Dynamic MRI Reconstruction using Low-Rank plus Sparse model with Optimal Rank Regularized Eigen-Shrinkage

Low-rank plus sparse matrix decomposition algorithms have seen fruitful application in dynamic contrast-enhanced MRI because the data is well modeled as the superposition of a low-rank static background and temporally sparse dynamic contrast enhancement. In this setting, we propose a novel algorithm that replaces the standard nuclear norm with a recently discovered optimal rank regularizer from random matrix theory. This regularizer preserves the quality of high signal-to-noise ratio image features while maintaining data compressibility, resulting in better qualitative and quantitative image reconstruction than existing nuclear norm techniques. We substantiate these claims on undersampled multicoil cardiac perfusion MRI data.

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.

Click here for more information on becoming a member.