Phase-based conductivity mapping using MRI data contains an assumption of locally constant complex permittivity and use of a differential operator which result in significant inaccuracies at tissue...Show morePhase-based conductivity mapping using MRI data contains an assumption of locally constant complex permittivity and use of a differential operator which result in significant inaccuracies at tissue boundaries and amplification of noise in data. This work focuses on the implementation of an iterative model-based nonlinear optimization algorithm that aims to surpass these rising inaccuracies. The algorithm is designed to optimize conductivity maps using phase data acquired from MRI. In addition to optimization, the algorithm focuses on regularization which further improves the optimized outcome of the conductivity maps. Successful results are demonstrated using both simulated as well as phantom data. The comparison between results of a conventional phase-based conductivity mapping and the iterative algorithm shows improved accuracy for the latter. In addition, the model-based algorithm possesses potential for reduced acquisition time as it is capable of reconstructing accurate conductivity maps with relatively low SNR. In the future, experiments on in-vivo data can be performed. Additionally, to improve the accuracy of the conductivity maps even further, implementation of optimal methods to determine regularization parameters and regularization functions is possible.Show less
Electrical properties tomography (ETP) enables find the electrical properties of tissues in a non-invasive method by using the magnetic fields of an MRI scanner. EPT can be used in clinic to assess...Show moreElectrical properties tomography (ETP) enables find the electrical properties of tissues in a non-invasive method by using the magnetic fields of an MRI scanner. EPT can be used in clinic to assess lesions in the breast. Still, there is a lot of variance between inter-subject conductivity values. In this project, we studied the effect of SNR, which we varied by receiving the signal with different coils. The effect of the pulse sequence was investigated by scanning a phantom and a healthy volunteer with a 3D turbo spin echo (TSE), a 3D balanced steady state free precession (bSSFP) and additionally the healthy volunteer was scanned with a 2D TSE. Lastly, we studied the effect of noise-smoothing operations by pre-processing the data with a Gaussian filter with a standard deviation of 2 pixels and post-processing with a 5x5 pixel median filter. We found that the noise does not affect the mean value of the conductivity. Also, the noise can be smoothed with the operations described above. Artifacts caused by acquisition does influence the values that we obtained. From the sequences that were used, we found that the 2D TSE resulted in the best conductivity map. In the future, we hope to differentiate benign from malignant tumors in breast data by applying this sequence in the clinic, such that histology of the tumoral tissue can be abolished.Show less
In this thesis we investigate conductivity changes due to magnetite in agarose gels mimicking grey brain matter. We use conventional MRI sequences to acquire B + 1 phase maps. Using the homogeneous...Show moreIn this thesis we investigate conductivity changes due to magnetite in agarose gels mimicking grey brain matter. We use conventional MRI sequences to acquire B + 1 phase maps. Using the homogeneous Helmholtz equation and the B + 1 phase-only approximation, we reconstruct conductivity maps. The current sensitivity of the reconstructions is too low to detect conductivity changes due to magnetite nanoparticles in the concentration found in the brain of Alzheimer’s disease patients. Nevertheless, we have promising indications that we have been able to observe a change in the standard deviation of the conductivity due to the presence of magnetite.Show less
In this study, we employed several methods to characterize iron-oxide nanoparticles using SQUID magnetometry and MRI. With SQUID magnetometry, we measured the Isothermal Remanent Magnetization of C...Show moreIn this study, we employed several methods to characterize iron-oxide nanoparticles using SQUID magnetometry and MRI. With SQUID magnetometry, we measured the Isothermal Remanent Magnetization of C. Elegans and two human brain samples. We obtained the iron concentration from the fit. We were able to detect changes in iron concentration due to mutations in C. Elegans. For the MRI measurements, we used Quantitative Susceptibility Mapping and an Off-Resonance Saturation method for brain phantoms. These phantoms consist of different concentrations of magnetite or ferritin dissolved in an agarose gel and mimics the human brain. With QSM we observed a comparable slope of the susceptibility/µg iron/ml. For the ORS method, a good agreement is found between the obtained iron concentration and the pre-determined iron concentration in the sample.Show less
