ISSN 1004-4140
CN 11-3017/P
Volume 31 Issue 3
May  2022
Turn off MathJax
Article Contents
Abstract
References
WEN D Y, YANG J Y, WANG Q, et al. Application of deep learning reconstruction algorithm in upper abdomen CT[J]. CT Theory and Applications, 2022, 31(3): 329-336. DOI: 10.15953/j.ctta.2021.005. (in Chinese).
Citation: WEN D Y, YANG J Y, WANG Q, et al. Application of deep learning reconstruction algorithm in upper abdomen CT[J]. CT Theory and Applications, 2022, 31(3): 329-336. DOI: 10.15953/j.ctta.2021.005. (in Chinese).
shu

Application of Deep Learning Reconstruction Algorithm in Upper Abdomen CT

  • Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China

More Information
  • Received Date: September 25, 2021
  • Accepted Date: November 11, 2021
  • Available Online: November 16, 2021
  • Published Date: May 22, 2022
    Graphical Abstract
    • Abstract
  • Objective: To explore the application of deep learning image reconstruction (DLIR) algorithm in upper abdominal CT imaging by analyzing the image quality of adaptive statistical iterative reconstruction (ASIR) algorithm and DLIR. Methods: Retrospectively included 75 patients’ upper abdominal CT plain scan images, using adaptive statistical iterative reconstruction algorithm ASIR (30%, 50%, 70%, 90%) and deep learning reconstruction algorithm (DL-L, DL-M, DL-H) to reconstruct images, a total of 7 groups. Measured the CT and SD values of the liver, pancreas, and erector spinae , and calculated the signal to noise ratio (SNR) and contrast to noise ratio (CNR). Objective indicators were evaluated by one-way ANOVA. Two radiologists scored the image quality and noise, and compared them with Friedman <i<M</i< test. Results: (1) The SD value, SNR, and liver CNR of the seven reconstructed images had statistically significant differences. (2) The difference in CT value, SD value, SNR value and CNR value at each ROI between DL-L and ASIR 50%, DL-M and ASIR 70%, DL-H and ASIR 90% was small. (3) The SNR value of the three DLIR algorithms increased as the level increased, and the difference was statistically significant; and the SNR value of the DL-H algorithm was higher than ASIR 30% and ASIR 50%, and the SD value was lower than the other five reconstruction algorithms except for the ASIR 90%. (4) The difference in the subjective scores of the seven groups of images was statistically significant. The algorithm DL-H had the best image quality and the lowest noise, DL-M, ASIR 90%, DL-L, ASIR 70%, ASIR 50%, ASIR 30% image noise in sequence increased. Conclusion: The DLIR algorithm can reduce the image noise of the upper abdomen and improve the image quality. As the level increased, the image noise decreased, the quality improved, and the signal-to-noise ratio increased.
    • FullText(HTML)
    • References (22)
  • [1]
    张卓璐, 王征, 刘卓, 等. 迭代重建算法对冠状动脉Agatston钙化积分的影响[J]. 临床放射学杂志, 2020,39(10): 2093−2097.

    ZHANG Z L, WANG Z, LIU Z, et al. Influence of iterative reconstruction algorithm on coronary artery agatston calcium score[J]. Journal of Clinical Radiology, 2020, 39(10): 2093−2097. (in Chinese).
    [2]
    张喜荣, 贺太平, 贾永军, 等. 低辐射剂量下FBP、ASIR和ASIR-V 3种不同重建算法对上腹部CT图像质量的影响[J]. 中国中西医结合影像学杂志, 2020,18(3): 305−308. doi: 10.3969/j.issn.1672-0512.2020.03.027

    ZHANG X R, HE T P, JIA Y J, et al. Effect of three different reconstruction algorithms (FBP, ASIR and ASIR-V) on the upper abdominal CT image quality with low radiation dose[J]. Chinese Imaging Journal of Integrated Traditional and Western Medicine, 2020, 18(3): 305−308. (in Chinese). doi: 10.3969/j.issn.1672-0512.2020.03.027
    [3]
    贾永军, 于勇, 贺太平, 等. 新一代基于模型的迭代重建在低剂量上腹部CT中的应用[J]. 中国医学影像技术, 2017,33(12): 1882−1887.

    JIA Y J, YU Y, HE T P, et al. Application of new model-based iterative reconstruction in low-dose upper abdominal CT[J]. Chinese Journal of Medical Imaging Technology, 2017, 33(12): 1882−1887. (in Chinese).
    [4]
    陈其锋, 林梓朗, 杨宇凌. 低剂量扫描联合迭代重建CTA在颈部疾病患者中的应用效果及价值研究[J]. 医学理论与实践, 2021,34(7): 1205−1207.
    [5]
    姜一, 秦立新, 李宝学, 等. 80kV结合低剂量对比剂和迭代重建在胸部CT增强检查中的运用[J]. 中国医疗设备, 2021,36(3): 102−105. doi: 10.3969/j.issn.1674-1633.2021.03.022

