SNR Is Not Only About S

Ed X Wu¹
¹Department of Electrical and Electronic Engineering, University of Hong Kong, China

Synopsis

Keywords: Physics & Engineering: Low-field MRI, Image acquisition: Machine learning, Image acquisition: Sequences

The ongoing development of ultra-low-field (ULF) (<0.1T) and low-field (LF) (0.1-0.5T) MRI technologies will enable patient-centric and site-agnostic MRI scanners to fulfill the unmet clinical needs across various healthcare corners. This presentation aims to outline several approaches to mitigate the MR signal reduction problem intrinsic to ULF (and LF) MRI.

MRI has revolutionized modern healthcare. However, existing mainstream high-field MRI technology is not readily accessible for point-of-care applications or in mid- and low-income countries. The ongoing development of ultra-low-field (ULF) (<0.1T) and low-field (LF) (0.1-0.5T) MRI technologies will enable patient-centric and site-agnostic MRI scanners to fulfill the unmet clinical needs across various healthcare corners.
This presentation aims to outline several approaches to mitigate the MR signal reduction problem intrinsic to ULF (and LF) MRI by:
(i) time-efficient acquisition sequences for various protocols/contrasts;
(ii) superconducting RF coil for improved detection sensitivity;
(iii) electromagnetic interference elimination for RF shielding-free MRI; and
(iv) powerful artificial intelligence strategies for quality image formation.

Acknowledgements

This study is supported in part by Hong Kong Research Grant Council (R7003-19,HKU17112120, HKU17127121 and HKU17127022) and Lam Woo Foundation.

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Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)