Efficiency and reproducibility in pulmonary nodule detection in simulated dose reduction lung CT images

被引:1
|
作者
Kubo, Takeshi [1 ]
Kishimoto, Ayami Ohno [1 ]
Togashi, Kaori [1 ]
机构
[1] Kyoto Univ, Dept Diagnost Imaging & Nucl Med, Grad Sch Med, Sakyo Ku, 54 Shogoin Kawahara Cho, Kyoto 6068507, Japan
关键词
Computed tomography; Radiation dose reduction; Automatic exposure control; Pulmonary nodules; AUTOMATIC EXPOSURE CONTROL; TUBE CURRENT MODULATION; CHEST CT; COMPUTED-TOMOGRAPHY; OPTIMIZATION; QUALITY; MAS;
D O I
10.1016/j.ejro.2019.02.001
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: To determine the reproducibility and productivity of reduced dose chest computed tomography (CT) using a nodule detection task. Materials and methods: Eighty-eight consecutive non-contrast CT examinations were performed using an automatic exposure system with a reference standard deviation of 8.5. Simulated raw data of a reduced dose scan (standard deviation at 21 and 29) were generated with a dose simulator. Original and simulated raw data were reconstructed to series of 7-mm-thick images (Original, Simulation A, Simulation B). In the first part of the reading experiment, three readers independently interpreted these images (88 cases x 3 series) and recorded the size, type, and location of the pulmonary nodules. The reading time for every case was recorded. In the second part of the experiment, the repeated interpretation of standard dose images was performed by two readers. Concordance or discordance of nodule detection between the first and the repeated reading result was assessed. Results: A statistically significant difference in the detected nodule counts for lesions less than 5 mm by one reader was observed in simulation B images. Discordance of the interpretation result was found only in ground-glass nodules larger than 5 mm detected by one reader in simulation B images. There was no statistically significant difference in the reading time among the three image types. Conclusion: Simulated standard deviation 21 images can reproduce the image interpretation result of original images, whereas simulated standard deviation 29 images may compromise the accuracy of nodule assessment. The effect on the reading time was not observed with dose reduction simulation.
引用
收藏
页码:113 / 118
页数:6
相关论文
共 50 条
  • [2] A Pulmonary Nodule Detection Algorithm Based on Low Dose CT Images
    Yang, Qian
    HuiqinJiang
    LingMa
    XiaozhenDu
    Gao, Jianbo
    [J]. THIRD INTERNATIONAL SYMPOSIUM ON IMAGE COMPUTING AND DIGITAL MEDICINE (ISICDM 2019), 2019, : 239 - 243
  • [3] Pulmonary nodule detection in PET/CT images: Improved approach using combined nodule detection and hybrid FP reduction
    Teramoto, Atsushi
    Fujita, Hiroshi
    Tomita, Yoya
    Takahashi, Katsuaki
    Yamamuro, Osamu
    Tamaki, Tsuneo
    [J]. MEDICAL IMAGING 2012: COMPUTER-AIDED DIAGNOSIS, 2012, 8315
  • [4] A Multimodal Neural Network for Lung Nodule Detection with Low-Dose CT Images
    Cao, Wenming
    Wu, Rui
    Cao, Guitao
    [J]. BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 124 : 37 - 38
  • [5] Automated pulmonary nodule detection on helical CT images
    Lee, Y
    Hara, T
    Fujita, H
    Kojima, A
    Itoh, S
    Ishigaki, T
    [J]. CAR '98 - COMPUTER ASSISTED RADIOLOGY AND SURGERY, 1998, 1165 : 878 - 878
  • [6] A unified approach to pulmonary nodule detection in CT images
    Matsumoto, S
    Kundel, HL
    Gefter, WB
    Hatabu, H
    [J]. RADIOLOGY, 2002, 225 : 534 - 534
  • [7] Pulmonary nodule detection using chest CT images
    Kim, DY
    Kim, JH
    Noh, SM
    Park, JW
    [J]. ACTA RADIOLOGICA, 2003, 44 (03) : 252 - 257
  • [8] Automated lung nodule detection in helical CT images - False positive reduction strategies
    Gurcan, MN
    Sahiner, B
    Petrick, NA
    Chan, H
    Kazerooni, EA
    Cascade, PN
    [J]. RADIOLOGY, 2001, 221 : 546 - 547
  • [9] Pulmonary nodule detection with low-dose CT of the lung: Agreement among radiologists
    Leader, JK
    Warfel, TE
    Fuhrman, CR
    Golla, SK
    Weissfeld, JL
    Avila, RS
    Turner, WD
    Zheng, B
    [J]. AMERICAN JOURNAL OF ROENTGENOLOGY, 2005, 185 (04) : 973 - 978
  • [10] Automatic Detection and Segmentation of Lung Nodule on CT Images
    Yang Chunran
    Wang Yuanyuan
    Guo Yi
    [J]. 2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018), 2018,