DM
D.N. Moens
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By implementing cone-beam computed tomography (CBCT) into proton therapy radiationunits, a predetermined treatment plan could be updated prior to each treatment fraction ac-cording to the changing anatomy of the patient for better dose distribution. However, CBCTdoes not produce high enough image quality compared to fan-beam CT (FBCT), which nowa-days is used to build a treatment plan based on the stopping power ratio (SPR) of the objectivetissue in the body. Spectral CBCT is a promising method to potentially increase the imagequality of conventional CBCT. A provided joint reconstruction spectral CBCT algorithm inMATLAB is used to determine whether the low image quality of CBCT can be improved, as jointreconstruction algorithms have been proven to improve image quality for FBCT in practicalexperiments. The provided code is converted to Python, after which equivalent results areensured using comparative analysis. A phantom with multiple biological materials is thenimplemented in this acquired Python code to investigate the quality of the reconstruction im-ages. Moreover, SPR maps and a VMI are constructed from these images and their qualitydetermined.The results show that for 10 to 12 iterations, the used reconstruction provides the reconstructedimages with the lowest mean squared error (MSE). For higher iterations, the image becomesoversmoothed and loses quality. In future research, the used joint reconstruction algorithmshould be compared to non-joint reconstruction algorithms to investigate the impact of thistechnique on the image quality, after which it could be applied to more realistic data.
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By implementing cone-beam computed tomography (CBCT) into proton therapy radiationunits, a predetermined treatment plan could be updated prior to each treatment fraction ac-cording to the changing anatomy of the patient for better dose distribution. However, CBCTdoes not produce high enough image quality compared to fan-beam CT (FBCT), which nowa-days is used to build a treatment plan based on the stopping power ratio (SPR) of the objectivetissue in the body. Spectral CBCT is a promising method to potentially increase the imagequality of conventional CBCT. A provided joint reconstruction spectral CBCT algorithm inMATLAB is used to determine whether the low image quality of CBCT can be improved, as jointreconstruction algorithms have been proven to improve image quality for FBCT in practicalexperiments. The provided code is converted to Python, after which equivalent results areensured using comparative analysis. A phantom with multiple biological materials is thenimplemented in this acquired Python code to investigate the quality of the reconstruction im-ages. Moreover, SPR maps and a VMI are constructed from these images and their qualitydetermined.The results show that for 10 to 12 iterations, the used reconstruction provides the reconstructedimages with the lowest mean squared error (MSE). For higher iterations, the image becomesoversmoothed and loses quality. In future research, the used joint reconstruction algorithmshould be compared to non-joint reconstruction algorithms to investigate the impact of thistechnique on the image quality, after which it could be applied to more realistic data.