shows a method of analytically describing this
hielding in the Greens functions and its efficient implementation in the corresponding Greens operators for CSI-EPT, exploiting the Greens functions being degenerate. This results in a significant increase in the reconstruction performance both qualitatively and quantitatively, at the cost of a slight increase of the computational load. The inversion problem being ill-posed leads to poor reconstruction performance on noisy data. This thesis presents a method for quantization of the tissue parameters, by enforcing a multi-modal distribution for these parameters.
through simulation, the quantization method shows a significant increase in performance for noisy data, at the cost of losing smaller details in the image. This improved model was tested on synthetic E-polarized data, and realistic three-dimensional data, showing a more robust, and
accurate reconstruction of the electrical properties for both.
re reconstructed using two-dimensional contrast source inversion, due to a significant complexity reduction in comparison to its three-dimensional counterpart, allowing for reconstructions in a reasonable amount of time. Data acquisition is usually performed using a shielded
irdcage coil. Current methods, however, disregard the presence of this shielding, only accounting for it using a rough first-order approximation, as properly accounting for it would lead to an intractable computational load. This thesis shows a method of analytically describing this
hielding in the Greens functions and its efficient implementation in the corresponding Greens operators for CSI-EPT, exploiting the Greens functions being degenerate. This results in a significant increase in the reconstruction performance both qualitatively and quantitatively, at the cost of a slight increase of the computational load. The inversion problem being ill-posed leads to poor reconstruction performance on noisy data. This thesis presents a method for quantization of the tissue parameters, by enforcing a multi-modal distribution for these parameters.
through simulation, the quantization method shows a significant increase in performance for noisy data, at the cost of losing smaller details in the image. This improved model was tested on synthetic E-polarized data, and realistic three-dimensional data, showing a more robust, and
accurate reconstruction of the electrical properties for both.
Optical Calibration System
Subproject within the ADome project
Bachelor thesis
(2021)
-
D. Roos, T.I. van Velden, M. Spirito, F.A. Musters, R.A. Coesoij, L.C.N. de Vreede, M. Alonso Del Pino
In the ADome project, a truncated icosahedron is used as a frame for multiple antenna nodes. These nodes are used to measure the radiation pattern of an antenna-under-test located in the center of this dome. Since these antennas can be placed anywhere in the ADome by the user, a calibration system is necessary to find the spherical coordinates of each of these antennas.
In this thesis, a method is proposed that uses computer vision algorithms, written using the OpenCV library in C++, to locate each of these antennas by detecting controllable LEDs attached to the PCBs of the antenna nodes. The found pixel locations will then be used to get the spherical coordinates of the antennas with respect to the camera. Finally these spherical coordinates will be transformed to fit to the coordinate system of the ADome itself, by detecting landmarks in the form of fiducial markers which are located at predetermined locations.
The methods of recognition of the antennas within using computer vision are discussed, implemented and tested on real and simulated data. The accuracy of finding the antennas on the real data was within 2 pixels from the true location.
The methods regarding estimating the location of the antenna with respect to the camera are discussed next. These methods include a distortion estimation and correction, after which each pixel will get there corresponding spherical angles θ and φ. These methods are also tested on simulated and real data, where the accuracy on the real data falls within 0.5 degrees in comparison to the true data.
Finally the methods of transforming the spherical angle θ and φ of the antenna with respect to the camera, to the coordinate system of the ADome are discussed. This starts with a method of recognizing fiducial landmarks (landmarks are accurately known reference points), where in this research there is chosen for ArUco fiducials. After this, the location of these landmarks can be used to determine the location and orientation of the camera within the ADome. The method of finding the distances between each of the landmarks and the camera, is discussed and tested and has an accuracy of 2 cm. The method of finding the location of the camera, and the method of finding the orientation of the camera are discussed, however this has not been tested and fully implemented yet. With this location and orientation the location of an antenna can be easily determined.
In conclusion, this thesis is a good starting point for designing an Optical Calibration System, which could make determining the location of each of the antennas faster, and more accurate. ...
In this thesis, a method is proposed that uses computer vision algorithms, written using the OpenCV library in C++, to locate each of these antennas by detecting controllable LEDs attached to the PCBs of the antenna nodes. The found pixel locations will then be used to get the spherical coordinates of the antennas with respect to the camera. Finally these spherical coordinates will be transformed to fit to the coordinate system of the ADome itself, by detecting landmarks in the form of fiducial markers which are located at predetermined locations.
The methods of recognition of the antennas within using computer vision are discussed, implemented and tested on real and simulated data. The accuracy of finding the antennas on the real data was within 2 pixels from the true location.