    JIANG Y, QIN L X, LI B X, et al. Application of 80kV Combined with low-dose contrast medium and iterative reconstruction in enhanced chest CT examination[J]. China Medical Devices, 2021, 36(3): 102−105. (in Chinese). doi: 10.3969/j.issn.1674-1633.2021.03.022
    [6]
    FRANCK C, ZHANG G, DEAK P, et al. Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study[J]. Physica Medica, 2021, 81: 86−93. doi: 10.1016/j.ejmp.2020.12.005
    [7]
    BENZ D C, BENETOS G, RAMPIDIS G, et al. Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy[J]. Journal of Cardiovascular Computed Tomography, 2020, 14(5): 444−451. doi: 10.1016/j.jcct.2020.01.002
    [8]
    PARAKH A, CAO J, PIERCE T T, et al. Sinogram-based deep learning image reconstruction technique in abdominal CT: Image quality considerations[J]. European Radiology, 2021: 1−12.
    [9]
    NJØLSTAD T, SCHULZ A, GODT J C, et al. Improved image quality in abdominal computed tomography reconstructed with a novel deep learning image reconstruction technique: Initial clinical experience[J]. Acta Radiologica Open, 2021, 10(4): 20584601211008391.
    [10]
    贾秀川, 陈英敏, 暴云锋, 等. 双源CT小肠造影双能量虚拟平扫与常规平扫对比研究[J]. 中国医疗设备, 2021,36(1): 87−89, 93. doi: 10.3969/j.issn.1674-1633.2021.01.018

    JIA X C, CHEN Y M, BAO Y F, et al. Comparative study of virtual and conventional non-contrast CT enterography using dual-source and energy CT[J]. China Medical Devices, 2021, 36(1): 87−89, 93. (in Chinese). doi: 10.3969/j.issn.1674-1633.2021.01.018
    [11]
    曾文, 曾令明, 徐旭, 等. 基于深度学习的图像重建算法在胸部薄层CT中的降噪效果评估[J]. 四川大学学报(医学版), 2021,52(2): 286−292.

    ZENG W, ZENG L M, XU X, et al. Noise reduction effect of deep-learning-based image reconstruction algorithms in thin-section chest CT[J]. Journal of Sichuan University (Medical Sciences), 2021, 52(2): 286−292. (in Chinese).
    [12]
    SILVA A C, LAWDER H J, HARA A, et al. Innovations in CT dose reduction strategy: Application of the adaptive statistical iterative reconstruction algorithm[J]. American Journal of Roentgenology, 2010, 194(1): 191−199. doi: 10.2214/AJR.09.2953
    [13]
    JENSEN C T, WAGNER-BARTAK N A, VU L N, et al. Detection of colorectal hepatic metastases is superior at standard radiation dose CT versus reduced dose CT[J]. Radiology, 2019, 290(2): 400−409. doi: 10.1148/radiol.2018181657
    [14]
    LI L L, WANG H, SONG J, et al. A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm[J]. Journal of X-ray Science and Technology, 2021, 29(2): 361−372. doi: 10.3233/XST-200826
    [15]
    孙记航, 王帆宁, 段晓岷, 等. 自适应迭代重建技术结合高分辨算法提高儿童低剂量胸部CT肺脏病变显示的能力[J]. 中国医学影像技术, 2017,33(5): 773−777.

    SUN J H, WANG F N, DUAN X M, et al. Improve image resolution in low-dose pediatric chest CT scans with combination of adaptive statistical iterative reconstruction and sharp recon kernel[J]. Chinese Journal of Medical Imaging Technology, 2017, 33(5): 773−777. (in Chinese).
    [16]
    HATA A, YANAGAWA M, YOSHIDA Y, et al. The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting[J]. Clinical Radiology, 2021, 76(2): 155.e15−155.e23. doi: 10.1016/j.crad.2020.10.011
    [17]
    SINGH R, DIGUMARTHY S R, MUSE V V, et al. Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT[J]. American Journal of Roentgenology, 2020, 214(3): 566−573. doi: 10.2214/AJR.19.21809
    [18]
    HIGAKI T, NAKAMURA Y, ZHOU J, et al. Deep learning reconstruction at CT: Phantom study of the image characteristics[J]. Academic radiology, 2020, 27(1): 82−87. doi: 10.1016/j.acra.2019.09.008
    [19]
    KIM J H, YOON H J, LEE E, et al. Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise[J]. Korean Journal of Radiology, 2021, 22(1): 131. doi: 10.3348/kjr.2020.0116
    [20]
    NODA Y, KAGA T, KAWAI N, et al. Low-dose whole-body CT using deep learning image reconstruction: Image quality and lesion detection[J]. The British Journal of Radiology, 2021, 94: 20201329. doi: 10.1259/bjr.20201329
    [21]
    NAM J G, HONG J H, KIM D S, et al. Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: Similar image quality with lower radiation dose in direct comparison with iterative reconstruction[J]. European Radiology, 2021, 31(8): 5533−5543. doi: 10.1007/s00330-021-07712-4
    [22]
    CHENG Y, HAN Y, LI J, et al. Low-dose CT urography using deep learning image reconstruction: A prospective study for comparison with conventional CT urography[J]. The British Journal of Radiology, 2021, 94(1120): 20201291. doi: 10.1259/bjr.20201291
    • Related Articles

  • Cited by

    Periodical cited type(1)

    1. 赖小珍，邓玲玲. 比较MRI与CT检查对肝脏孤立性结节鉴别的诊断价值. 现代诊断与治疗. 2024(13): 1976-1977+1980 .

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML
    Article views (472) PDF downloads (78) Cited by(1)
    Related

    Website Copyright © Editorial Office of Computerized Tomography Theory and Applications

    Address：No.5 Minzudaxue Nanlu,Haidian District,Beijing 100081,P.R.China（100081）

    E-mail：cttacn@cea-igp.ac.cn

    Phone：010-68729234

    京ICP备08002722号-11

    震球期刊
    CT理论与应用研究-中英文
    E-mail
    RSS

    Supported by: Beijing Renhe Information Technology Co. Ltd     Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return