Keywords: AI Diffusion Models, Image Reconstruction, Diffusion model, compressed sensing, sampling, Multi-scale, MAP estimate
Motivation: Deep reconstruction algorithms do not have theoretical guarantees enjoyed by compressive sensing. Moreover, memory demands of unrolled methods and contraction constraints in Plug-and-Play (PnP) methods restrict their application to large-scale problems.
Goal(s): To develop a fast PnP algorithm with guaranteed convergence to global minima and performance comparable to unrolled methods.
Approach: We introduce i-MuSE, an implicit multi-scale energy that smoothly approximates the negative log-prior. A local monotone constraint is added to the gradient to ensure local convexity of the energy.
Results: i-MuSE outperforms PnP methods and matches unrolled methods. It also offers improved generalizability. Convexity constraint improves convergence and performance at high accelerations.
Impact: i-MuSE offers a memory-efficient alternative to unrolled models while guaranteeing convergence, offering better generalization performance, and facilitating fast optimization algorithms. It can also perform posterior sampling, like diffusion models, to estimate uncertainty.
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