Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Model-based imaging, gradient coil free system
Motivation: Using permanent magnets for polarization and encoding in a low-field body-part dedicated MRI enhances its portability and lowers the cost. However, the spatial encoding magnetic field decays over time which degrades image quality.
Goal(s): AI-based correction of the decay without labor-intensive mapping of the magnetic field or additional hardware.
Approach: Physics-encoded Neural Network (PeNN), which contains a model-based image reconstruction concept, is proposed to correct the decay. The gradients for the encoded physics are computed algorithmically instead of mathematically for backpropagation.
Results: PeNN can model the decay accurately, leading to a 78% improvement of structural similarity index measure in both simulations and experiments.
Impact: PeNN effectively and efficiently corrects the spatial encoding magnetic field in a portable MRI system using model-based imaging, saving regular labor-intensive mapping of magnetic fields. The proposed PeNN does not necessarily require the physics concept to be differentiable for backpropagation.
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