Gearhart Addison, Anjewierden Scott, Buddhe Sujatha, Tandon Animesh
Department of Cardiology, Seattle Children's Hospital, Seattle, WA 98105, USA.
Department of Pediatrics, University of Washington, Seattle, WA 98195, USA.
Children (Basel). 2025 Mar 26;12(4):416. doi: 10.3390/children12040416.
Cardiovascular magnetic resonance (CMR) imaging is essential for the management of congenital heart disease (CHD), due to the ability to perform anatomic and physiologic assessments of patients. However, CMR scans can be time-consuming to perform and analyze, creating roadblocks to broader use of CMR in CHD. Recent publications have shown artificial intelligence (AI) has the potential to increase efficiency, improve image quality, and reduce errors. This review examines the use of AI techniques to improve CMR in CHD, by focusing on deep learning techniques applied to image acquisition and reconstruction, image processing and reporting, clinical use cases, and future directions.
心血管磁共振(CMR)成像对于先天性心脏病(CHD)的管理至关重要,因为它能够对患者进行解剖学和生理学评估。然而,CMR扫描的执行和分析可能很耗时,这为CMR在CHD中的更广泛应用设置了障碍。最近的出版物表明,人工智能(AI)有提高效率、改善图像质量和减少错误的潜力。本综述通过关注应用于图像采集与重建、图像处理与报告、临床应用案例及未来方向的深度学习技术,探讨了利用AI技术改善CHD的CMR成像。