Accuracy and variability of radiomics in photon-counting CT: texture features and lung lesion morphology

被引:1
作者
Rajagopal, Jayasai R. [1 ,2 ,4 ]
Hoye, Jocelyn [1 ,2 ]
Robins, Marthony [1 ,2 ]
Jones, Elizabeth C. [4 ]
Samei, Ehsan [1 ,2 ,3 ]
机构
[1] Duke Univ, Med Ctr, Dept Radiol, Carl E Ravin Adv Imaging Labs, Durham, NC 27705 USA
[2] Duke Univ, Med Ctr, Med Phys Grad Program, Durham, NC 27705 USA
[3] Duke Univ, Med Ctr, Dept Radiol, Clin Imaging Phys Grp, Durham, NC 27705 USA
[4] NIH, Radiol & Imaging Sci, Ctr Clin, Bldg 10, Bethesda, MD 20892 USA
来源
MEDICAL IMAGING 2019: PHYSICS OF MEDICAL IMAGING | 2019年 / 10948卷
基金
美国国家卫生研究院;
关键词
quantitative imaging; photon-counting; computed tomography; morphology; texture;
D O I
10.1117/12.2512934
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The purpose of this study was to evaluate the potential of a prototype photon-counting CT system scanner to characterize liver texture and lung lesion morphology features. We utilized a multi-tiered phantom (Mercury Phantom 4.0) to characterize the noise power spectrum and task-transfer functions of both conventional and photon-counting modes on the scanner. Using these metrics, we blurred three textures models and fifteen model lesions for four doses (CTDIvol: 4, 8, 16, 24 mGy), and three slice thicknesses (1.6, 2.5, 4 mm), for a total of 12 imaging conditions. Twenty texture features and twenty-one morphology features were evaluated. Performance was characterized in terms of accuracy (percent bias of features across different conditions) and variability (coefficient of variation of features due to repeats and averaged across conditions). Compared to conventional CT, photon-counting CT had comparable accuracy and variability for texture features. For morphology features, photon-counting CT had comparable accuracy and less variability than conventional CT. For both imaging modes, change in dose showed slight variation in features and increasing slice thickness caused a monotonic change with feature dependent directionality. Photon-counting CT can improve the characterization of morphology features without compromising texture features.
引用
收藏
页数:9
相关论文
共 14 条
  • [1] CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
    Coroller, Thibaud P.
    Grossmann, Patrick
    Hou, Ying
    Velazquez, Emmanuel Rios
    Leijenaar, Ralph T. H.
    Hermann, Gretchen
    Lambin, Philippe
    Haibe-Kains, Benjamin
    Mak, Raymond H.
    Aerts, Hugo J. W. L.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2015, 114 (03) : 345 - 350
  • [2] A computational model to generate simulated three-dimensional breast masses
    de Sisternes, Luis
    Brankov, Jovan G.
    Zysk, Adam M.
    Schmidt, Robert A.
    Nishikawa, Robert M.
    Wernick, Miles N.
    [J]. MEDICAL PHYSICS, 2015, 42 (02) : 1098 - 1118
  • [3] Hoye J., BIAS VARIABILITY MOR
  • [4] Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors
    Huang, Yan-Hao
    Chang, Yeun-Chung
    Huang, Chiun-Sheng
    Wu, Tsung-Ju
    Chen, Jeon-Hor
    Chang, Ruey-Feng
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2013, 112 (03) : 508 - 517
  • [5] Kappler S., RES PROTOTYPE SYSTEM, V7622
  • [6] Radiomics: the process and the challenges
    Kumar, Virendra
    Gu, Yuhua
    Basu, Satrajit
    Berglund, Anders
    Eschrich, Steven A.
    Schabath, Matthew B.
    Forster, Kenneth
    Aerts, Hugo J. W. L.
    Dekker, Andre
    Fenstermacher, David
    Goldgof, Dmitry B.
    Hall, Lawrence O.
    Lambin, Philippe
    Balagurunathan, Yoganand
    Gatenby, Robert A.
    Gillies, Robert J.
    [J]. MAGNETIC RESONANCE IMAGING, 2012, 30 (09) : 1234 - 1248
  • [7] Robins M., RELIABLE ARE TEXTURE
  • [8] Solomon J., AUTOMATED SOFTWARE T, V45, pE134
  • [9] Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms
    Solomon, Justin
    Ba, Alexandre
    Bochud, Francois
    Samei, Ehsan
    [J]. MEDICAL PHYSICS, 2016, 43 (12) : 6497 - 6506
  • [10] A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging
    Solomon, Justin
    Samei, Ehsan
    [J]. PHYSICS IN MEDICINE AND BIOLOGY, 2014, 59 (21) : 6637 - 6657