深度学习重建算法改善腹部门静脉期CT图像质量的应用价值  

The application value of deep learning imaging reconstruction algorithms for improving the quality of abdominal portal venous phase CT images

作　　者：朱永琪 王振华 陈大治[1] 石晓萌 吴金花[1] 戴志军[1] ZHU Yong-qi;WANG Zhen-hua;CHEN Da-zhi;SHI Xiao-meng;WU Jin-hua;DAI Zhi-jun(Medical Imaging Center,People's Hospital of Ningxia Hui Autonomous Region,Yinchuan 750000,China;GE(China)CT Research Center,Shanghai 200100,China)

机构地区：[1]宁夏自治区人民医院医学影像中心,宁夏银川750000 [2]GE(中国)CT影像研究中心,上海200100

出　　处：《中国临床医学影像杂志》2024年第7期498-502,共5页Journal of China Clinic Medical Imaging

摘　　要：目的:探究深度学习重建算法(Deep learning image reconstruction,DLIR)、传统滤波反投影(Filered back-projection,FBP)及自适应迭代重建算法(Adaptive statistical iterative reconstruction-veo,ASIR-V)对改善腹部门静脉期CT图像质量差异及临床获益。方法:前瞻性纳入45例行腹部增强CT扫描患者,其中包括18例肝硬化失代偿期患者,对门静脉期图像进行FBP、30%ASIR-V、80%ASIR-V及DLIR-H重建,并测量比较4组重建图像肝脏、脾脏、脾静脉、门静脉及左右支CT值、噪声、信噪比(Signal-to-noise ratio,SNR)及对比信噪比(Contrast-to-noise ratio,CNR);比较各重建算法图像主观评价,包括18例肝硬化失代偿期患者交通支血管。结果:4组重建算法图像CT值无统计学差异(P>0.05),噪声、SNR、CNR均有统计学差异,两两比较FBP与30%ASIR-V,80%ASIR-V与DLIR-H在CNR、SNR值中无统计学差异(校正P<0.008),80%ASIR-V与DLIR-H算法在SD值无统计学差异(校正P<0.008),余均有统计学差异。主观评价DLIR图像整体质量、对比度、失真伪影与其他各组有显著性差异(校正P<0.008),仅图像噪声与80%ASIR-V无显著性差异(校正P≥0.008)。DLIR交通支血管轮廓、清晰度与各组有显著性差异(校正P<0.008),噪声与80%ASIR-V无显著性差异(校正P≥0.008)。结论:DLIR算法降低腹部CT图像噪声,改善图像质量具有优势,尤其是肝硬化失代偿期微小血管结构,该重建算法可能为患者的精准诊断、风险评估提供更多信息。Objective:To compare the differences in image quality and clinical benefits of deep learning image reconstruction(DLIR),filtered back-projection(FBP),and adaptive statistical iterative reconstruction-veo(ASIR-V)in abdominal portal venous phase CT images.Methods:Forty-five patients who underwent abdominal contrast-enhanced CT scans were enrolled,and18 cases with decompensated liver cirrhosis were contained.The portal venous phase images were reestablished by FBP,30%ASIR-V,80%ASIR-V,and DLIR-H algorithms.The CT values and noise of the liver,spleen,splenic vein,portal vein,and left and right branches in each reconstructed image,as well as the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were measured and compared.The subjective evaluations of each reconstructed image,including collateral vessels in 18 cases with decompensated liver cirrhosis.Results:There was no statistically significant difference in CT values among the four reconstructed image groups(P>0.05).However,there were statistically significant differences in noise,SNR,and CNR.Comparisons between FBP and 30%ASIR-V,as well as 80%ASIR-V and DLIR-H,showed no statistically significant differences in CNR and SNR values(adjusted P<0.008).There were no statistically significant differences in SD values between 80%ASIR-V and DLIR-H algorithms(adjusted P<0.008),but differences were observed in other comparisons.Subjective evaluation showed statistically significant differences in overall quality,contrast,and distortion/artifacts of DLIR images compared to other groups(adjusted P<0.008).Only image noise in DLIR did not show significant differences compared to 80%ASIR-V(adjusted P≥0.008).The delineation of vascular structures and clarity in DLIR images showed significant differences compared to other groups(adjusted P<0.008),with no significant differences in noise compared to 80%ASIR-V.Conclusion:The DLIR algorithm offers advantages in reducing noise and improving image quality of abdominal CT images,particularly in the visualization of small vascular

关 键 词：腹部 肝硬化 体层摄影术 X线计算机 

分 类 号：R575.2[医药卫生—消化系统] R814.42[医药卫生—内科学]