Doctoral Degrees (Medical Physics)
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Browsing Doctoral Degrees (Medical Physics) by Author "Van Eeden, Déte"
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Item Open Access Feasibility of tissue differentiation with multi-energy computed tomography: a Monte Carlo breast phantom study(University of the Free State, 2017-03) Van Eeden, Déte; Du Plessis, F. C. P.English: Dedicated breast CT is a new innovative way of imaging breast tissue without the limitations of overlapping anatomical features. It has been shown that the dose received by the patient is comparable to that of conventional mammography techniques. Further developments have led to the idea of a photon-counting detector that can be utilised in conjunction with breast CT. This will produce images with higher CNRs and will improve the detection of malignant masses. Other applications of multi-energy CT include image-based energy weighting and the differentiation of different tissues. The aim of this study was to explore the feasibility for tissue differentiation in breast tissue through the Monte Carlo simulation of a virtual multi-energy CT unit. The EGSnrc Monte Carlo code was used to simulate a virtual CT unit, similar to the Toshiba Aquillion LB 16 CT. The radiation source modelling code, BEAMnrc, was used to model the different components of the virtual CT. These components include the X-ray tube, suitable filters and beam-defining components such as collimators. A phase-space file was obtained consisting of all the particles generated by the different components. The energy spectrum of the Toshiba Aquillion LB 16 CT was approximated by the virtual CT using HVL measurements. The RMI electron density CT phantom was used to benchmark the virtual CT against the Toshiba Aquillion LB 16 CT. The phantom consists of several inserts with known electron densities that produce different CT numbers. A similar phantom was modelled with an in-house developed IDL program and used for the simulations. The reconstructed images were then used for the benchmarking of the HUs. This benchmarking ensured that the method used in this study produces a realistic model of a CT unit. Breast simulator software was used to model three breast phantoms consisting of different glandularities. The composition of the different breast tissues was taken from literature. The three phantoms were simulated at 20 keV up to 65 keV in 5 keV increments. All of the image reconstructions in this study was done with a filtered backprojection algorithm by using the OSCar reconstruction software. The CNRs of the different images obtained at different energies were assessed. Image-based energy weighting was investigated to further enhance the CNRs of the images by multiplying each energy bin with a specific weighting factor. The weighting factors were determined by a random number generator in an in-house developed IDL code. Good results were obtained with a 1.2-1.3 fold increase in the CNR. Further improvements were made by applying constraints to the weighting factors of the different energy bins. A new method was proposed to differentiate between different breast tissues by using the mass attenuation information from multiple energies. This technique showed promising results and can detect malignant tissue by using a single egs_cbct simulation. In conclusion, it is feasible to differentiate between different breast tissue types when using a multiple-energy CT unit. Better CNRs are obtained when utilising the information of the entire energy spectrum. This will lead to better tumour detection, even in dense breasts consisting of 89% glandular tissue.