[1]乔文俊,周芳,刘泉芬,等.深度学习图像重建算法对改善直肠CT图像质量的临床应用价值[J].中国医学物理学杂志,2024,41(8):975-981.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.008]
　QIAO Wenjun,ZHOU Fang,et al.Improving rectal CT image quality with a deep learning image reconstruction algorithm[J].Chinese Journal of Medical Physics,2024,41(8):975-981.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.008]
点击复制

深度学习图像重建算法对改善直肠CT图像质量的临床应用价值()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第8期
页码:
975-981
栏目:
医学影像物理
出版日期:
2024-08-31

文章信息/Info

Title:
Improving rectal CT image quality with a deep learning image reconstruction algorithm
文章编号:
1005-202X（2024）08-0975-07
作者:
乔文俊1; 2; 周芳1; 刘泉芬1; 黄婵桃1; 许乙凯1; 2
1.南方医科大学南方医院影像诊断科， 广东 广州 510515； 2.广东省辐射防护协会医学辐射防护专委会， 广东 广州 510515
Author(s):
QIAO Wenjun1;  2;  ZHOU Fang1;  LIU Quanfen1;  HUANG Chantao1;  XU Yikai1;  2
1. Department of Imaging Diagnostics, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China 2. Medical Radiation Protection Committee of Radiation Protection Association of Guangdong Province, Guangzhou 510515, China
关键词:
直肠; 电子计算机断层扫描; 深度学习图像重建; 图像质量
Keywords:
Keywords: rectum computed tomography deep learning image reconstruction image quality
分类号:
R318；R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2024.08.008
文献标志码:
A
摘要:
目的：探索深度学习图像重建（DLIR）算法是否可以改善静脉期肛管直肠的CT图像质量。方法：回顾性纳入进行腹部CT增强扫描的71例患者，所有影像资料使用50% ASiR-V和DLIR低、中、高（DLIR-L、DLIR-M、DLIR-H）3个强度的DLIR重建静脉期薄层图像。测量各组图像的肛管和臀部脂肪的CT值和标准差（SD），以臀部脂肪SD作为背景噪声，计算肛管对比噪声比（CNR）和信噪比（SNR）。两名影像科医师使用Likert 5分量表法独立进行图像质量评估和直肠癌局部侵犯情况诊断信心评价。分析比较客观测量指标和图像主观评分，采用Kappa检验评估一致性。结果：各组间肛管CT值及臀部脂肪CT值比较差异没有统计学意义（P>0.05），脂肪SD、肛管SNR及CNR比较差异有统计学意义（P<0.05），DLIR-H组脂肪SD最低，SNR及CNR最高，而50% ASiR-V组脂肪SD最高，SNR及CNR最低。与50% ASiR-V组相比，DLIR-H组脂肪SD降低44.3%，肛管SNR及CNR分别提升89.5%和92.1%（P<0.05）。4组图像质量主观评分比较差异有统计学意义（P<0.05），从DLIR-H到50% ASiR-V依次降低。其中50% ASiR-V、DLIR-L组间比较差异没有统计学意义（P>0.05），其余各组间比较差异均有统计学意义（P<0.05）。各组间直肠癌局部侵犯情况诊断信心评分比较差异有统计学意义（P<0.05），DLIR-M及DLIR-H组优于50% ASiR-V组（P<0.05）。结论：与标准50% ASiR-V图像相比，DLIR-M和DLIR-H重建算法能有效提高图像质量，重建强度越高，图像质量越好，显示细微结构的能力越强，能为临床精准评估及个体化精准治疗提供更多的依据。
Abstract:
Abstract: Objective To improve the CT image quality of the anorectal junction in venous phase using a new deep learning image reconstruction (DLIR) algorithm. Methods A retrospective analysis was conducted on 71 patients undergoing pelvic computed tomography (CT) scans. All CT images were reconstructed at a thin slice thickness of 0.625 mm using 50% ASiR-V, low-, medium- and high-intensity DLIR (DLIR-L, DLIR-M and DLIR-H). The CT attenuations and standard deviation values of anal canal and hip fat were measured for each reconstruction group. With the standard deviation of hip fat as background noise, the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of anal canal were calculated. Two radiologists independently assessed image quality and diagnostic confidence for local invasion of rectal cancer using the 5-point Likert scale. The objective measurement indicators and subjective scores were analyzed and compared, and Kappa test was used to evaluate the consistency. Results The differences in CT value of anal canal and hip fat among the groups were trivial (P>0.05), but fat SD, anal canal SNR and CNR (P<0.05) differed significantly, with lowest fat SD, highest anal canal SNR and CNR in DLIR-H group, while highest fat SD, lowest anal canal SNR and CNR in 50% ASiR-V group. Compared with 50% ASiR-V group, DLIR-H group decreased fat SD by 44.3%, but increased anal canal SNR and CNR by 89.5% and 92.1%, respectively (P<0.05). The subjective score of 4 groups were significantly different (P<0.05), decreasing from DLIR-H to 50% ASiR-V, and the inter-group differences were significant (P<0.05), except the difference between 50% ASiR-V group and DLIR-L group (P>0.05). There was a statistically significant difference in the diagnostic confidence for local invasion of rectal cancer among different groups (P<0.05), and the scores were significantly higher in DLIR-M and DLIR-H groups than in 50% ASiR-V and DLIR-L groups (P<0.05). Conclusion Compared with the standard 50% ASiR-V image, DLIR-M and DLIR-H reconstruction algorithms can effectively improve the image quality for the anorectal junction in CT imaging. The higher-intensity DLIR results in better image quality and stronger ability to display fine structures, which can provide more evidences for clinical precision evaluation and personalized precision treatment.

