Fast non-local means reconstruction for multi-contrast compressed sensing

This abstract proposes a non-local means technique to reconstruct partially sampled images in MRI compressed sensing. Instead of imposing total variation constraint, we use a fully-sampled contrast as a prior estimate to reconstruct other undersampled contrasts. Partial volume information is extracted from the prior estimate by a feature-based non-local means approach and then applied as constraint to the undersampled images. Experiments show that the proposed method is comparable to M-FOCUSS with prior estimate in terms of normalized root-mean-square (NRMSE) error while being up to 30 faster. It also attains 50% NRMSE reduction and 20 speed-up relative to the sparseMRI algorithm.

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.