高分辨率TOF-MRA使用基于压缩传感的深度学习图像重建来可视化晶状动脉:初步研究
Yuya Hirano1, Noriyuki Fujima2, Hiroyuki Kameda3
1Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
概括
与传统的CS-SENSE相比,用于压缩感应 (CS) 磁共振血管造影 (MRA) 的深度学习 (DL) 重建改善了晶状动脉 (LSA) 的可见性. 这种先进的技术可以在更高的加速度因子下保持图像质量.
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:
- 飞行时间 (TOF) 的MR血管造影 (MRA) 对于可视化内血管系统至关重要.
- 压缩传感 (CS) 技术加速MRA获取,但可能影响图像质量.
- 深度学习 (DL) 提供了在MRA中改进图像重建的潜力.
研究的目的:
- 为了评估基于CS的DL图像重建在TOF-MRA中对lenticulostriate动脉 (LSA) 可视化的有效性.
- 为了比较CS-DL重建的MRA与传统的CS-SENSE重建的图像质量.
主要方法:
- 五名健康志愿者接受了高分辨率的TOF-MRA,并进行了完整的抽样 (R因子=1).
- 使用CS-DL和传统的CS-SENSE在2,4和6的R因子上重建图像.
- 定量评估包括可见的LSA数量,LSA长度和正常化平均平方误差 (NMSE).
- 定性评估包括由两名放射科医生评估整体图像质量和外围LSA可见性.
主要成果:
- 与CS-SENSE相比,CS-DL重建显示了R因子4和6的可见LSA数量显著增加.
- 描绘的LSA的长度在R-factor 6.的CS-DL时显著更长.
- 在R因子4和6上,CS-DL产生了显著较低的NMSE值.
- 定性分析显示,通过CS-DL在各种R因子上显著提高整体图像质量和外围LSA可见性.
结论:
- CS-DL重建有效地保留和提高图像质量,以在TOF-MRA中描绘LSA,即使在R因子升高的情况下.
- 这种方法为加速MRA获取提供了传统CS-SENSE的有希望的替代方案.
- 基于DL的重建有可能提高神经血管成像中的诊断准确性.
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