Affiliations: [a] Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand | [b] Department of Control Systems and Instrumentation Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand | [c] X-ray CT and Medical Imaging Laboratory, Biomedical Electrnics and Systems Development Unit, National Electronics and Computer Technology Center, Pathumthani, Thailand | [d] Faculty of Radiological Technology, Rangsit University, Pathumthani, Thailand | [e] Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
Correspondence:
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Corresponding author: Titipong Kaewlek, Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand. E-mail:titipongk@nu.ac.th
Abstract: Metal artifacts often appear in the images of computed
tomography (CT) imaging. In the case of lumbar spine CT images, artifacts
disturb the images of critical organs. These artifacts can affect the
diagnosis, treatment, and follow up care of the patient. One approach to
metal artifact reduction is the sinogram completion method. A mixed-variable
thresholding (MixVT) technique to identify the suitable metal sinogram is
proposed. This technique consists of four steps: 1) identify the metal
objects in the image by using k-mean clustering with the soft cluster
assignment, 2) transform the image by separating it into two sinograms, one
of which is the sinogram of the metal object, with the surrounding tissue
shown in the second sinogram. The boundary of the metal sinogram is then
found by the MixVT technique, 3) estimate the new value of the missing data
in the metal sinogram by linear interpolation from the surrounding tissue
sinogram, 4) reconstruct a modified sinogram by using filtered
back-projection and complete the image by adding back the image of the metal
object into the reconstructed image to form the complete image. The
quantitative and clinical image quality evaluation of our proposed technique
demonstrated a significant improvement in image clarity and detail, which
enhances the effectiveness of diagnosis and treatment.
Keywords: Metal artifacts reduction, image quality evaluation, lumbar spine image, metal sinogram, segmentation
