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

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine
Daniel PolakKawin Setsompop

Abstract

To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly. 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 combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than 3 min. 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 sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded ...Continue Reading

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Citations

Feb 17, 2021·Magnetic Resonance Imaging·Chaoping ZhangDirk H J Poot
May 30, 2021·Magnetic Resonance Imaging·Xinwen LiuFeng Liu
Jul 14, 2021·Journal of Medical Imaging and Radiation Oncology·Shekhar S ChandraStuart Crozier
Aug 13, 2021·Computational and Mathematical Methods in Medicine·K Suresh ManicS Uma
Aug 15, 2021·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Daniel PolakStephen Cauley
Oct 17, 2021·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Jieying ZhangHua Guo

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