Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques

Objective Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed. Methods First, fast and high-quality unshielded imaging is achieved using active electromagnetic shielding an...

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Veröffentlicht in:	Magma (New York, N.Y.) Jg. 38; H. 2; S. 253 - 269
Hauptverfasser:	Li, Lei, He, Qingyuan, Wei, Shufeng, Wang, Huixian, Wang, Zheng, Yang, Wenhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.04.2025
Schlagworte:	Algorithms Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Computer Appl. in Life Sciences Deep Learning Equipment Design Health Informatics Humans Image Processing, Computer-Assisted - methods Imaging Magnetic Resonance Imaging - instrumentation Magnetic Resonance Imaging - methods Medicine Medicine & Public Health Phantoms, Imaging Radiology Research Article Signal-To-Noise Ratio Solid State Physics Low-field MRI Deep learning Super-resolution Cross-field Image processing
ISSN:	1352-8661, 1352-8661
Online-Zugang:	Volltext
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