Prior-based artifact correction (PBAC) in computed tomography

被引:31
|
作者
Heusser, Thorsten [1 ]
Brehm, Marcus [1 ]
Ritschl, Ludwig [2 ]
Sawall, Stefan [1 ,3 ]
Kachelriess, Marc [1 ,3 ]
机构
[1] German Canc Res Ctr, D-69120 Heidelberg, Germany
[2] Ziehm Imaging GmbH, D-90451 Nurnberg, Germany
[3] Univ Erlangen Nurnberg, Inst Med Phys, D-91052 Erlangen, Germany
关键词
metal artifact reduction; truncation artifact reduction; limited angle artifact reduction; prior knowledge; sinogram inpainting; CONE-BEAM CT; FIELD-OF-VIEW; PRINCIPAL COMPONENT ANALYSIS; RADIATION-THERAPY; IMAGE-RECONSTRUCTION; SHADING CORRECTION; PROJECTION DATA; PLANNING CT; REDUCTION; ALGORITHM;
D O I
10.1118/1.4851536
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion. Methods: The proposed prior-based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form of a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image. Results: The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry-based flat detector CT device. In all cases, the corrected images obtained by PBAC are nearly artifact-free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient-specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result. Conclusions: The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data. (C) 2014 American Association of Physicists in Medicine.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Frequency split metal artifact reduction (FSMAR) in computed tomography
    Meyer, Esther
    Raupach, Rainer
    Lell, Michael
    Schmidt, Bernhard
    Kachelriess, Marc
    MEDICAL PHYSICS, 2012, 39 (04) : 1904 - 1916
  • [22] Influence of Artifact Reduction Tools in Micro-computed Tomography Images for Endodontic Research
    Queiroz, Polyane Mazucatto
    Rovaris, Karla
    Gaeta-Araujo, Hugo
    de Souza Bueno, Stefani Marzola
    Freitas, Deborah Queiroz
    Groppo, Francisco Carlos
    Haiter-Neto, Francisco
    JOURNAL OF ENDODONTICS, 2017, 43 (12) : 2108 - 2111
  • [23] Model Image-Based Metal Artifact Reduction for Computed Tomography
    Dmytro Luzhbin
    Jay Wu
    Journal of Digital Imaging, 2020, 33 : 71 - 82
  • [24] Model Image-Based Metal Artifact Reduction for Computed Tomography
    Luzhbin, Dmytro
    Wu, Jay
    JOURNAL OF DIGITAL IMAGING, 2020, 33 (01) : 71 - 82
  • [25] Metal Artifact Reduction Based on Beam Hardening Correction and Statistical Iterative Reconstruction for X-ray Computed Tomography
    Zhang, Yanbo
    Mou, Xuanqin
    MEDICAL IMAGING 2013: PHYSICS OF MEDICAL IMAGING, 2013, 8668
  • [26] Nonlinear Deviation CorrecTion for Ring-Artifact Removal With Cone-Beam Computed Tomography
    Guo, Xiaodong
    Wu, Weiwen
    Duan, Xiaojiao
    Yu, Haijun
    Chang, Dingyue
    He, Peng
    Wang, Jue
    Zhou, Rifeng
    Du, Yu
    An, Kang
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [27] Common computed tomography artifact: source and avoidance
    Alzain, Amel F.
    Elhussein, Nagwan
    Fadulelmulla, Ibtisam Abdallah
    Ahmed, Amna Mohamed
    Elbashir, M. E.
    Elamin, Badria Awad
    EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE, 2021, 52 (01)
  • [28] Dual-energy-based metal segmentation for metal artifact reduction in dental computed tomography
    Hegazy, Mohamed A. A.
    Eldib, Mohamed Elsayed
    Hernandez, Daniel
    Cho, Myung Hye
    Cho, Min Hyoung
    Lee, Soo Yeol
    MEDICAL PHYSICS, 2018, 45 (02) : 714 - 724
  • [29] Blind Separation Model of Multi-voltage Projections for the Hardening Artifact Correction in Computed Tomography
    Wei, Jiaotong
    Chen, Ping
    Han, Yan
    Zhao, Yaoxia
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 64
  • [30] The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review
    Puvanasunthararajah, Sathyathas
    Fontanarosa, Davide
    Wille, Marie-Luise
    Camps, Saskia M.
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2021, 22 (06): : 198 - 223