Department of Medical Imaging and Clinical Oncology

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Browsing Department of Medical Imaging and Clinical Oncology by browse.metadata.advisor "Doruyter, Alexander"

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    The value of different reconstruction algorithms for quantification of FDG PET brain imaging
    (Stellenbosch : Stellenbosch University, 2016-12) Moalosi, Tumelo Carel Godwin; Ellmann, Annare; Mix, Michael; Warwick, James; Du Toit, Monique; Doruyter, Alexander; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Medical Imaging. Medical Physics.
    ENGLISH SUMMARY : Modern reconstruction techniques of positron emission tomography/computed tomography (PET/CT) data are optimized for whole body imaging. Such optimization is less developed for brain imaging. This study aimed at investigating the effect of different image reconstruction parameters (varying number of iterations, scan duration, relaxation parameter (smoothing levels) and the use of time of flight (TOF)) on PET/CT images with the objective of evaluating the algorithms for quantification of fluorodeoxyglucose (FDG) PET brain imaging. Materials and methods: A Philips Gemini TF Big Bore PET/CT scanner was used for acquiring the data. The study was based primarily on phantom and limited patient data for preliminary validation. Three dimensional (3D) Hoffman brain phantom (HBP) data and data of patients attending the Western Cape Academic PET/CT Centre for oncological purposes, with low probability of neurological pathology, were included in the study. The data was reconstructed using two different iterative reconstruction algorithms, row action maximum likelihood algorithm (RAMLA) and spherically symmetric basis function ordered subset algorithm (BLOB or BLOB OS), with variation in the number of iterations, scan acquisition duration, switching TOF on and off for BLOB OS and by varying the relaxation parameter. The set of output images were analyzed using MATLAB code. Results: From the HBP data, in all regions of the brain, the grey matter/white matter ratio, and the mean and the normalized mean counts increased as the number of iterations increased, reaching a plateau after 15 iterations for all algorithms. When comparing the algorithms with relaxation values λ=0.7 and λ=1.0, it was found that the latter converged faster. Overall, BLOB TOF (λ=1.0) proved to have faster convergence followed by BLOB TOF (λ=0.7). The coefficient of variation (COV) for all volumes of interest showed BLOB TOF to be superior compared to all the other algorithms. The COV results for different scan durations showed that there is minimal improvement after 5 min in high-activity regions (GM) and after 10 min in low-activity region (WM). The patient data was used as proof of principle but the numbers were too small to analyze further, as no pattern of behaviour could be identified for the different algorithms in the three patient images available. Conclusions: A higher number of iterations, such as 15, than currently used by the vendor of the PET scanner led to improved image quality for all algorithms. An acquisition time of 10 min provided an optimal trade-off between image quality and scan time irrespective of the reconstruction algorithm used. Including the TOF in the reconstruction algorithm improved the image quality, proving that TOF also improves image quality for small objects such as the brain similar to that seen for larger anatomical diameters as indicated in the literature.