相似文献/References:

[1]蔡爱楠,随力,王君. CT/MRI双模态造影剂的制备及研究进展[J].中国医学物理学杂志,2018,35(2):219.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.020]
　CAI Ainan,SUI Li,WANG Jun. Preparation and research progress of CT/MRI bimodal contrast agent[J].Chinese Journal of Medical Physics,2018,35(8):219.[doi:DOI:10.3969/j.issn.1005-202X.2018.02.020]
[2]窦益腾,朱新进,夏俊,等. 64排螺旋CT对结节性甲状腺肿的诊断价值[J].中国医学物理学杂志,2018,35(3):303.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.011]
　DOU Yiteng,ZHU Xinjin,XIA Jun,et al. Diagnostic value of 64-slice spiral CT for nodular goiter[J].Chinese Journal of Medical Physics,2018,35(8):303.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.011]
[3]倪晓雷,陈榕钦,柏朋刚,等. 图像引导下宫颈癌容积旋转调强放疗中膀胱和直肠实际受照剂量评估[J].中国医学物理学杂志,2019,36(2):142.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.004]
　NI Xiaolei,CHEN Rongqin,BAI Penggang,et al. Evaluation of actual doses to bladder and rectum during image-guided volumetric modulated arc therapy for cervical cancer[J].Chinese Journal of Medical Physics,2019,36(8):142.[doi:DOI:10.3969/j.issn.1005-202X.2019.02.004]
[4]何重阳,文明.CT在外突性子宫肌瘤与卵巢性索间质肿瘤的鉴别诊断价值[J].中国医学物理学杂志,2020,37(5):589.[doi:10.3969/j.issn.1005-202X.2020.05.011]
　HE Chongyang,WEN Ming.Diagnostic value of CT in outprojecting uterine leiomyoma and ovarian sex cord-stromal tumor[J].Chinese Journal of Medical Physics,2020,37(8):589.[doi:10.3969/j.issn.1005-202X.2020.05.011]
[5]黄勃,王志.后踝撞击综合征MRI和CT诊断价值分析[J].中国医学物理学杂志,2020,37(6):730.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.014]
　HUANG Bo,WANG Zhi.Diagnostic values of MRI and CT in posterior ankle impingement syndrome[J].Chinese Journal of Medical Physics,2020,37(8):730.[doi:DOI:10.3969/j.issn.1005-202X.2020.06.014]
[6]刘胜,范承武.CT诊断重症肺炎支原体肺炎的价值[J].中国医学物理学杂志,2021,38(7):842.[doi:DOI:10.3969/j.issn.1005-202X.2021.07.010]
　LIU Sheng,FAN Chengwu.Value of CT in diagnosing severe Mycoplasma pneumoniae pneumonia[J].Chinese Journal of Medical Physics,2021,38(8):842.[doi:DOI:10.3969/j.issn.1005-202X.2021.07.010]
[7]李娜,曾珉,刘俊宏.脑利钠肽水平联合肺动脉CT评估老年心力衰竭患者预后的价值[J].中国医学物理学杂志,2021,38(9):1119.[doi:10.3969/j.issn.1005-202X.2021.09.013]
　LI Na,ZENG Min,LIU Junhong.Value of brain natriuretic peptide level combined with pulmonary artery CT in evaluating theprognosis of elderly patients with heart failure[J].Chinese Journal of Medical Physics,2021,38(8):1119.[doi:10.3969/j.issn.1005-202X.2021.09.013]
[8]张白霖,黄泽裕,戴振晖,等.直肠充盈对直肠壁CT影像组学特征的影响[J].中国医学物理学杂志,2022,39(2):194.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.011]
　ZHANG Bailin,Huang Zeyu,DAI Zhenhui,et al.Effects of rectal filling on CT radiomics features of rectal wall[J].Chinese Journal of Medical Physics,2022,39(8):194.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.011]
[9]周美君,朱晟.高频超声与CT在良恶性浅表淋巴结鉴别诊断中的应用价值[J].中国医学物理学杂志,2022,39(4):464.[doi:DOI:10.3969/j.issn.1005-202X.2022.04.013]
　ZHOU Meijun,ZHU Sheng.Value of high-frequency ultrasound and CT in the differential diagnosis of benign and malignant superficial lymph node lesions[J].Chinese Journal of Medical Physics,2022,39(8):464.[doi:DOI:10.3969/j.issn.1005-202X.2022.04.013]
[10]郭艺,杜秋晨,吴朦朦,等.基于轻量级神经网络的新冠肺炎CT新型识别技术[J].中国医学物理学杂志,2022,39(10):1263.[doi:DOI:10.3969/j.issn.1005-202X.2022.10.014]
　GUO Yi,DU Qiuchen,WU Mengmeng,et al.COVID-19 recognition technology based on lightweight neural network[J].Chinese Journal of Medical Physics,2022,39(8):1263.[doi:DOI:10.3969/j.issn.1005-202X.2022.10.014]

备注/Memo

备注/Memo:
【收稿日期】2024-04-02 【基金项目】国家自然科学基金（82371655） 【作者简介】乔文俊，硕士，研究方向：CT扫描技术应用，E-mail: 1257826529@qq.com 【通信作者】许乙凯，主任医师，博士生导师，研究方向：医学影像诊断及分子影像，E-mail: yikaivip@163.com
更新日期/Last Update: 2024-08-31