Keywords: CEST / APT / NOE, Molecular Imaging, AI, Deep Learning, Unsupervised Learning, Bloch-McConnell, Differentiable Physics
Motivation: MRF-based quantification of semi-solid MT/CEST proton-exchange requires a computationally demanding dictionary synthesis/matching. Recently reported unsupervised learning alternatives were incompatible with pulsed clinical CEST and multi-pool imaging.
Goal(s): To develop a training-set-free MRF reconstruction method, learning directly from the acquired data via pulsed-saturation-compatible physical modeling.
Approach: A differentiable multi-pool Bloch-McConnel simulator was designed and embedded within a test-time learning framework. Validation was performed using L-arginine phantoms and a human subject at 3T.
Results: The method enabled quantitative MT/CEST reconstruction in ~1 minute. The resulting maps were highly correlated with ground-truth in-vitro (Pearson’s r>0.95). In-vivo, semi-solid volume fractions were in agreement with MRF-based maps (r~0.8).
Impact: A one-stop-shop for semisolid MT and CEST MRF reconstruction was developed, enabling a training-set-free rapid quantification of exchange parameters on clinical scanners. This accessible approach could help a variety of Bloch-fitting applications to benefit from deep learning through differentiable spin-physics.
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