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Background field removal (BFR) is a critical step required forsuccessful quantitative susceptibility mapping (QSM). However, eliminating thebackground field in brains containing significant susceptibility sources, suchas intracranial hemorrhages, is challenging due to the relatively large scaleof the field induced by these pathological susceptibility sources. Method: Thisstudy proposes a new deep learning-based method, BFRnet, to remove backgroundfield in healthy and hemorrhagic subjects. The network is built with thedual-frequency octave convolutions on the U-net architecture, trained withsynthetic field maps containing significant susceptibility sources. The BFRnetmethod is compared with three conventional BFR methods and one previous deeplearning method using simulated and in vivo brains from 4 healthy and 2hemorrhagic subjects. Robustness against acquisition field-of-view (FOV)orientation and brain masking are also investigated. Results: For bothsimulation and in vivo experiments, BFRnet led to the best visually appealingresults in the local field and QSM results with the minimum contrast loss andthe most accurate hemorrhage susceptibility measurements among all fivemethods. In addition, BFRnet produced the most consistent local field andsusceptibility maps between different sizes of brain masks, while conventionalmethods depend drastically on precise brain extraction and further brain edgeerosions. It is also observed that BFRnet performed the best among all BFRmethods for acquisition FOVs oblique to the main magnetic field. Conclusion:The proposed BFRnet improved the accuracy of local field reconstruction in thehemorrhagic subjects compared with conventional BFR algorithms. The BFRnetmethod was effective for acquisitions of titled orientations and retained wholebrains without edge erosion as often required by traditional BFR methods.

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources