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Abstract #2732

Artificial Sparsity Enhanced Deep Learning BUDA Imaging Enables Rapid and Distortion-corrected High-Resolution 3D-EPI and QSM

Zhifeng Chen1,2, Jin Jin3, Richard Mcintyre2, Kieran O'Brien3, Daniel Stäb4,5, Meng Law6,7, and Zhaolin Chen1,2
1Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Australia, 2Monash Biomedical Imaging, Monash University, Clayton, Australia, 3Siemens Healthcare Pty Ltd, Brisbane, Australia, 4Siemens Healthcare Pty Ltd, Melbourne, Australia, Melbourne, Australia, 5Department of Radiology, The Royal Melbourne Hospital and The University of Melbourne, Parkville, Australia, 6Department of Neuroscience, Faculty of Medicine, Monash University, Melbourne, Australia, 7Department of Radiology, Alfred Health, Melbourne, Australia

Synopsis

Keywords: Image Reconstruction, Quantitative Susceptibility mapping, EPI, distortion correction

Motivation: MR susceptibility mapping serves as a highly valuable tool in various neuroscientific and clinical applications.

Goal(s): This innovative approach is designed to facilitate fast and robust high-resolution whole-brain imaging and quantitative susceptibility mapping (QSM).

Approach: In this study, our primary objective was to create a distortion-free 3D-EPI with blip-up/down acquisition (BUDA), incorporating controlled aliasing in parallel imaging (CAIPI) sampling, and applying artificial sparsity enhanced deep learning image reconstruction.

Results: Our developed technique holds the potential to produce distortion-free high-resolution whole-brain quantitative susceptibility mapping in just 12s at 3T and 9s at 7T, achieving an impressive resolution of 1 mm isotropic.

Impact: The proposed 3D-BUDA, incorporating a 2D CAIPIRINHA acquisition sequence with artificial sparsity-enhanced self-supervised deep learning reconstruction, demonstrated its ability to deliver rapid, distortion-free, high-resolution, whole-brain T2*-weighted imaging and QSM.

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Keywords