Assessment of metal artifact reduction methods in pelvic CT. Issue 4 (3rd March 2016)

• Record Type: Journal Article
• Authors: Abdoli, Mehrsima; Mehranian, Abolfazl; Ailianou, Angeliki; Becker, Minerva; Zaidi, Habib
• Is Part Of: Medical physics. Volume 43:Issue 4(2016)
• Page Start: 1588
• Page End: 1597
• Publication Date: 2016-03-03
• Subjects: computerised tomography -- interpolation -- prosthetics; Biomedical engineering -- Computed tomography -- Interpolation; curve fitting; Computerised tomographs -- Prostheses implantable into the body -- Biological material, e.g. blood, urine; Haemocytometers; pelvic CT -- metal artifacts -- metal artifact reduction -- comparison -- simulation; Computed tomography -- Medical image artifacts -- Interpolation -- Medical image reconstruction -- Medical image segmentation -- Medical X‐ray imaging -- Tissues; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4942810
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Metal artifact reduction (MAR) produces images with improved quality potentially leading to confident and reliable clinical diagnosis and therapy planning. In this work, the authors evaluate the performance of five MAR techniques for the assessment of computed tomography images of patients with hip prostheses. Methods: Five MAR algorithms were evaluated using simulation and clinical studies. The algorithms included one‐dimensional linear interpolation (LI) of the corrupted projection bins in the sinogram, two‐dimensional interpolation (2D), a normalized metal artifact reduction (NMAR) technique, a metal deletion technique, and a maximum a posteriori completion (MAPC) approach. The algorithms were applied to ten simulated datasets as well as 30 clinical studies of patients with metallic hip implants. Qualitative evaluations were performed by two blinded experienced radiologists who ranked overall artifact severity and pelvic organ recognition for each algorithm by assigning scores from zero to five (zero indicating totally obscured organs with no structures identifiable and five indicating recognition with high confidence). Results: Simulation studies revealed that 2D, NMAR, and MAPC techniques performed almost equally well in all regions. LI falls behind the other approaches in terms of reducing dark streaking artifacts as well as preserving unaffected regions ( p < 0.05). Visual assessment of clinical datasets revealed the superiority of NMAR and MAPC in the evaluated pelvic organs and in terms of overall image quality. Conclusions: Overall, all methods, except LI, performed equally well in artifact‐free regions. Considering both clinical and simulation studies, 2D, NMAR, and MAPC seem to outperform the other techniques.