Automating quality control in cardiac magnetic resonance: Artificial intelligence for discriminative assessment of planning and motion artifacts and real-time reacquisition guidance

Automating quality control in cardiac magnetic resonance: Artificial intelligence for discriminative assessment of planning and motion artifacts and real-time reacquisition guidance

Background: Accurate measurements from cardiovascular magnetic resonance (CMR) images require precise positioning of scan planes and elimination of motion artifacts from arrhythmia or breathing. Unidentified or incorrectly managed artifacts degrade image quality, invalidate clinical measurements, an...

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Detalhes bibliográficos
Main Authors: Hoi C. Cheung, Kavitha Vimalesvaran, Sameer Zaman, Michalis Michaelides, Matthew J. Shun-Shin, Darrel P. Francis, Graham D. Cole, James P. Howard
Formato: Artigo
Idioma:English
Publicado em: Elsevier 2024-01-01
Colecção:Journal of Cardiovascular Magnetic Resonance
Assuntos:
Artificial intelligence
Machine learning
Cardiac magnetic resonance
Quality control
Quality assessment
Convolutional neural networks
Acesso em linha:http://www.sciencedirect.com/science/article/pii/S1097664724010949

Internet

http://www.sciencedirect.com/science/article/pii/S1097664724010949

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