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Abstract #0736

Accelerating QSM using Compressed Sensing and Deep Neural Network

Yang Gao1, Feng Liu1, Stuart Crozier1, and Hongfu Sun1
1The University of Queensland, Brisbane, Australia

Quantitative susceptibility mapping (QSM) has shown significant clinical potential for studying neurological disorders, but its acquisitions are relatively slow, e.g. 5-10 mins. Compressed sensing (CS) undersampling and reconstruction techniques have been used to accelerate the magnitude-based MRI acquisitions; however, most of them are ineffective to phase signal due to its non-convex nature. In this study, we propose a deep neural network “CANet” using complex attention modules to recover both the magnitude and phase images from the CS-undersampled data, enabling substantial acceleration of phase-based QSM.

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