ISSN 1004-4140
CN 11-3017/P
Volume 31 Issue 3
Jun.  2022
Turn off MathJax
Article Contents
LIU F T, LIU H S, CHEN Y, et al. Image quality assessment for deep learning image reconstruction algorithm: A phantom study[J]. CT Theory and Applications, 2022, 31(3): 351-356. DOI: 10.15953/j.ctta.2021.061. (in Chinese)
Citation: LIU F T, LIU H S, CHEN Y, et al. Image quality assessment for deep learning image reconstruction algorithm: A phantom study[J]. CT Theory and Applications, 2022, 31(3): 351-356. DOI: 10.15953/j.ctta.2021.061. (in Chinese)

Image Quality Assessment for Deep Learning Image Reconstruction Algorithm: A Phantom Study

doi: 10.15953/j.ctta.2021.061
  • Received Date: 2021-12-06
  • Accepted Date: 2022-01-12
  • Available Online: 2022-01-21
  • Publish Date: 2022-05-23
  • Objective: To compare the image quality between the deep learning image reconstruction (DLIR) and iterative reconstruction (IR) algorithms via the dedicated phantom. Method: ACR quality assurance phantom (Gammex 464) was scanned by GE Revolution Apex. The CT value accuracy, low contrast resolution, image uniformity and high contrast resolution of five substances from module 1 to module 4 were measured respectively. Through the above indicators, the image quality of three levels (DL, DM and DH) of Truefedelitytm (TFI) and three levels (30%, 60% and 90%) of adaptive statistical iterative reconstruction-V (asir-V, hereinafter referred to as AV) under high dose (20 mgy) were compared. The comparison between the two algorithms for each parameter was tested by One-Way Anova. Results: The high/low-contrast resolution of the six image series were consistent (high-contrast resolution: 10 lp/cm; low-contrast resolution: 6 mm). The two algorithms both slightly overestimated the CT value of polyethylene, air and acrylic, and no statistically significant difference was found among the difference of CT values of the substances. Both algorithms underestimated the CT value of bone and the solid water; TFI showed better performance in evaluating the solid water which was closer to the real value, though statistical difference was not found between each group of images. Among the 6 groups of images, TFI DH showed the best image uniformity and at the same reconstruction level, TFI showed better uniformity than AV. Conclusion: DLIR can further reduced image noise while maintaining image spatial resolution and CT value accuracy at high dose level.

     

  • loading
  • [1]
    BEREGI J P, GREFFIER J. Low and ultra-low dose radiation in CT: Opportunities and limitations[J]. Diagnostic and Interventional Imaging, 2019, 100(2): 63−64. DOI: 10.1016/j.diii.2019.01.007.
    [2]
    MACRI F, GREFFIER J, KHASANOVA E, et al. Minor blunt thoracic trauma in the emergency department: Sensitivity and specificity of chest ultralow-dose computed tomography compared with conventional radiography[J]. Annals of Emergency Medicine, 2019, 73(6): 665−670. DOI: 10.1016/j.annemergmed.2018.11.012.
    [3]
    KIM H G, LEE H J, LEE S K, et al. Head CT: Image quality improvement with ASIR-V using a reduced radiation dose protocol for children[J]. European Radiology, 2017, 27(9): 3609−3617. DOI: 10.1007/s00330-017-4733-z.
    [4]
    LARBI A, ORLIAC C, FRANDON J, et al. Detection and characterization of focal liver lesions with ultra-low dose computed tomography in neoplastic patients[J]. Diagnostic and Interventional Imaging, 2018, 99(5): 311−320. DOI: 10.1016/j.diii.2017.11.003.
    [5]
    TANG H, LIU Z, HU Z, et al. Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT[J]. British Journal of Radiology, 2019, 92(1103): 20180909. DOI: 10.1259/bjr.20180909.
    [6]
    仵腾辉, 查云飞, 杨峰. 不同螺距联合ASIR重建技术在COVID-19胸部低剂量CT扫描中的应用研究[J]. CT理论与应用研究, 2022,31(2): 194−201. DOI: 10.15953/j.1004-4140.2022.31.02.05.

    WU T H, ZHA Y F, YANG F. The application and study of different pitch combined with ASIR in low-dose chest CT screening on COVID-19[J]. CT Theory and Applications, 2022, 31(2): 194−201. DOI: 10.15953/j.1004-4140.2022.31.02.05. (in Chinese).
    [7]
    VERDUN F R, RACINE D, OTT J G, et al. Image quality in CT: From physical measurements to model observers[J]. Physica Medica, 2015, 31(8): 823−843. DOI: 10.1016/j.ejmp.2015.08.007.
    [8]
    OTT J G, BECCE F, MONNIN P, et al. Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms[J]. Physics in Medicine and Biology, 2014, 59(15): 4047−4064. DOI: 10.1088/0031-9155/59/4/4047.
    [9]
    SAMEI E, BAKALYAR D, BOEDEKER K L, et al. Performance evaluation of computed tomography systems: Summary of AAPM task group 233[J]. Medical Physics, 2019, 46(11): e735−e756. DOI: 10.1002/mp.13763.
    [10]
    GEYER L L, SCHOEPF U J, MEINEL F G, et al. State of the art: Iterative CT reconstruction techniques[J]. Radiology, 2015, 276(2): 339−357. DOI: 10.1148/radiol.2015132766.
    [11]
    GREFFIER J, HAMARD A, PEREIRA F, et al. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: A phantom study[J]. European Radiology, 2020, 30(7): 3951−3959. DOI: 10.1007/s00330-020-06724-w.
    [12]
    LYU P, NEELY B, SOLOMON J, et al. Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence[J]. European Journal of Radiology, 2021, 141: 109825. DOI: 10.1016/j.ejrad.2021.109825.
    [13]
    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.
    [14]
    GREFFIER J, FRANDON J, Si-MOHAMED S, et al. Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data[J]. Diagnostic and Interventional Imaging, 2021, S2211-5684(21): 00174−1. DOI: 10.1016/j.diii.2021.08.001.
    [15]
    温德英, 杨杰尹, 汪琴, 等. 深度学习重建算法在上腹部CT成像中的应用[J]. CT理论与应用研究, 2021,31(3): 329−336. DOI: 10.15953/j.ctta.2021-005.

    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, 2021, 31(3): 329−336. DOI: 10.15953/j.ctta.2021-005. (in Chinese).
    [16]
    ICHIKAWA Y, KANII Y, YAMAZAKI A, et al. Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: Comparison with hybrid iterative reconstruction[J]. Japanese Journal of Radiology, 2021, 39(6): 598−604. DOI: 10.1007/s11604-021-01089-6.
    [17]
    PARK C, CHOO K S, JUNG Y, et al. CT iterative vs deep learning reconstruction: Comparison of noise and sharpness[J]. European Radiology, 2021, 31(5): 3156−3164. DOI: 10.1007/s00330-020-07358-8.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(1)  / Tables(1)

    Article Metrics

    Article Views(136) PDF Downloads(11) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return