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深度学习重建与腹部CT标准重建:BMI的影响

Huixia Wang¹, Songwei Yue¹, Nana Liu¹

¹The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1, Eastern Jianshe Road, Zhengzhou, 450052, Henan Province, China.

European radiology

|August 31, 2023

概括

深度学习图像重建 (DLIR) 显著提高腹部CT扫描图像质量和病变在各种身体质量指数 (BMI) 的显著性. 中等DLIR设置 (DM) 为整体图像质量和病变检测提供了最佳平衡.

科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
计算机断层扫描 (CT) 是一种计算机断层扫描.

背景情况:

在腹部CT中使用的标准图像重建算法在优化图像质量和病变显著性方面可能受到限制,特别是在不同的身体质量指数 (BMI) 中.
深度学习图像重建 (DLIR) 为改善腹部CT成像诊断精度提供了潜在的进步.

研究的目的:

评估和比较DLIR的图像质量和病变明显性与腹部对比增强CT扫描中的传统方法 (过后投影和代重建) 相比.
评估DLIR在广泛的BMI中的性能.

主要方法:

112名参与者接受了增强对比度的腹部CT扫描,分为三个BMI组 (≤23.9,24-28.9,≥29 kg/m2).
使用过后投影 (FBP),自适应统计代重建 (IR) 和DLIR (低,中,高水平) 重建图像.
评估了主观 (噪音,人工物,质量,显眼性) 和定量 (CT减弱,噪音,CNR) 参数.

主要成果:

在所有BMI组中,DLIR (中高水平) 与FBP和IR相比,在质量和数量上表现优越.
中等DLIR水平 (DM) 产生了最高的主观图像质量和损伤明显度得分.
与FBP相比,DLIR对图像质量和病变明显性的改善在较低的BMI组中更为明显.

结论:

在腹部CT扫描中,DLIR有效地提高了各种BMI患者的图像质量和肝损伤明显性.
建议使用中等DLIR设置 (DM) 作为平衡图像质量和病变检测的最佳选择.
DLIR代表了腹部CT解释的重大进步,改善了不同患者身体类型的诊断信心.

关键词:

深度学习是一种深度学习.图像处理,计算机辅助图像处理.辐射剂量辐射剂量放射,腹部,腹部的放射.断层扫描,X射线计算成像

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