摘要
目的 探讨深度学习重建算法在颌面部CT成像中的应用效果。方法选取因外伤、感染性病变、肿瘤性病变行颌面部CT成像患者56例,随机分为常规剂量组(A组)28例和低剂量组(B组)28例,对两组静脉期扫描数据分别进行50%迭代重建算法(ASIR-V 50%)、DLIR-M、DLIR-H重建,亚组分别命名为A_(AS-50%)、A_(DL-M)、A_(DL-H)、B_(AS-50%)、B_(DL-M)、B_(DL-H)。比较两组各亚组间图像噪声、信噪比(SNR)、对比噪声比(CNR)及主观图像质量评分差异。结果 B_(AS-50%)组的SD最高,A_(DL-H)组SD值最低,A组和B组DL-M和DL-H的SNR和CNR均高于ASIR-V 50%组,SD值均低于ASIR-V 50%,差异均有统计学意义(P均<0.05)。A_(DL-H)组分别与B_(DL-M)、B_(DL-H)组比较,差异均有统计学意义(P均<0.05),B_(DL-M)、B_(DL-H)组间比较,差异均有统计学意义(P均<0.05)。在辐射剂量降低33%时,DL-M和DL-H组主观评价仍高于ASIR-V 50%组,差异有统计学意义(P<0.05)。结论 深度学习重建算法用于颌面部CT成像可改善图像质量,有效降低辐射剂量。
Objective This study aims to explore the application of deep learning reconstruction algorithms in head and neck CT imaging.Methods A total of 56 patients undergoing facial CT imaging due to trauma,infectious lesions,or tumors were randomly divided into a standard dose group(Group A,28 patients)and a low dose group(Group B,28 patients).The venous phase scan data for both groups were reconstructed using the 50%iterative reconstruction algorithm(ASIR-V 50%),DLIR-M and DLIR-H,with subgroups named AAS-50%,ADL-M,ADL-H,BAS-50%,BDL-M,and BDL-H.Statistical differences in image noise,signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR),and subjective image quality scores between the subgroups were compared.Results The BAS-50%group exhibited the highest standard deviation(SD),while,the ADL-H group had the lowest SD value.The SNR and CNR of the DL-M and DL-H groups in both Group A and Group B were higher than those of the ASIR-V 50%group,and their SD values were lower,with all differences being statistically significant.Comparisons of the ADL-H group with the BDL-M and BDL-H groups showed statistically significant differences,as did the comparison between the BDL-M and BDL-H groups.Even with a 33%reduction in radiation dose,the subjective evaluations of the DL-M and DL-H groups remained higher than those of the ASIR-V 50%group.Conclusion This study demonstrates that deep learning reconstruction algorithms significantly improve image quality in head and neck CT imaging,while effectively reducing radiation doses.
作者
苏蕾
魏凯通
胡丽丽
梁晓雪
马雅晴
孙强
SU Lei;WEI Kaitong;HU Lili;LIANG Xiaoxue;MA Yaqing;SUN Qiang(Department of Radiology,The First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China;Oral and Maxillofacial Surgery,The First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China)
出处
《医学影像学杂志》
2025年第7期23-26,共4页
Journal of Medical Imaging
基金
河南省科技厅科技攻关项目(编号:232102310282)
河南省教育厅高校重点科研项目(编号:25A320070
24A320056)。
关键词
深度学习
体层摄影术
X线计算机
颌面部
图像重建
辐射剂量
Deep learning
Tomography,X-ray computed
Maxillofacial region
Image reconstruction
Image quality
Radiation dose
