Comparative Methods for Metal Artifact Reduction in x-ray CT

被引:0
|
作者
Abdoli, Mehrsima [1 ]
Mehranian, Abolfazl [2 ]
Ailianou, Angeliki [2 ]
Becker, Minerva [2 ]
Zaidi, Habib [2 ,3 ]
机构
[1] Netherlands Canc Inst, Dept Radiat Oncol, Amsterdam, Netherlands
[2] Univ Hosp Geneva, Div Nucl Med & Mol Imaging, Geneva, Switzerland
[3] Univ Groningen, Univ Med Ctr Groningen, Dept Nucl Med & Mol Imaging, Groningen, Netherlands
基金
瑞士国家科学基金会;
关键词
ATTENUATION CORRECTION; COMPUTED-TOMOGRAPHY; ALGORITHM; IMPLANTS; INTERPOLATION; SINOGRAMS;
D O I
暂无
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
To assess the performance of five metal artefact reduction (MAR) techniques for the assessment of computed tomography (CT) images of patients with hip prostheses. Five MAR algorithms were evaluated using simulation and clinical studies. The algorithms included one-dimensional linear interpolation (LI) of the corrupted projections in the sinogram, two-dimensional interpolation (2D), a normalized metal artefact reduction (NMAR) technique, a metal deletion technique (MDT), and a 3D prior image constrained projection completion approach (MAPC). The algorithms were applied to 10 simulated datasets as well as30 clinical studies of patients with metallic hip implants. Qualitative evaluations were performed by two blinded, experienced radiologists, who ranked overall artefact severity, as well as pelvic organ recognition for each algorithm, respectively. The simulated studies revealed that 2D, NMAR and MAPC techniques performed almost equally well in regions with dark streaking artefacts. However, in regions with bright streaking artefacts, LI outperformed the other techniques (p < 0.05). Visual assessment of clinical datasets confirmed the superiority of NMAR and MAPC in the evaluated pelvic organs and in terms of overall image quality. Overall, all methods performed equally well in artefact-free regions. However, NMAR and MAPC outperformed the other techniques in regions affected by artefacts.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] A Feasibility Study On Reducing Metal Artifact in CT Using Collimation at the X-Ray Source
    Schurr, R.
    Morrow, A.
    MEDICAL PHYSICS, 2017, 44 (06)
  • [32] Metal artifact reduction in x-ray computed tomography: inpainting versus missing value
    Heil, Ulrich
    Gross, Daniel
    Schulze, Ralf
    Schwanecke, Ulrich
    Schoemer, Elmar
    2011 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2011, : 2675 - 2677
  • [33] Prior-Guided Metal Artifact Reduction for Iterative X-Ray Computed Tomography
    Chang, Zhiqian
    Ye, Dong Hye
    Srivastava, Somesh
    Thibault, Jean-Baptiste
    Sauer, Ken
    Bouman, Charles
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2019, 38 (06) : 1532 - 1542
  • [34] Metal Artifact Reduction in X-Ray Computed Tomography Based On Local Anatomical Similarity
    Dong, X.
    Yang, X.
    Rosenfield, J.
    Elder, E.
    Dhabaan, A.
    MEDICAL PHYSICS, 2016, 43 (06) : 3326 - 3326
  • [35] Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography
    Zhang, Yanbo
    Yu, Hengyong
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2018, 37 (06) : 1370 - 1381
  • [36] High-kVp Assisted Metal Artifact Reduction for X-Ray Computed Tomography
    Xi, Yan
    Jin, Yannan
    De Man, Bruno
    Wang, Ge
    IEEE ACCESS, 2016, 4 : 4769 - 4776
  • [37] Sparsity Constrained Sinogram Inpainting for Metal Artifact Reduction in X-ray Computed Tomography
    Mehranian, A.
    Ay, M. R.
    Rahmim, A.
    Zaidi, H.
    2011 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2011, : 3694 - 3699
  • [38] Metal Artifact Reduction in CT via Ray Profile Correction
    Ha, Sungsoo
    Mueller, Klaus
    MEDICAL IMAGING 2016: PHYSICS OF MEDICAL IMAGING, 2016, 9783
  • [39] Reconstruction Artifact Reduction in X-Ray Cone Beam CT Using a Treatment Couch Model
    Lasio, G.
    Hu, E.
    Zhou, J.
    Lee, M.
    Yi, B.
    MEDICAL PHYSICS, 2015, 42 (06) : 3201 - 3201
  • [40] Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
    Nazemi, Ehsan
    Six, Nathanael
    Iuso, Domenico
    De Samber, Bjorn
    Sijbers, Jan
    De Beenhouwer, Jan
    APPLIED SCIENCES-BASEL, 2021, 11 (07):