Quantitative Susceptibility Mapping (QSM) can quantitatively estimate tissue magnetic susceptibility, which enables differentiation of diamagnetic calcifications and paramagnetic hemorrhages. The translation of QSM into clinical practice faces technical implementation challenges, particularly the QSM inversion process. In the clinical practice current QSM post-processing techniques are constrained due to large slick thicknesses, which result in compromised background field removal and streaking artifacts in QSM images. To address these limitations, here we a apply a deep-learning-based QSM pipeline, including: (1) a 2D neural network to construct brain masks, (2) a background field removal deep neural network reveal local tissue fields, and (3) a QSM inversion deep neural network. Nine patients with stroke were scanned using a clinical susceptibility-weighted MR protocol were used to demonstrate that the proposed clinically viable QSM workflow can effectively detect microbleeds and differentiate calcifications from hemorrhages.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords