Development of a phantom and metal artifact correction (MAC) algorithm for post-operative spine computed tomography (CT) imaging / Noor Diyana Osman

Osman, Noor Diyana (2015) Development of a phantom and metal artifact correction (MAC) algorithm for post-operative spine computed tomography (CT) imaging / Noor Diyana Osman. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 7 (7). Institute of Graduate Studies, UiTM, Shah Alam.


The assessment of post-operative spine using computed tomography (CT) imaging is so challenging in the presence of metal artifacts induced by spinal implants. Metal artifacts can severely affect the quality of CT images, and mislead the diagnosis interpretation. The artifacts are due to the beam hardening, photon starvation effect, and inappropriate correction algorithm during image reconstruction. Knowledge on factors affecting the artifacts is crucially important to minimize these artifacts. The main aim of this study is to develop a novel technique for suppression of metal artifacts and this study generally consists three main parts. The first part is the development of a simple phantom useful for metal streaking analysis performed in the second part of this work. A customized phantom was constructed with tissue-equivalent materials and various metal inserts to simulate the streaking artifacts similar to the clinical findings in post-operative spine imaging. In this work, a thorough study on factors influencing the magnitude of metal streak artifacts is presented. There are 3 different variables studied which were metal insert characteristics, exposure factors, and reconstruction parameters. The phantom was scanned using Siemens Definition AS+ CT scanner in Radiology Department, HUSM. All phantom images were acquired using standard field of view (FOV) of 230mm with various acquisition and reconstruction protocols depending on the parameters studied. Standardized regions of interest (ROIs) were defined within the streaking region to obtain 1625 attenuation measurements in Hounsfield units (HU). All data are displayed as mean ± standard deviation (SD). The severity of metal streak in each image was determined by CT fluctuation, and noise at each ROI…


Item Type: Book Section
Email / ID Num.
Osman, Noor Diyana
Subjects: L Education > LB Theory and practice of education > Higher Education > Dissertations, Academic. Preparation of theses > Malaysia
Divisions: Universiti Teknologi MARA, Shah Alam > Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IPSis Biannual Publication
Volume: 7
Number: 7
Keywords: Abstract; Abstract of thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM; algorithm
Date: 2015
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