提高图像质量和诊断性能,使用深度学习图像重建在100kVpCT肠道镜中,用于身体质量指数范围广泛的患者
Yan Luo1, Youfa Tang1, Suping Chen2
1Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
深度学习图像重建 (DLIR) 显著提高图像质量和诊断信心在CT肠道成像 (CTE) 在广泛的体重指数 (BMI). DLIR显示了改善肠道病变检测的潜力,特别是在经验较少的读者中.
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
- 胃肠道诊断 胃肠道诊断 胃肠道诊断
背景情况:
- 传统的自适应统计代重建-Veo (ASiR-V) 是广泛用于CT肠道学 (CTE).
- 深度学习图像重建 (DLIR) 提供了一种新的方法来提高医学成像中的图像质量.
- 评估DLIR在100kVp CTE中对各种体质指数 (BMI) 种群的疗效对于临床采用至关重要.
研究的目的:
- 评估DLIR与ASiR-V在100kVp CTE中的临床效用.
- 为了比较图像质量,诊断信心和肠道病变检测能力.
- 评估各种患者BMI的表现.
主要方法:
- 84名患者接受了100kVp双相CTE.
- 使用过后投影 (FBP),ASiR-V (30%,60%) 和DLIR (低,中,高水平) 来重建图像.
- 进行了定量图像分析 (SNR,CNR) 和定性评估 (图像质量,诊断信心,病变检测).
主要成果:
- 与FBP和ASiR-V相比,DLIR持续改善了信号与噪声比 (SNR) 和对比与噪声比 (CNR) (p < 0.001).
- DLIR表明相对CNR增加和BMI (r:0.307-0.506,p ≤0.005) 之间存在正相关性.
- 在初级读者中,DLIR-M显示出高于ASiR-V的诊断信心60% (p ≤ 0.018) 和对炎症病变的更高灵敏度.
结论:
- 在100kVp双相CTE中,DLIR有效地降低了噪音并提高了图像质量,适用于不同的BMI患者.
- DLIR-M提供卓越的图像质量和诊断信心,对检测肠道病变有潜在的好处.
- 需要进一步的研究来证实DLIR在改善肠道疾病临床决策方面的作用.
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