Keywords: Gray Matter, Quantitative Susceptibility mapping
Motivation: MRI signals have phase information from the GRE sequence, which reflects B0 field homogeneities.
Goal(s): Due to acquisition, the phase is converted from complex data, ranging from -π to π and causing visual discontinuities. However, previous learning-based approaches have difficulties processing 3D brain data directly.
Approach: In this study, we introduced an unsupervised refinement based on Deep Image Prior to enhance the performance of the pre-trained networks (PHU-DIP), and the inference were performed on one simulated and one in vivo brain.
Results: The PHU-DIP method corrected the misclassification regions from the pre-trained networks and exhibited the significant time-efficiency compared to conventional method.
Impact: The PHU-DIP provided a refinement scheme that help to improve the performance of a well-trained network. This technique could also be expanded onto other training modes and other pathological conditions.
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