通过人工智能降低加多利尼姆对比度
Brian Tsui1, Evan Calabrese2, Greg Zaharchuk3
1Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Journal of magnetic resonance imaging : JMRI
|October 31, 2023
概括
机器学习可以减少或消除MRI扫描中的gadolinium对比度,最大限度地降低诸如神经系统性纤维化和gadolinium沉积等风险. 这篇评论探讨了人工智能.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 神经成像是一种神经成像.
背景情况:
- 加多对比剂对MRI至关重要,特别是在神经成像中,通过检测血脑屏障的破坏来帮助诊断炎症,感染和瘤疾病.
- 然而,加多对比剂的使用与重大风险有关,包括质性全身纤维化,大脑和骨组织中的加多沉积以及类似过敏的反应.
- 计算机硬件和人工智能的发展为减轻这些风险提供了机会.
研究的目的:
- 审查加多对比剂在MRI中的临床应用,重点是神经成像.
- 要总结与加多对比剂管理相关的已知风险和不良影响.
- 探索目前最先进的机器学习 (ML) 方法来减少或消除神经成像中的加多对比剂量,并讨论它们的局限性.
主要方法:
- 临床用途,风险和ML应用的文献综述与MRI中的加多对比相关.
- 专注于将ML技术应用于神经成像的研究,以减少对比剂的使用.
- 分析当前的ML方法,它们的有效性,以及在加多减少的背景下限制.
主要成果:
- 加多对比剂对于通过MRI诊断各种神经疾病至关重要.
- 确定的风险包括NSF,加多沉积和过敏反应.
- 新兴的ML技术在降低或消除加多对比度,同时保持诊断质量方面显示出前景.
结论:
- 机器学习提供了一种潜在的解决方案,以最大限度地降低MRI中与gadolinium对比相关的风险.
- 需要对ML算法进行进一步的研究和验证,以便在神经成像中广泛采用临床技术.
- 人工智能驱动的方法可以通过减少对加多对比剂的依赖来提高MRI安全性和可访问性.
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