A comparison of material decomposition techniques for dual-energy CT colonography

被引:4
|
作者
Nasirudin, Radin A. [1 ]
Tachibana, Rie [2 ]
Naeppi, Janne J. [2 ]
Mei, Kai [1 ]
Kopp, Felix K. [1 ]
Rummeny, Ernst J. [1 ]
Yoshida, Hiroyuki [2 ]
Noel, Peter B. [1 ]
机构
[1] Tech Univ Munich, Dept Diagnost & Intervent Radiol, D-80290 Munich, Germany
[2] Harvard Univ, Massachusetts Gen Hosp, Sch Med, Dept Radiol,Imaging Res D3, Boston, MA USA
关键词
Dual-Energy CT; CT Colonography; Computer-Aided Design; Material Decomposition; POLYPS; PERFORMANCE; EXPERIENCE; CAD;
D O I
10.1117/12.2081982
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In recent years, dual-energy computed tomography (DECT) has been widely used in the clinical routine due to improved diagnostics capability from additional spectral information. One promising application for DECT is CT colonography (CTC) in combination with computer-aided diagnosis (CAD) for detection of lesions and polyps. While CAD has demonstrated in the past that it is able to detect small polyps, its performance is highly dependent on the quality of the input data. The presence of artifacts such as beam-hardening and noise in ultra-low-dose CTC may severely degrade detection performances of small polyps. In this work, we investigate and compare virtual monochromatic images, generated by image-based decomposition and projection-based decomposition, with respect to CAD performance. In the image-based method, reconstructed images are firstly decomposed into water and iodine before the virtual monochromatic images are calculated. On the contrary, in the projection-based method, the projection data are first decomposed before calculation of virtual monochromatic projection and reconstruction. Both material decomposition methods are evaluated with regards to the accuracy of iodine detection. Further, the performance of the virtual monochromatic images is qualitatively and quantitatively assessed. Preliminary results show that the projection-based method does not only have a more accurate detection of iodine, but also delivers virtual monochromatic images with reduced beam hardening artifacts in comparison with the image-based method. With regards to the CAD performance, the projection-based method yields an improved detection performance of polyps in comparison with that of the image-based method.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Dynamic material decomposition method for MeV dual-energy X-ray CT
    Zhao, Tiao
    Li, Liang
    Chen, Zhiqiang
    APPLIED RADIATION AND ISOTOPES, 2018, 140 : 55 - 62
  • [32] A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks
    Shi, Zaifeng
    Li, Huilong
    Cao, Qingjie
    Wang, Zhongqi
    Cheng, Ming
    MEDICAL PHYSICS, 2021, 48 (06) : 2891 - 2905
  • [33] Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography
    Tachibana, Rie
    Nappi, Janne J.
    Ota, Junko
    Kohlhase, Nadja
    Hironaka, Toru
    Kim, Se Hyung
    Regge, Daniele
    Yoshida, Hiroyuki
    RADIOGRAPHICS, 2018, 38 (07) : 2034 - 2050
  • [34] Dual-Energy CT: Techniques in Acquisition and Image Processing
    Pelc, N.
    MEDICAL PHYSICS, 2016, 43 (06) : 3746 - 3746
  • [35] LEARNED MIXED MATERIAL MODELS FOR EFFICIENT CLUSTERING BASED DUAL-ENERGY CT IMAGE DECOMPOSITION
    Li, Zhipeng
    Ravishankar, Saiprasad
    Long, Yong
    Fessler, Jeffrey A.
    2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018), 2018, : 529 - 533
  • [36] Two-Material Decomposition Algorithm of Dual-Energy CT Based on Gradient Descent Method
    Teng Y.-Y.
    Zheng S.-Y.
    Lu Z.-P.
    Kang Y.
    Dongbei Daxue Xuebao/Journal of Northeastern University, 2017, 38 (07): : 936 - 940
  • [37] Prediction of proton stopping power ratios using dual-energy CT basis material decomposition
    Pettersson, Erik
    Thilander-Klang, Anne
    Back, Anna
    MEDICAL PHYSICS, 2024, 51 (02) : 881 - 897
  • [38] Iterative Material Decomposition with Gradient L0-norm Minimization for Dual-energy CT
    Wang, Qian
    Xie, Huiqiao
    Wang, Tonghe
    Roper, Justin
    Tang, Xiangyang
    Bradley, Jeffrey D.
    Liu, Tian
    Yang, Xiaofeng
    MEDICAL IMAGING 2022: IMAGE PROCESSING, 2022, 12032
  • [39] Noise Suppression in Image-Domain Multi-Material Decomposition for Dual-Energy CT
    Jiang, Yangkang
    Xue, Yi
    Lyu, Qihui
    Xu, Lei
    Luo, Chen
    Yang, Pengfei
    Yang, Chunlin
    Wang, Jing
    Hu, Xi
    Zhang, Xiaoqun
    Sheng, Ke
    Niu, Tianye
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2020, 67 (02) : 523 - 535
  • [40] Dual-energy CT based material decomposition to differentiate intrahepatic cholangiocarcinoma from hepatocellular carcinoma
    Mahmoudi, Scherwin
    Bernatz, Simon
    Althoff, Friederike C.
    Koch, Vitali
    Gruenewald, Leon D.
    Scholtz, Jan-Erik
    Walter, Dirk
    Zeuzem, Stefan
    Wild, Peter J.
    Vogl, Thomas J.
    Kinzler, Maximilian N.
    EUROPEAN JOURNAL OF RADIOLOGY, 2022, 156