The methods regarding estimating the location of the antenna with respect to the camera are discussed next. These methods include a distortion estimation and correction, after which each pixel will get there corresponding spherical angles θ and φ. These methods are also tested on simulated and real data, where the accuracy on the real data falls within 0.5 degrees in comparison to the true data.
Finally the methods of transforming the spherical angle θ and φ of the antenna with respect to the camera, to the coordinate system of the ADome are discussed. This starts with a method of recognizing fiducial landmarks (landmarks are accurately known reference points), where in this research there is chosen for ArUco fiducials. After this, the location of these landmarks can be used to determine the location and orientation of the camera within the ADome. The method of finding the distances between each of the landmarks and the camera, is discussed and tested and has an accuracy of 2 cm. The method of finding the location of the camera, and the method of finding the orientation of the camera are discussed, however this has not been tested and fully implemented yet. With this location and orientation the location of an antenna can be easily determined.
In conclusion, this thesis is a good starting point for designing an Optical Calibration System, which could make determining the location of each of the antennas faster, and more accurate. ...
In the ADome project, a truncated icosahedron is used as a frame for multiple antenna nodes. These nodes are used to measure the radiation pattern of an antenna-under-test located in the center of this dome. Since these antennas can be placed anywhere in the ADome by the user, a calibration system is necessary to find the spherical coordinates of each of these antennas.
In this thesis, a method is proposed that uses computer vision algorithms, written using the OpenCV library in C++, to locate each of these antennas by detecting controllable LEDs attached to the PCBs of the antenna nodes. The found pixel locations will then be used to get the spherical coordinates of the antennas with respect to the camera. Finally these spherical coordinates will be transformed to fit to the coordinate system of the ADome itself, by detecting landmarks in the form of fiducial markers which are located at predetermined locations.
The methods of recognition of the antennas within using computer vision are discussed, implemented and tested on real and simulated data. The accuracy of finding the antennas on the real data was within 2 pixels from the true location.
The methods regarding estimating the location of the antenna with respect to the camera are discussed next. These methods include a distortion estimation and correction, after which each pixel will get there corresponding spherical angles θ and φ. These methods are also tested on simulated and real data, where the accuracy on the real data falls within 0.5 degrees in comparison to the true data.
Finally the methods of transforming the spherical angle θ and φ of the antenna with respect to the camera, to the coordinate system of the ADome are discussed. This starts with a method of recognizing fiducial landmarks (landmarks are accurately known reference points), where in this research there is chosen for ArUco fiducials. After this, the location of these landmarks can be used to determine the location and orientation of the camera within the ADome. The method of finding the distances between each of the landmarks and the camera, is discussed and tested and has an accuracy of 2 cm. The method of finding the location of the camera, and the method of finding the orientation of the camera are discussed, however this has not been tested and fully implemented yet. With this location and orientation the location of an antenna can be easily determined.
In conclusion, this thesis is a good starting point for designing an Optical Calibration System, which could make determining the location of each of the antennas faster, and more accurate.
In this thesis, a method is proposed that uses computer vision algorithms, written using the OpenCV library in C++, to locate each of these antennas by detecting controllable LEDs attached to the PCBs of the antenna nodes. The found pixel locations will then be used to get the spherical coordinates of the antennas with respect to the camera. Finally these spherical coordinates will be transformed to fit to the coordinate system of the ADome itself, by detecting landmarks in the form of fiducial markers which are located at predetermined locations.
The methods of recognition of the antennas within using computer vision are discussed, implemented and tested on real and simulated data. The accuracy of finding the antennas on the real data was within 2 pixels from the true location.
The methods regarding estimating the location of the antenna with respect to the camera are discussed next. These methods include a distortion estimation and correction, after which each pixel will get there corresponding spherical angles θ and φ. These methods are also tested on simulated and real data, where the accuracy on the real data falls within 0.5 degrees in comparison to the true data.
Finally the methods of transforming the spherical angle θ and φ of the antenna with respect to the camera, to the coordinate system of the ADome are discussed. This starts with a method of recognizing fiducial landmarks (landmarks are accurately known reference points), where in this research there is chosen for ArUco fiducials. After this, the location of these landmarks can be used to determine the location and orientation of the camera within the ADome. The method of finding the distances between each of the landmarks and the camera, is discussed and tested and has an accuracy of 2 cm. The method of finding the location of the camera, and the method of finding the orientation of the camera are discussed, however this has not been tested and fully implemented yet. With this location and orientation the location of an antenna can be easily determined.
In conclusion, this thesis is a good starting point for designing an Optical Calibration System, which could make determining the location of each of the antennas faster, and more accurate.