Artificial Intelligence for Neuro MRI Acquisition: A Review
Hongjia Yang, Guanhua Wang, Ziyu Li, Haoxiang Li, Jialan Zheng, Yuxin, Hu, Xiaozhi Cao, Congyu Liao, Huihui Ye, Qiyuan Tian
TL;DR
This review explores how artificial intelligence is revolutionizing neuro MRI acquisition by improving planning, sequence design, and artifact correction, thereby enhancing efficiency and clinical impact.
Contribution
It provides a comprehensive overview of recent AI-based methods in neuro MRI acquisition, highlighting technological advances and clinical implications.
Findings
AI improves MRI acquisition efficiency
Enhanced artifact correction techniques
Potential risks and clinical impact discussed
Abstract
Magnetic resonance imaging (MRI) has significantly benefited from the resurgence of artificial intelligence (AI). By leveraging AI's capabilities in large-scale optimization and pattern recognition, innovative methods are transforming the MRI acquisition workflow, including planning, sequence design, and correction of acquisition artifacts. These emerging algorithms demonstrate substantial potential in enhancing the efficiency and throughput of acquisition steps. This review discusses several pivotal AI-based methods in neuro MRI acquisition, focusing on their technological advances, impact on clinical practice, and potential risks.
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