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Abstract #0189

Accurate and Efficient QSM Reconstruction using Deep Learning

Enhao Gong1, Berkin Bilgic2, Kawin Setsompop2, Audrey Fan3, Greg Zaharchuk3, and John Pauly1

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Harvard Medical School, Boston, MA, United States, 3Radiology, Stanford University, Stanford, CA, United States

Quantitative Susceptibility Mapping (QSM) is a powerful MRI technique to quantify susceptibility changes and reveal pathology such as multiple sclerosis (MS) lesions and demyelination. QSM reconstruction is very challenging because it requires solving an ill-posed deconvolution and removing the effects of a dipole kernel on tissue phases to obtain susceptibility. To address the limitations of existing QSM reconstruction methods in accuracy, stability and efficiency, an iteration-free data-driven QSM reconstruction is proposed that trains a deep learning model to approximate COSMOS QSM quantification from acquired signals and pre-processed phases. Cross-validated on in-vivo datasets with 15 single direction QSM scans and 3 COSMOS QSM results from 3 healthy subjects, the proposed deep learning method achieves accurate QSM reconstruction, outperforming state-of-the-art methods across various metrics. The deep learning solution is also faster than iterative reconstruction by several orders of magnitude, which enables broader clinical applications.

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