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深度学习重建算法在上腹部CT成像中的应用

温德英 杨杰尹 汪琴 李臻 王寒箫 汪艾杰 邓巧 唐露 伍希 姚晋 卢春燕 孙家瑜

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, 2022, 31(3): 329-336. DOI: 10.15953/j.ctta.2021.005
引用本文: 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, 2022, 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, 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)

深度学习重建算法在上腹部CT成像中的应用

doi: 10.15953/j.ctta.2021.005
基金项目: 四川省科技厅重点研发项目(2020 YFS0123)。
详细信息
    作者简介:

    温德英:女,四川大学华西医院放射科技师,主要从事CT、MR成像技术研究,E-mail:940035866@qq.com

    孙家瑜:影像医学与核医学硕士,四川大学华西医院放射科主任技师,主要从事CT、MR成像及心脏MR技术研究,E-mail:sjy080512@163.com

  • 中图分类号: O  242;R  814

Application of Deep Learning Reconstruction Algorithm in Upper Abdomen CT

  • 摘要: 目的:通过分析比较自适应统计迭代重建(ASIR)算法和深度学习重建(DLIR)算法在上腹部CT成像中的图像质量,探讨DLIR算法在上腹部CT成像中的应用价值。方法:回顾性纳入75例患者上腹部CT平扫图像,利用自适应统计迭代重建算法ASIR(30%、50%、70%、90%)和深度学习重建算法(DL-L、DL-M、DL-H)重建图像,共7组。测量每组图像肝脏、胰腺、竖脊肌的CT值和SD值,并计算信噪比(SNR)和对比噪声比(CNR),采用单因素方差分析对各指标进行客观评价;同时由两位放射医师对图像质量和噪声评分,采用Friedman M检验进行比较。结果:①七组重建图像的SD值、SNR、肝脏CNR差异有统计学意义。②DL-L与ASIR 50%、DL-M与ASIR 70%、DL-H与ASIR 90% 间各ROI处CT值、SD值、SNR值、CNR值无差异。③三种深度学习重建算法间随等级升高,SNR值升高,差异有统计学意义;且DL-H 算法的SNR值高于ASIR 30%、ASIR 50%,SD值低于除ASIR 90% 外的其余5组重建算法。④七组图像主观评分差异有统计学意义,算法DL-H具有最佳的图像质量和最低的噪声,DL-M、ASIR 90%、DL-L、ASIR 70%、ASIR 50%、ASIR 30% 图像噪声依次增加。结论:深度学习重建算法能够降低上腹部图像噪声,提高图像质量,且随等级升高,图像噪声降低、质量提高、信噪比升高。

     

  • 图  1  上腹部7组重建图像

    (a)~(g)依次是算法DL-L、DL-M、DL-H、ASIR 30%、ASIR 50%、ASIR 70%、ASIR 90%。

    Figure  1.  7 groups of reconstructed images of upper abdomen

    图  2  上腹部7组重建图像局部放大图

    (a)~(g)依次是算法DL-L、DL-M、DL-H、ASIR 30%、ASIR 50%、ASIR 70%、ASIR 90%。

    Figure  2.  Partial enlarged view of 7 groups of reconstructed images of upper abdomen

    表  1  图像主观评价5级评分标准

    Table  1.   5-level scoring standard for subjective image evaluation

    评分评分细节
    5分肝脏、胰腺等上腹部组织结构显示非常清晰,图像细腻,能提供充分的诊断信息
    4分肝脏、胰腺等上腹部组织结构显示较为清晰,图像较细腻,能提供足够的诊断信息
    3分肝脏、胰腺等上腹部组织结构显示欠清晰,图像欠细腻,能提供一定的诊断信息
    2分肝脏、胰腺等上腹部组织结构显示模糊,图像粗糙,图像提供的诊断信息不足
    1分肝脏、胰腺等上腹部组织结构无法清晰显示,伪影重,图像不清,不能提供诊断信息
    下载: 导出CSV

    表  2  七组重建图像的CT值、SD值分析

    Table  2.   Analysis of CT value and SD value of seven groups of reconstructed images

    算法CT值/HU SD值/HU
    ROI1ROI2ROI3ROI1ROI2ROI3
    DL-L60.65±8.6444.63±7.4549.43±8.70 14.05±4.2115.38±4.7315.25±5.57
    DL-M61.24±9.6244.33±7.4649.46±8.6711.38±3.2712.81±3.9412.68±4.97
    DL-H60.64±8.4344.33±7.2749.48±8.668.41±2.159.53±3.169.90±4.54
    ASIR 30%60.58±8.8044.50±7.6849.46±8.5517.70±5.2519.34±5.5418.79±6.26
    ASIR 50%60.65±8.8144.29±7.4849.45±8.6014.69±4.3316.14±4.9515.77±5.46
    ASIR 70%60.59±8.9644.32±7.2849.51±8.6611.86±4.4313.07±4.2412.85±4.92
    ASIR 90%60.75±8.7044.29±7.5349.55±8.888.74±2.8310.1±3.5710.27±4.70
    P>0.05>0.05>0.05<0.05<0.05<0.05
    下载: 导出CSV

    表  3  七组重建图像的SNR值、CNR值分析

    Table  3.   Analysis of SNR and CNR values of seven groups of reconstructed images

    算法SNR CNR
    ROI1ROI2ROI3 ROI1ROI2
    DL-L4.67±1.533.18±1.053.67±1.30 0.71±0.70-0.41±0.52
    DL-M5.80±1.913.80±1.284.47±1.640.90±0.89-0.53±0.64
    DL-H7.66±2.305.11±1.645.85±2.171.07±1.03-0.70±0.80
    ASIR 30%3.71±1.282.51±0.862.94±1.030.58±0.58-0.33±0.42
    ASIR 50%4.49±1.583.02±1.043.54±1.290.70±0.67-0.42±0.51
    ASIR 70%5.67±2.094.47±1.764.42±1.670.84±0.86-0.22±0.63
    ASIR 90%7.70±2.944.96±1.835.79±2.481.09±1.05-0.72±0.84
    P<0.05<0.05<0.05<0.05>0.05
    下载: 导出CSV

    表  4  图像质量主观评分分析

    Table  4.   Image quality subjective scoring analysis

    评分内容 评分者DL-LDL-MDL-HASIR 30%ASIR 50%ASIR 70%ASIR 90%
    质量 Reader 1 4.41±0.594.79±0.414.97±0.163.57±0.683.89±0.684.42±0.574.71±0.45
    Reader 24.30±0.564.84±0.364.96±0.193.53±0.573.87±0.614.36±0.484.75±0.43
    噪声 Reader 12.21±0.571.18±0.391.01±0.113.79±0.413.37±0.562.66±0.551.83±0.50
    Reader 22.18±0.511.11±0.311.05±0.223.91±0.293.18±0.392.36±0.551.68±0.46
    下载: 导出CSV
  • [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
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出版历程
  • 收稿日期:  2021-09-26
  • 录用日期:  2021-11-12
  • 网络出版日期:  2021-11-17
  • 刊出日期:  2022-05-23

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