深度学习图像重建方法对MRI吞吐量的影响
Anthony Yang1, Mark Finkelstein1, Clara Koo1
1From the Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029.
Radiology. Artificial intelligence
|March 20, 2024
概括
深度学习重建 (DLR) 算法显著减少了MRI扫描和室内时间,而基于DICOM的方法在临床实践中显示出比基于k空间的方法更大的效率增长.
科学领域:
- 放射学 放射学是指放射学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 深度学习重建 (DLR) 算法是MRI中的新兴技术.
- 评估其现实世界的临床效率对于采用至关重要.
研究的目的:
- 评估两个不同的DLR算法的对MRI检查效率的影响.
- 在多中心环境中比较基于DICOM和基于k空间的DLR方法.
主要方法:
- 在10台扫描仪上对7346次MRI检查进行了回顾性分析.
- 在DLR实施之前和之后的扫描和房间时间的比较.
- 使用Wilcoxon测试来比较DLR和非DLR组.
主要成果:
- 基于DICOM的DLR将扫描时间减少了高达53%,房间时间减少了高达41%.
- 基于k空间的DLR将扫描时间减少了高达27%,但没有显著影响房间时间.
- 根据检查类型,效率的提高有所不同.
结论:
- 在临床环境中,DLR方法可以减少MRI扫描和室内时间.
- DLR的有效性是异质的,取决于特定的算法和检查类型.
- 在DLR整合之前,建议仔细评估机构案例组合.
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