深度学习重建提高腹部能谱CT图像质量和病灶诊断的可行性研究  

Feasibility Study of Deep Learning Reconstruction To Improve Dual-energy CT Image Quality and Lesion Diagnosis

作　　者：褚冰倩 沈艺 宋建[1] 刘斌[1] CHU Bing-qian;SHEN Yi;SONG Jian;LIU Bin(Department of Radiology,the First Affiliated Hospital of Anhui Medical University,Hefei 230022,Anhui Province,China)

机构地区：[1]安徽医科大学第一附属医院放射科,安徽合肥230032

出　　处：《中国CT和MRI杂志》2024年第6期154-157,共4页Chinese Journal of CT and MRI

摘　　要：目的 与常规多模型迭代重建(adaptive statistica literative reconstruction Veo,ASIR-V)相比,评估深度学习图像重建(deep learning image reconstruction,DLIR)提高能谱CT单能量图像的图像质量和病灶诊断检出率的可行性。方法随机纳入65例完成腹部能谱CT扫描的患者,对病人的门脉期扫描数据分别增加在薄层(1.25mm)层厚下的ASIR-V40%、DLIR-M(中度)、DLIR-H(高度)三种重建方式进行重建。获取70keV条件下常规5m m的A SIR-V40%及薄层(1.25mm)的ASIR-V40%、DLIR-M、DLIR-H共4组的门脉期图像。对4组图像的肝脏、脾脏、竖脊肌及背部脂肪进行CT值、SD值的测量,并计算相应的SNR及CNR。此外,计算薄层(1.25m m)的3组数据中肝脏病灶在门脉期图像的检出率。图像质量主观评分及病灶的诊断信心由2名有着丰富阅片经验的放射科医师进行评分。结果对于能谱扫描下70keV的薄层(1.25mm)图像,DLIR-M组和DLIR-H组较ASIR-V4 0%组的肝脏、脾脏、竖脊肌及背部脂肪的SD值更低,SNR、CNR更高(P均<0.001),且三组数据的病灶检出率一致,而DLIR-M组和DLIR-H组的诊断信心和图像质量主观评分均高于ASIR-V40%组,其中DLIR-H组分数最佳。此外,70keV的薄层(1.25mm)DLIR-H组图像的SD值、CNR较70keV的常规5mmASIR-V40%组比较无明显统计学差异(P=0.211,0.358,0.208,0.052)。结论和常规的多模型迭代重建算法(ASIR-V)相比,用深度学习重建算法(DLIR)获得的能谱单能量图像能在保证理想的诊断性能的前提下,进一步降低腹部CT的图像噪声,获得更好的图像质量和更高的病灶诊断信心。同时,与70keV+ASIR-V40%相比,70keV+DLIR-H能够在相似图像噪声的情况下实现更薄层厚图像重建。Objective To evaluate the feasibility of deep learning image reconstruction(DLIR) on improving image quality and lesion diagnosis using virtual monochromatic spectral images in abdominal dual-energy CT(DECT),compared with adaptive statistical iterative reconstruction-V(ASIR-V).Methods Sixty-five patients who completed abdominal dual-energy CT scan were randomly included and reconstructed by ASIR-V40%,DLIR-M(moderate),and DLIR-H(height).The portal images of conventional 5mm ASIR-V40% at 70 keV and ASIR-V40%,DLIR-M and DLIR-H of thinner layer(1.25mm) at 70 keV.Measu re CT attenuation,standa rd deviation(SD) value,signal-to-noise ratio(SNR),and noise to contrast(CN R)of liver,spleen,vertical spine,and intramuscular fat.The number of liver lesions in the portal stage images of thinner layer groups was counted.Image quality and diagnosis confidence were subjectively evaluated by two radiologists with extensive experience.Results For the 1.25mm images with 70keV,DLIR-M and DLIR-H had lower SD,higher SNR and CNR,and better subjective image quality than ASIR-V40% with consistent lesion detection rates and DLIR-H performed the best(all P<0.001) There was no significant statistical difference in the SD value,CNR between 5mm ASIR-V40% group at 70keV and 1.25mm DLIR-H group at 70 keV(P=0.211,0.358,0.208,0.052).Conclusion Compared with the conventional ASIR-V,deep learning reconstruction algorithm(DLIR) with DECT can further reduce the image noise of abdominal CT,obtain better image quality and higher confidence in lesion diagnosis.Moreover,DLIR-H at 70 keV can achieve thinner thickness image reconstruction with similar image noise compared with ASIR-V40% at 70 keV.

关 键 词：深度学习图像重建算法 能谱成像 门脉期 图像质量 病灶诊断 

分 类 号：R814.42[医药卫生—影像医学与核医学] R572[医药卫生—放射医学] R575[医药卫生—临床医学] R543