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Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging by Polak, Daniel; Cauley, Stephen; Bilgic, Berkin; Gong, Enhao; Bachert, Peter; Adalsteinsson, Elfar; Setsompop, Kawin is a scholarly article available to read on EtoBox.

What is Joint multi-contrast Variational Network reconstruction (jVN) with application to rapid 2D and 3D imaging about?

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 was 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=4x4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than three minutes. Results: Across all test datasets, our joint multi-contrast network better preserved fine anatomical details with reduced image-blurring when compared to the corresponding single-contrast reconstructions. Improvement in image quality was also obtained through complementary k-space sampli

Author

Polak, Daniel; Cauley, Stephen; Bilgic, Berkin; Gong, Enhao; Bachert, Peter; Adalsteinsson, Elfar; Setsompop, Kawin

Published

2019

Language

EN