Department of Urology Surgery, The Third Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001 Hunan, China.
Department of Endocrine Nephrology, The Third Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001 Hunan, China.
Comput Math Methods Med. 2022 Jan 27;2022:5823720. doi: 10.1155/2022/5823720. eCollection 2022.
The objective of this study was to explore the accuracy of low-dosage computed tomography (CT) images based on the expectation maximization algorithm denoising algorithm (EM algorithm) in the detection and diagnosis of renal dysplasia, so as to provide reasonable research basis for accuracy improvement of clinical diagnosis of renal dysplasia. 120 patients with renal dysplasia in hospital were randomly selected as the research objects, and they were divided into two groups by random number method, with 60 patients in each group. The low-dosage CT images of patients in the control group were not processed (nonalgorithm group), and the low-dosage CT images of patients in the observation group were denoised using the EM algorithm (algorithm group). In addition, it was compared with the results of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients and the consistency with the results of the pathological diagnosis. The results were compared with those of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients. The results showed that the peak signal-to-noise ratio (PSNR) (15.9 dB) of the EM algorithm was higher than the regularized adaptive matching pursuit (RAMP) algorithm (1.69 dB) and the mean filter (4.3 dB) ( < 0.05). The time consumption of EM algorithm (21 s) was shorter than that of PWLS algorithm (34 s) and MS-PWLS algorithm (39 s) ( < 0.05). The diagnosis accuracy of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney was obviously higher in the algorithm group than the control group ( < 0.05), which were 66.67% vs. 90%, 60% vs. 88.89%, 71.42% vs. 100%, and 60% vs. 88.89%, respectively. The incidence of hypertension in patients with autosomal dominant polycystic kidney disease (ADPKD) (56.77%) was much higher than that of the other diseases ( < 0.05). After denoising by the EM algorithm, low-dosage CT image could improve the diagnostic accuracy of several types of renal dysplasia except ADPKD, showing certain clinical application value. In addition, ADPKD was easy to cause hypertension.
本研究旨在探讨基于期望最大化算法去噪算法（EM 算法）的低剂量 CT 图像在肾发育不良检测和诊断中的准确性，为临床肾发育不良诊断准确性的提高提供合理的研究依据。随机选取我院 120 例肾发育不良患者作为研究对象，采用随机数字法将其分为两组，每组 60 例。对照组患者的低剂量 CT 图像不进行处理（非算法组），观察组患者的低剂量 CT 图像采用 EM 算法进行去噪（算法组）。此外，与综合诊断（金标准）结果进行比较，分析两组患者的诊断准确率及与病理诊断结果的一致性。将结果与综合诊断（金标准）结果进行比较，分析两组患者的诊断准确率。结果表明，EM 算法的峰值信噪比（PSNR）（15.9dB）高于正则化自适应匹配追踪（RAMP）算法（1.69dB）和均值滤波器（4.3dB）（ < 0.05）。EM 算法的时间消耗（21s）短于 PWLS 算法（34s）和 MS-PWLS 算法（39s）（ < 0.05）。算法组中单侧肾发育不良、单侧肾缺如、马蹄肾和重复肾的诊断准确率明显高于对照组（ < 0.05），分别为 66.67%比 90%、60%比 88.89%、71.42%比 100%和 60%比 88.89%。常染色体显性多囊肾病（ADPKD）（56.77%）患者高血压发生率明显高于其他疾病（ < 0.05）。经过 EM 算法去噪后，低剂量 CT 图像可提高除 ADPKD 以外的几种肾发育不良的诊断准确率，具有一定的临床应用价值。此外，ADPKD 易引起高血压。
