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Abstract #0743

Self-gated self-supervised ADMM unrolling enables mesoscale high-resolution motion-robust diffusion-weighted imaging

Zhengguo Tan1, Patrick Liebig2, Annika Hofmann3, Michael Jaroszewicz1, Yun Jiang1, Vikas Gulani1, Frederik Laun4, and Florian Knoll3
1Michigan Institute for Imaging Technology and Translation (MIITT), Radiology, University of Michigan, Ann Arbor, MI, United States, 2Siemens Healthineers, Erlangen, Germany, 3Artificial Intelligence in Biomedical Engineering, University of Erlangen-Nuremberg, Erlangen, Germany, 4Radiology, Uniklinikum Erlangen, Erlangen, Germany

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: High-resolution and motion-robust diffusion-weighted imaging (DWI) is clinically demanding. A self-supervised image reconstruction model that leverages spatial-diffusion complementary sampling and convolution is beneficial to high-quality clinical DWI.

Goal(s): To develop an efficient self-supervised algorithm unrolling technique for high-resolution and motion-robust DWI.

Approach: We unroll the alternating direction method of multipliers (ADMM) to perform scan-specific self-supervised learning for deep DWI reconstruction.

Results: We demonstrate that (1) ADMM unrolling is generalizable across slices, (2) ADMM unrolling outperforms compressed sensing with locally-low rank (LLR) regularization in terms of image sharpness, tissue continuity and motion robustness, (3) ADMM unrolling enables clinically feasible inference time.

Impact: Our proposed ADMM unrolling enables whole brain DWI of 21 volumes at 0.7 mm isotropic resolution and 10 minutes scan, and shows higher signal-to-noise ratio (SNR), clearer tissue delineation, and improved motion robustness, which make it plausible for clinical translation.

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Keywords