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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 combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than 3 min. 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 sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplary slices and quantitative error metrics. CONCLUSION:By leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi-contrast approach learnt more efficient regularizers, which helped to retain natural image appearance and avoid over-smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R = 16-fold acceleration with good image quality. This should help pave the way to very rapid high-resolution brain exams.

SUBMITTER: Polak D

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

REPOSITORIES: biostudies-literature

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

Polak Daniel D Cauley Stephen S Bilgic Berkin B Gong Enhao E Bachert Peter P Adalsteinsson Elfar E Setsompop Kawin K

Magnetic resonance in medicine 20200304 3

<h4>Purpose</h4>To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly.<h4>Methods</h4>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 imp ...[more]

PMID: 32129529

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Dataset Information

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

Publications

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

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