图像质量和病变检测能力深度学习加速的T2加权迪克森成像的椎脊柱的图像质量和病变检测能力
Geojeong Seo1, Sun Joo Lee2, Dae Hyun Park3
1Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Skeletal radiology
|May 26, 2023
概括
深度学习加速迪克森 (DL-Dixon) 成像显著减少椎脊椎MRI采集时间. 与常规的迪克森成像相比,这种先进的技术保持了出色的图像质量和病变检测.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 常规的迪克森成像是椎脊椎MRI的标准.
- 深度学习 (DL) 为加速成像技术提供了潜力.
- 验证DL加速方法对于临床采用至关重要.
研究的目的:
- 为了比较深度学习加速的迪克森 (DL-Dixon) 影像与常规的迪克森影像用于椎脊椎MRI.
- 评估主观图像质量和病变检测能力.
- 评估对获取时间和图像文物的影响.
主要方法:
- 50名患者接受了常规的迪克森和DL-Dixon椎脊椎MRI.
- 分析了获取参数和非统一性 (NU) 值.
- 两位放射科医生独立评估图像质量和病变检测,并计算了互读器和互方法协议.
主要成果:
- DL-Dixon 减少了 23.76% 的获取时间,并略高了 NU 值.
- 解剖结构 (脊髓,磁盘边缘等) 的高可见性. 与DL-Dixon一起观察到的情况.
- 对各种病理的方法间一致性很大,几乎是完美的,对前狭窄症的读者间一致性得到了改进.
结论:
- 用DL加速的迪克森成像显著减少了宫脊椎的MRI采集时间.
- DL-Dixon提供与传统的迪克森序列相美的图像质量.
- 在DL-Dixon和常规的迪克森成像之间没有发现病变检测能力的显著差异.
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