Keywords: System Imperfections, System Imperfections: Measurement & Correction, pre-emphasis, deep learning
Motivation: Gradient waveform fidelity is critical in MRI for accurate signal encoding, but system imperfections often lead to waveform deviations that compromise image quality.
Goal(s): To develop a deep learning-based method for accurate gradient waveform pre-emphasis that compensates for these system imperfections.
Approach: A bi-directional LSTM network iteratively learns the system’s gradient response, optimizing input waveforms for high-fidelity output. This was validated using 3D Cones sequences on phantoms and human brains.
Results: The output gradient waveform achieved near-perfect alignment with the ideal waveform. Phantom and human brain images demonstrated enhanced spatial uniformity and reduced artifacts, showcasing improved MRI image quality.
Impact: This method enhances MRI image quality by reducing artifacts and improving spatial accuracy through gradient waveform pre-emphasis using deep learning.
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