Effect of tube current on computed tomography radiomic features

被引:0
|
作者
Dennis Mackin
Rachel Ger
Cristina Dodge
Xenia Fave
Pai-Chun Chi
Lifei Zhang
Jinzhong Yang
Steve Bache
Charles Dodge
A. Kyle Jones
Laurence Court
机构
[1] The University of Texas MD Anderson Cancer Center,Department of Radiation Physics
[2] The University of Texas Health Science Center at Houston,Graduate School of Biomedical Sciences
[3] Texas Children’s Hospital,Department of Radiology
[4] The University of Texas MD Anderson Cancer Center,Department of Imaging Physics
[5] Houston Methodist Hospital,Imaging Physics
来源
Scientific Reports | / 8卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Variability in the x-ray tube current used in computed tomography may affect quantitative features extracted from the images. To investigate these effects, we scanned the Credence Cartridge Radiomics phantom 12 times, varying the tube current from 25 to 300 mA∙s while keeping the other acquisition parameters constant. For each of the scans, we extracted 48 radiomic features from the categories of intensity histogram (n = 10), gray-level run length matrix (n = 11), gray-level co-occurrence matrix (n = 22), and neighborhood gray tone difference matrix (n = 5). To gauge the size of the tube current effects, we scaled the features by the coefficient of variation of the corresponding features extracted from images of non-small cell lung cancer tumors. Variations in the tube current had more effect on features extracted from homogeneous materials (acrylic, sycamore wood) than from materials with more tissue-like textures (cork, rubber particles). Thirty-eight of the 48 features extracted from acrylic were affected by current reductions compared with only 2 of the 48 features extracted from rubber particles. These results indicate that variable x-ray tube current is unlikely to have a large effect on radiomic features extracted from computed tomography images of textured objects such as tumors.
引用
收藏
相关论文
共 50 条
  • [1] Effect of tube current on computed tomography radiomic features
    Mackin, Dennis
    Ger, Rachel
    Dodge, Cristina
    Fave, Xenia
    Chi, Pai-Chun
    Zhang, Lifei
    Yang, Jinzhong
    Bache, Steve
    Dodge, Charles
    Jones, A. Kyle
    Court, Laurence
    SCIENTIFIC REPORTS, 2018, 8
  • [2] Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features
    Foy, Joseph J.
    Shenouda, Mena
    Ramahi, Sahar
    Armato, Samuel
    Ginat, Daniel Thomas
    JOURNAL OF MEDICAL IMAGING, 2020, 7 (06)
  • [3] Effect of reduction in tube current on reader confidence in paediatric computed tomography
    Shah, R
    Gupta, AK
    Rehani, MM
    Pandey, AK
    Mukhopadhyay, S
    CLINICAL RADIOLOGY, 2005, 60 (02) : 224 - 231
  • [4] Radiomic features analysis in computed tomography images of lung nodule classification
    Chen, Chia-Hung
    Chang, Chih-Kun
    Tu, Chih-Yen
    Liao, Wei-Chih
    Wu, Bing-Ru
    Chou, Kuei-Ting
    Chiou, Yu-Rou
    Yang, Shih-Neng
    Zhang, Geoffrey
    Huang, Tzung-Chi
    PLOS ONE, 2018, 13 (02):
  • [5] Effects of Reconstruction Filter Kernels in Computed Tomography on Radiomic Image Features
    Sweetland, Katharine
    Chu, Karen
    Lei, Xiaomeng
    Cen, Steven Y.
    Duddalwar, Vinay A.
    Varghese, Bino A.
    Hwang, Darryl H.
    2023 19TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, SIPAIM, 2023,
  • [6] The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review
    Reiazi, Reza
    Abbas, Engy
    Famiyeh, Petra
    Rezaie, Aria
    Kwan, Jennifer Y. Y.
    Patel, Tirth
    Bratman, Scott V.
    Tadic, Tony
    Liu, Fei-Fei
    Haibe-Kains, Benjamin
    COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 133
  • [7] Effect of Tube Current on Linear Measurement Accuracy of Cone Beam Computed Tomography Images
    Sener, Elif
    Onem, Erinc
    Mert, Ali
    Baksi, B. Guniz
    CURRENT MEDICAL IMAGING REVIEWS, 2018, 14 (02) : 327 - 333
  • [8] Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography
    Kim, Young Jae
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (16)
  • [9] Computed Tomography-Based Radiomic Features for Diagnosis of Indeterminate Small Pulmonary Nodules
    Liu, Qin
    Huang, Yan
    Chen, Huai
    Liu, Yanwen
    Liang, Ruihong
    Zeng, Qingsi
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2020, 44 (01) : 90 - 94
  • [10] Computed Tomography-Based Radiomic Features for Prognostication in Patients with Primary Retroperitoneal Sarcoma
    Pasquali, Sandro
    Iadecola, Sara
    Infante, Gabriele
    Bologna, Marco
    Corino, Valentina
    Greco, Gabriella
    Morosi, Carlo
    Beretta, Alessia
    Percio, Stefano
    Vallacchi, Viviana
    Brich, Silvia
    Bertolotti, Alessia
    Sanfilippo, Roberta
    Fabbroni, Chiara
    Stacchiotti, Silvia
    Fiore, Marco
    Mainardi, Luca
    Miceli, Rosalba
    Gronchi, Alessandro
    Callegaro, Dario
    ANNALS OF SURGICAL ONCOLOGY, 2024, 31 (01) : S260 - S261