Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

CT体外预测泌尿系结石易碎性的研究

目的：探讨CT是否可以被用来预测泌尿系结石的易碎性。方法：选择40例开放手术取出尿路结石标本进行气压弹道碎石，结石体积采用容器盛水法测量，体积0．25—8mL。碎石前行CT扫描了解结石CT值及影像学特征，收集碎石屑测定成分，CT值与碎石次数进行相关分析。结果：CT值与每毫升碎石次数进行相关分析，Person相关系数0．886，P=0．000，认为结石CT值与碎石次数之间存在正相关关系，而且相关性很高。结论：结石的CT值及影像学特征是预测其易碎性的重要手段。

泌尿系结石CT值与其硬度值的关系

目的:分析离体泌尿系结石平均CT值与结石硬度的关系,探讨CT检查预测泌尿系结石硬度的可能性。方法:选择2002年3月至2004年3月间60例尿路结石患者手术取出的结石进行CT扫描测定平均CT值,以硬度计测定结石的硬度,对结石标本的平均CT值和硬度值进行直线相关性分析。结果:60枚结石平均CT值与其硬度值的Pearson相关系数r=0.661,P<0.005。结论:泌尿系结石平均CT值与结石硬度呈正相关,对碎石治疗具有一定的指导意义。

CT体外预测泌尿系结石硬度的研究

目的探讨CT预测泌尿系结石硬度的可行性,为临床选择合理的治疗方法提供科学依据。方法选择2002年3月-2004年3月60例住院行开放手术的尿路结石患者,手术取出结石后测定平均CT值,以硬度计测定结石的硬度,将结石标本的平均CT值与其硬度值进行直线相关性分析。结果60枚结石平均CT值与其硬度值的Person相关系数为0.661,P=0.000。认为结石平均CT值与其硬度值间呈直线相关关系。结论结石平均CT值与结石硬度呈正相关关系,提示混合性结石CT值越高,其硬度就越大,可能所需碎石次数就越多。

CT能预测泌尿系结石易碎性吗?

目的：探讨CT是否可以被用来预测泌尿系结石的易碎性。方法：选择40例开放手术取出尿路结石标本进行气压弹道碎石，结石体积0．25～8ml，碎石前行CT扫描了解结石CT值及影像学特征，收集碎石屑测定成分，CT值与碎石次数进行相关分析。结果：CT值与每ml碎石次数进行相关分析，Person相关系数0．886，P=0．000，认为结石CT值与碎石次数之间存在正相关关系，而且相关性很高。结论：结石的CT值及影像学特征是预测其易碎性的重要手段。

CT和AI技术预测泌尿系结石成分的研究进展

尿石症是泌尿外科最常见的一种疾病,且患病率逐年增长。因此,对结石的诊断、治疗、预防成为泌尿外科医生主要的工作。结石成分的分析结果通常在术后才能获得,这对个性化治疗方案的选择无明显益处。过去,国内外学者主要使用结石的CT值、有效原子序数或双能量比等参数在体内外区分结石成分。近5年,越来越多的研究者利用分类算法、神经网络或预测模型判断结石的成分及性质,其预测的效率及准确性均较前显著提升。本文综述了电子计算机断层扫描和人工智能技术预测泌尿系结石成分的研究进展。