School of Biomedical Engineering, Tsinghua University, Beijing, China.
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
MAGMA. 2024 Jul;37(3):383-396. doi: 10.1007/s10334-024-01182-7. Epub 2024 Jun 26.
To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts.
A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.
The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency.
The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.
综述人工智能(AI)在提高神经影像学中 MRI 采集工作流程的效率和吞吐量方面的最新进展,包括规划、序列设计和采集伪影校正。
对基于 AI 的神经 MRI 采集的最新方法进行了全面分析。该研究侧重于关键技术进步、对临床实践的影响以及这些方法相关的潜在风险。
结果表明,基于 AI 的算法对 MRI 采集过程具有显著的积极影响,提高了效率和吞吐量。确定了特定的算法在优化采集步骤方面特别有效,据报道,工作流程效率有所提高。
该综述强调了 AI 在神经 MRI 采集中的变革潜力,强调了技术进步和临床益处。然而,它还讨论了潜在的风险和挑战,提出了未来研究的领域,以减轻这些担忧并进一步加强 AI 在 MRI 采集中的集成。