Meeting Banner
Abstract #2937

A deep learning-based background field removal method for brains containing high susceptibility sources

Xuanyu Zhu1, Yang Gao1, Feng Liu1, ‪Stuart Crozier‬1, and Hongfu Sun1
1University of Queensland, Brisbane, Australia


Background field removal (BFR) is a critical step in quantitative susceptibility mapping (QSM). Eliminating the background field in brains containing high susceptibility sources, such as intracranial hemorrhages, is challenging due to the relatively large scale of the local field induced from these sources. This study proposed a new deep learning-based method, "BFRnet", and compared it with several conventional BFR methods in processing two simulated and two in vivo brain datasets. The BFRnet method was effective in background field removal for acquisitions of arbitrary orientations and performed significantly better than other methods in the regions with high susceptibility sources.

This abstract and the presentation materials are available to members only; a login is required.

Join Here