| 周成,刘洋,邱迎伟,何代均,闫宇,罗敏,雷有缘.基于最邻近层自监督学习人工智能降噪用于泌尿系结石超低剂量CT[J].中国医学影像技术,2024,40(8):1249~1253 |
| 基于最邻近层自监督学习人工智能降噪用于泌尿系结石超低剂量CT |
| Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi |
| 投稿时间：2024-01-05 修订日期：2024-04-28 |
| DOI：10.13929/j.issn.1003-3289.2024.08.029 |
| 中文关键词: 尿路结石 体层摄影术,X线计算机 人工智能 前瞻性研究 |
| 英文关键词:urinary calculi tomography, X-ray computed artificial intelligence prospective studies |
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| 摘要点击次数: 843 |
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| 中文摘要: |
| 目的 观察基于最邻近层自监督深度学习人工智能(AI)降噪技术用于超低剂量CT(ULDCT)检查泌尿系结石的价值。方法 前瞻性对88例泌尿系结石患者行腹部低剂量CT(LDCT)和ULDCT扫描,计算有效剂量(ED)。随机将患者分入训练集(n=75)及测试集(n=13),以训练集ULDCT图像构建基于最邻近层自监督深度学习网络AI降噪系统,用于测试集ULDCT图像降噪;比较测试集AI降噪前、后ULDCT及LDCT图像质量,包括盲/无参考图像空间质量评估器(BRISQUE)评分、图像噪声(SDROI)及信噪比(SNR)。结果 相比LDCT扫描,88例腹部ULDCT扫描的管电流、容积CT剂量指数及剂量长度乘积均较低(P均＜0.05),ED下降约82.66%。对于测试集13例泌尿系结石患者,BRISQUE评分显示其降噪前ULDCT图像质量水平为LDCT图像的54.42%,而AI降噪后质量水平达到LDCT图像的95.76%;AI降噪后ULDCT图像及LDCT图像的SDROI均低于、而其SNR均高于降噪前ULDCT图像(校正P均＜0.05),且前二者间差异均无统计学意义(校正P均＞0.05)。结论 基于最邻近层自监督学习AI降噪技术可有效降低泌尿系结石ULDCT图像噪声、提高图像质量,有利于临床推广应用ULDCT。 |
| 英文摘要: |
| 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 (SDROI) 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 SDROI 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. |
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