Joint multi-contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging.

ABSTRACT:

Purpose

To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly.

Methods

Data from our multi-contrast acquisition were embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts. Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans was tested for retrospective under-sampling at R = 6 (2D) and R = 4 × 4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the

SUBMITTER: Polak D 

PROVIDER: S-EPMC7539238 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature