Keywords: New Trajectories & Spatial Encoding Methods, Machine Learning/Artificial IntelligenceEfficient k-space trajectories are crucial for accelerated MRI. SNOPY proposes a generalized gradient-based method for optimizing 3D non-Cartesian sampling patterns. The algorithm can simultaneously tune multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method applies to various scenarios, such as optimizing gradient waveforms or optimizing rotation angles of radial/spiral trajectories. We adopted several computational strategies to address this non-convex and large-scale problem. Various simulated and in-vivo experiments demonstrated the effectiveness of SNOPY.
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